Acquiring 3-D information about thick objects from differential interference contrast images using texture extraction

NASA Astrophysics Data System (ADS)

Sierra, Heidy; Brooks, Dana; Dimarzio, Charles

2010-07-01

The extraction of 3-D morphological information about thick objects is explored in this work. We extract this information from 3-D differential interference contrast (DIC) images by applying a texture detection method. Texture extraction methods have been successfully used in different applications to study biological samples. A 3-D texture image is obtained by applying a local entropy-based texture extraction method. The use of this method to detect regions of blastocyst mouse embryos that are used in assisted reproduction techniques such as in vitro fertilization is presented as an example. Results demonstrate the potential of using texture detection methods to improve morphological analysis of thick samples, which is relevant to many biomedical and biological studies. Fluorescence and optical quadrature microscope phase images are used for validation.

Cloud cover analysis with Arctic Advanced Very High Resolution Radiometer data. II - Classification with spectral and textural measures

NASA Technical Reports Server (NTRS)

Key, J.

1990-01-01

The spectral and textural characteristics of polar clouds and surfaces for a 7-day summer series of AVHRR data in two Arctic locations are examined, and the results used in the development of a cloud classification procedure for polar satellite data. Since spatial coherence and texture sensitivity tests indicate that a joint spectral-textural analysis based on the same cell size is inappropriate, cloud detection with AVHRR data and surface identification with passive microwave data are first done on the pixel level as described by Key and Barry (1989). Next, cloud patterns within 250-sq-km regions are described, then the spectral and local textural characteristics of cloud patterns in the image are determined and each cloud pixel is classified by statistical methods. Results indicate that both spectral and textural features can be utilized in the classification of cloudy pixels, although spectral features are most useful for the discrimination between cloud classes.

SU-F-R-18: Updates to the Computational Environment for Radiological Research for Image Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Apte, Aditya P.; Deasy, Joseph O.

2016-06-15

Purpose: To present new tools in CERR for Texture Analysis and Visualization. Method: (1) Quantitative Image Analysis: We added the ability to compute Haralick texture features based on local neighbourhood. The Texture features depend on many parameters used in their derivation. For example: (a) directionality, (b) quantization of image, (c) patch-size for the neighborhood, (d) handling of the edge voxels within the region of interest, (e) Averaging co-occurance matrix vs texture features for different directions etc. A graphical user interface was built to set these parameters and then visualize their impact on the resulting texture maps. The entire functionality wasmore » written in Matlab. Array indexing was used to speed up the texture calculation. The computation speed is very competitive with the ITK library. Moreover, our implementation works with multiple CPUs and the computation time can be further reduced by using multiple processor threads. In order to reduce the Haralick texture maps into scalar features, we propose the use of Texture Volume Histograms. This lets users make use of the entire distribution of texture values within the region of interest rather than using just the mean and the standard deviations. (2) Qualitative/Visualization tools: The derived texture maps are stored as a new scan (derived) within CERR’s planC data structure. A display that compares various scans was built to show the raw image and the derived texture maps side-by-side. These images are positionally linked and can be navigated together. CERR’s graphics handling was updated and sped-up to be compatible with the newer Matlab versions. As a result, the users can use (a) different window levels and colormaps for different viewports, (b) click-and-drag or use mouse scroll-wheel to navigate slices. Results: The new features and updates are available via https://www.github.com/adityaapte/cerr . Conclusion: Features added to CERR increase its utility in Radiomics and Outcomes modeling.« less

Photomorphic analysis techniques: An interim spatial analysis using satellite remote sensor imagery and historical data

NASA Technical Reports Server (NTRS)

Keuper, H. R.; Peplies, R. W.; Gillooly, R. P.

1977-01-01

The use of machine scanning and/or computer-based techniques to provide greater objectivity in the photomorphic approach was investigated. Photomorphic analysis and its application in regional planning are discussed. Topics included: delineation of photomorphic regions; inadequacies of existing classification systems; tonal and textural characteristics and signature analysis techniques; pattern recognition and Fourier transform analysis; and optical experiments. A bibliography is included.

Combined texture feature analysis of segmentation and classification of benign and malignant tumour CT slices.

PubMed

Padma, A; Sukanesh, R

2013-01-01

A computer software system is designed for the segmentation and classification of benign from malignant tumour slices in brain computed tomography (CT) images. This paper presents a method to find and select both the dominant run length and co-occurrence texture features of region of interest (ROI) of the tumour region of each slice to be segmented by Fuzzy c means clustering (FCM) and evaluate the performance of support vector machine (SVM)-based classifiers in classifying benign and malignant tumour slices. Two hundred and six tumour confirmed CT slices are considered in this study. A total of 17 texture features are extracted by a feature extraction procedure, and six features are selected using Principal Component Analysis (PCA). This study constructed the SVM-based classifier with the selected features and by comparing the segmentation results with the experienced radiologist labelled ground truth (target). Quantitative analysis between ground truth and segmented tumour is presented in terms of segmentation accuracy, segmentation error and overlap similarity measures such as the Jaccard index. The classification performance of the SVM-based classifier with the same selected features is also evaluated using a 10-fold cross-validation method. The proposed system provides some newly found texture features have an important contribution in classifying benign and malignant tumour slices efficiently and accurately with less computational time. The experimental results showed that the proposed system is able to achieve the highest segmentation and classification accuracy effectiveness as measured by jaccard index and sensitivity and specificity.

Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age.

PubMed

Baños, Núria; Perez-Moreno, Alvaro; Migliorelli, Federico; Triginer, Laura; Cobo, Teresa; Bonet-Carne, Elisenda; Gratacos, Eduard; Palacio, Montse

2017-01-01

Quantitative texture analysis has been proposed to extract robust features from the ultrasound image to detect subtle changes in the textures of the images. The aim of this study was to evaluate the feasibility of quantitative cervical texture analysis to assess cervical tissue changes throughout pregnancy. This was a cross-sectional study including singleton pregnancies between 20.0 and 41.6 weeks of gestation from women who delivered at term. Cervical length was measured, and a selected region of interest in the cervix was delineated. A model to predict gestational age based on features extracted from cervical images was developed following three steps: data splitting, feature transformation, and regression model computation. Seven hundred images, 30 per gestational week, were included for analysis. There was a strong correlation between the gestational age at which the images were obtained and the estimated gestational age by quantitative analysis of the cervical texture (R = 0.88). This study provides evidence that quantitative analysis of cervical texture can extract features from cervical ultrasound images which correlate with gestational age. Further research is needed to evaluate its applicability as a biomarker of the risk of spontaneous preterm birth, as well as its role in cervical assessment in other clinical situations in which cervical evaluation might be relevant. © 2016 S. Karger AG, Basel.

A Comprehensive Texture Segmentation Framework for Segmentation of Capillary Non-Perfusion Regions in Fundus Fluorescein Angiograms

PubMed Central

Zheng, Yalin; Kwong, Man Ting; MacCormick, Ian J. C.; Beare, Nicholas A. V.; Harding, Simon P.

2014-01-01

Capillary non-perfusion (CNP) in the retina is a characteristic feature used in the management of a wide range of retinal diseases. There is no well-established computation tool for assessing the extent of CNP. We propose a novel texture segmentation framework to address this problem. This framework comprises three major steps: pre-processing, unsupervised total variation texture segmentation, and supervised segmentation. It employs a state-of-the-art multiphase total variation texture segmentation model which is enhanced by new kernel based region terms. The model can be applied to texture and intensity-based multiphase problems. A supervised segmentation step allows the framework to take expert knowledge into account, an AdaBoost classifier with weighted cost coefficient is chosen to tackle imbalanced data classification problems. To demonstrate its effectiveness, we applied this framework to 48 images from malarial retinopathy and 10 images from ischemic diabetic maculopathy. The performance of segmentation is satisfactory when compared to a reference standard of manual delineations: accuracy, sensitivity and specificity are 89.0%, 73.0%, and 90.8% respectively for the malarial retinopathy dataset and 80.8%, 70.6%, and 82.1% respectively for the diabetic maculopathy dataset. In terms of region-wise analysis, this method achieved an accuracy of 76.3% (45 out of 59 regions) for the malarial retinopathy dataset and 73.9% (17 out of 26 regions) for the diabetic maculopathy dataset. This comprehensive segmentation framework can quantify capillary non-perfusion in retinopathy from two distinct etiologies, and has the potential to be adopted for wider applications. PMID:24747681

Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET.

PubMed

Tixier, Florent; Hatt, Mathieu; Le Rest, Catherine Cheze; Le Pogam, Adrien; Corcos, Laurent; Visvikis, Dimitris

2012-05-01

(18)F-FDG PET measurement of standardized uptake value (SUV) is increasingly used for monitoring therapy response and predicting outcome. Alternative parameters computed through textural analysis were recently proposed to quantify the heterogeneity of tracer uptake by tumors as a significant predictor of response. The primary objective of this study was to evaluate the reproducibility of these heterogeneity measurements. Double baseline (18)F-FDG PET scans were acquired within 4 d of each other for 16 patients before any treatment was considered. A Bland-Altman analysis was performed on 8 parameters based on histogram measurements and 17 parameters based on textural heterogeneity features after discretization with values between 8 and 128. The reproducibility of maximum and mean SUV was similar to that in previously reported studies, with a mean percentage difference of 4.7% ± 19.5% and 5.5% ± 21.2%, respectively. By comparison, better reproducibility was measured for some textural features describing local heterogeneity of tracer uptake, such as entropy and homogeneity, with a mean percentage difference of -2% ± 5.4% and 1.8% ± 11.5%, respectively. Several regional heterogeneity parameters such as variability in the intensity and size of regions of homogeneous activity distribution had reproducibility similar to that of SUV measurements, with 95% confidence intervals of -22.5% to 3.1% and -1.1% to 23.5%, respectively. These parameters were largely insensitive to the discretization range. Several parameters derived from textural analysis describing heterogeneity of tracer uptake by tumors on local and regional scales had reproducibility similar to or better than that of simple SUV measurements. These reproducibility results suggest that these (18)F-FDG PET-derived parameters, which have already been shown to have predictive and prognostic value in certain cancer models, may be used to monitor therapy response and predict patient outcome.

Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET

PubMed Central

Tixier, Florent; Hatt, Mathieu; Le Rest, Catherine Cheze; Le Pogam, Adrien; Corcos, Laurent; Visvikis, Dimitris

2012-01-01

18F-FDG PET measurement of standardized uptake values (SUV) is increasingly used for monitoring therapy response or predicting outcome. Alternative parameters computed through textural analysis were recently proposed to quantify the tumor tracer uptake heterogeneity as significant predictors of response. The primary objective of this study was the evaluation of the reproducibility of these heterogeneity measurements. Methods Double-baseline 18F-FDG PET scans of 16 patients acquired within a period of 4 days prior to any treatment were considered. A Bland-Altman analysis was carried out on six parameters based on histogram measurements and 17 heterogeneity parameters based on textural features obtained after discretization with values between 8 and 128. Results SUVmax and SUVmean reproducibility were similar to previously reported studies with a mean percentage difference of 4.7±19.5% and 5.5±21.2% respectively. By comparison better reproducibility was measured for some of the textural features describing tumor tracer local heterogeneity, such as entropy and homogeneity with a mean percentage difference of −2±5.4% and 1.8±11.5% respectively. Several of the tumor regional heterogeneity parameters such as the variability in the intensity and size of homogeneous tumor activity distribution regions had similar reproducibility to the SUV measurements with 95% confidence intervals of −22.5% to 3.1% and −1.1% to 23.5% respectively. These parameters were largely insensitive to the discretization range values. Conclusion Several of the parameters derived from textural analysis describing tumor tracer heterogeneity at local and regional scales had similar or better reproducibility as simple SUV measurements. These reproducibility results suggest that these FDG PET image derived parameters which have already been shown to have a predictive and prognostic value in certain cancer models, may be used within the context of therapy response monitoring or predicting patient outcome. PMID:22454484

Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography.

PubMed

Zyout, Imad; Czajkowska, Joanna; Grzegorzek, Marcin

2015-12-01

The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multi-scale textural descriptors based on wavelet and gray level co-occurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples). The second set of suspicious regions was obtained from database of Mammographic Image Analysis Society (mini-MIAS) and contains 315 (207 normal and 108 abnormal) samples. Results from both datasets demonstrate the efficiency of using PSO based model selection for optimizing both classifier hyper-parameters and parameters, respectively. Furthermore, the obtained results indicate the promising performance of the proposed textural features and more specifically, those based on co-occurrence matrix of wavelet image representation technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multiresolution Local Binary Pattern texture analysis for false positive reduction in computerized detection of breast masses on mammograms

NASA Astrophysics Data System (ADS)

Choi, Jae Young; Kim, Dae Hoe; Choi, Seon Hyeong; Ro, Yong Man

2012-03-01

We investigated the feasibility of using multiresolution Local Binary Pattern (LBP) texture analysis to reduce falsepositive (FP) detection in a computerized mass detection framework. A new and novel approach for extracting LBP features is devised to differentiate masses and normal breast tissue on mammograms. In particular, to characterize the LBP texture patterns of the boundaries of masses, as well as to preserve the spatial structure pattern of the masses, two individual LBP texture patterns are then extracted from the core region and the ribbon region of pixels of the respective ROI regions, respectively. These two texture patterns are combined to produce the so-called multiresolution LBP feature of a given ROI. The proposed LBP texture analysis of the information in mass core region and its margin has clearly proven to be significant and is not sensitive to the precise location of the boundaries of masses. In this study, 89 mammograms were collected from the public MAIS database (DB). To perform a more realistic assessment of FP reduction process, the LBP texture analysis was applied directly to a total of 1,693 regions of interest (ROIs) automatically segmented by computer algorithm. Support Vector Machine (SVM) was applied for the classification of mass ROIs from ROIs containing normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classification accuracy and its improvement using multiresolution LBP features. With multiresolution LBP features, the classifier achieved an average area under the ROC curve, , z A of 0.956 during testing. In addition, the proposed LBP features outperform other state-of-the-arts features designed for false positive reduction.

Bone texture analysis on dental radiographic images: results with several angulated radiographs on the same region of interest

NASA Astrophysics Data System (ADS)

Amouriq, Yves; Guedon, Jeanpierre; Normand, Nicolas; Arlicot, Aurore; Benhdech, Yassine; Weiss, Pierre

2011-03-01

Bone microarchitecture is the predictor of bone quality or bone disease. It can only be measured on a bone biopsy, which is invasive and not available for all clinical situations. Texture analysis on radiographs is a common way to investigate bone microarchitecture. But relationship between three-dimension histomorphometric parameters and two-dimension texture parameters is not always well known, with poor results. The aim of this study is to performed angulated radiographs of the same region of interest and see if a better relationship between texture analysis on several radiographs and histomorphometric parameters can be developed. Computed radiography images of dog (Beagle) mandible section in molar regions were compared with high-resolution micro-CT (Computed-Tomograph) volumes. Four radiographs with 27° angle (up, down, left, right, using Rinn ring and customized arm positioning system) were performed from initial radiograph position. Bone texture parameters were calculated on all images. Texture parameters were also computed from new images obtained by difference between angulated images. Results of fractal values in different trabecular areas give some caracterisation of bone microarchitecture.

Association between pathology and texture features of multi parametric MRI of the prostate

NASA Astrophysics Data System (ADS)

Kuess, Peter; Andrzejewski, Piotr; Nilsson, David; Georg, Petra; Knoth, Johannes; Susani, Martin; Trygg, Johan; Helbich, Thomas H.; Polanec, Stephan H.; Georg, Dietmar; Nyholm, Tufve

2017-10-01

The role of multi-parametric (mp)MRI in the diagnosis and treatment of prostate cancer has increased considerably. An alternative to visual inspection of mpMRI is the evaluation using histogram-based (first order statistics) parameters and textural features (second order statistics). The aims of the present work were to investigate the relationship between benign and malignant sub-volumes of the prostate and textures obtained from mpMR images. The performance of tumor prediction was investigated based on the combination of histogram-based and textural parameters. Subsequently, the relative importance of mpMR images was assessed and the benefit of additional imaging analyzed. Finally, sub-structures based on the PI-RADS classification were investigated as potential regions to automatically detect maligned lesions. Twenty-five patients who received mpMRI prior to radical prostatectomy were included in the study. The imaging protocol included T2, DWI, and DCE. Delineation of tumor regions was performed based on pathological information. First and second order statistics were derived from each structure and for all image modalities. The resulting data were processed with multivariate analysis, using PCA (principal component analysis) and OPLS-DA (orthogonal partial least squares discriminant analysis) for separation of malignant and healthy tissue. PCA showed a clear difference between tumor and healthy regions in the peripheral zone for all investigated images. The predictive ability of the OPLS-DA models increased for all image modalities when first and second order statistics were combined. The predictive value reached a plateau after adding ADC and T2, and did not increase further with the addition of other image information. The present study indicates a distinct difference in the signatures between malign and benign prostate tissue. This is an absolute prerequisite for automatic tumor segmentation, but only the first step in that direction. For the specific identified signature, DCE did not add complementary information to T2 and ADC maps.

In Vivo Imaging of Tau Pathology Using Magnetic Resonance Imaging Textural Analysis

PubMed Central

Colgan, Niall; Ganeshan, Balaji; Harrison, Ian F.; Ismail, Ozama; Holmes, Holly E.; Wells, Jack A.; Powell, Nick M.; O'Callaghan, James M.; O'Neill, Michael J.; Murray, Tracey K.; Ahmed, Zeshan; Collins, Emily C.; Johnson, Ross A.; Groves, Ashley; Lythgoe, Mark F.

2017-01-01

Background: Non-invasive characterization of the pathological features of Alzheimer's disease (AD) could enhance patient management and the development of therapeutic strategies. Magnetic resonance imaging texture analysis (MRTA) has been used previously to extract texture descriptors from structural clinical scans in AD to determine cerebral tissue heterogeneity. In this study, we examined the potential of MRTA to specifically identify tau pathology in an AD mouse model and compared the MRTA metrics to histological measures of tau burden. Methods: MRTA was applied to T2 weighted high-resolution MR images of nine 8.5-month-old rTg4510 tau pathology (TG) mice and 16 litter matched wild-type (WT) mice. MRTA comprised of the filtration-histogram technique, where the filtration step extracted and enhanced features of different sizes (fine, medium, and coarse texture scales), followed by quantification of texture using histogram analysis (mean gray level intensity, mean intensity, entropy, uniformity, skewness, standard-deviation, and kurtosis). MRTA was applied to manually segmented regions of interest (ROI) drawn within the cortex, hippocampus, and thalamus regions and the level of tau burden was assessed in equivalent regions using histology. Results: Texture parameters were markedly different between WT and TG in the cortex (E, p < 0.01, K, p < 0.01), the hippocampus (K, p < 0.05) and in the thalamus (K, p < 0.01). In addition, we observed significant correlations between histological measurements of tau burden and kurtosis in the cortex, hippocampus and thalamus. Conclusions: MRTA successfully differentiated WT and TG in brain regions with varying degrees of tau pathology (cortex, hippocampus, and thalamus) based on T2 weighted MR images. Furthermore, the kurtosis measurement correlated with histological measures of tau burden. This initial study indicates that MRTA may have a role in the early diagnosis of AD and the assessment of tau pathology using routinely acquired structural MR images. PMID:29163005

Man-made objects cuing in satellite imagery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Skurikhin, Alexei N

2009-01-01

We present a multi-scale framework for man-made structures cuing in satellite image regions. The approach is based on a hierarchical image segmentation followed by structural analysis. A hierarchical segmentation produces an image pyramid that contains a stack of irregular image partitions, represented as polygonized pixel patches, of successively reduced levels of detail (LOOs). We are jumping off from the over-segmented image represented by polygons attributed with spectral and texture information. The image is represented as a proximity graph with vertices corresponding to the polygons and edges reflecting polygon relations. This is followed by the iterative graph contraction based on Boruvka'smore » Minimum Spanning Tree (MST) construction algorithm. The graph contractions merge the patches based on their pairwise spectral and texture differences. Concurrently with the construction of the irregular image pyramid, structural analysis is done on the agglomerated patches. Man-made object cuing is based on the analysis of shape properties of the constructed patches and their spatial relations. The presented framework can be used as pre-scanning tool for wide area monitoring to quickly guide the further analysis to regions of interest.« less

Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.

PubMed

Feng, Zhichao; Rong, Pengfei; Cao, Peng; Zhou, Qingyu; Zhu, Wenwei; Yan, Zhimin; Liu, Qianyun; Wang, Wei

2018-04-01

To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). This single-institutional retrospective study included 58 patients with pathologically proven small renal mass (17 in AMLwvf and 41 in RCC groups). Texture features were extracted from the largest possible tumorous regions of interest (ROIs) by manual segmentation in preoperative three-phase CT images. Interobserver reliability and the Mann-Whitney U test were applied to select features preliminarily. Then support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were adopted to establish discriminative classifiers, and the performance of classifiers was assessed. Of the 42 extracted features, 16 candidate features showed significant intergroup differences (P < 0.05) and had good interobserver agreement. An optimal feature subset including 11 features was further selected by the SVM-RFE method. The SVM-RFE+SMOTE classifier achieved the best performance in discriminating between small AMLwvf and RCC, with the highest accuracy, sensitivity, specificity and AUC of 93.9 %, 87.8 %, 100 % and 0.955, respectively. Machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC. • Although conventional CT is useful for diagnosis of SRMs, it has limitations. • Machine-learning based CT texture analysis facilitate differentiation of small AMLwvf from RCC. • The highest accuracy of SVM-RFE+SMOTE classifier reached 93.9 %. • Texture analysis combined with machine-learning methods might spare unnecessary surgery for AMLwvf.

Diagnostic Performance of Mammographic Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors.

PubMed

Li, Zhiming; Yu, Lan; Wang, Xin; Yu, Haiyang; Gao, Yuanxiang; Ren, Yande; Wang, Gang; Zhou, Xiaoming

2017-11-09

The purpose of this study was to investigate the diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. Digital mammography images were obtained from the Picture Archiving and Communication System at our institute. Texture features of mammographic images were calculated. Mann-Whitney U test was used to identify differences between the benign and malignant group. The receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of texture features. Significant differences of texture features of histogram, gray-level co-occurrence matrix (GLCM) and run length matrix (RLM) were found between the benign and malignant breast group (P < .05). The area under the ROC (AUROC) of histogram, GLCM, and RLM were 0.800, 0.787, and 0.761, with no differences between them (P > .05). The AUROCs of imaging-based diagnosis, texture analysis, and imaging-based diagnosis combined with texture analysis were 0.873, 0.863, and 0.961, respectively. When imaging-based diagnosis was combined with texture analysis, the AUROC was higher than that of imaging-based diagnosis or texture analysis (P < .05). Mammographic texture analysis is a reliable technique for differential diagnosis of benign and malignant breast tumors. Furthermore, the combination of imaging-based diagnosis and texture analysis can significantly improve diagnostic performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Segmentation and classification of brain images using firefly and hybrid kernel-based support vector machine

NASA Astrophysics Data System (ADS)

Selva Bhuvaneswari, K.; Geetha, P.

2017-05-01

Magnetic resonance imaging segmentation refers to a process of assigning labels to set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. In recent years, various brain tumour detection techniques are presented in the literature. The entire segmentation process of our proposed work comprises three phases: threshold generation with dynamic modified region growing phase, texture feature generation phase and region merging phase. by dynamically changing two thresholds in the modified region growing approach, the first phase of the given input image can be performed as dynamic modified region growing process, in which the optimisation algorithm, firefly algorithm help to optimise the two thresholds in modified region growing. After obtaining the region growth segmented image using modified region growing, the edges can be detected with edge detection algorithm. In the second phase, the texture feature can be extracted using entropy-based operation from the input image. In region merging phase, the results obtained from the texture feature-generation phase are combined with the results of dynamic modified region growing phase and similar regions are merged using a distance comparison between regions. After identifying the abnormal tissues, the classification can be done by hybrid kernel-based SVM (Support Vector Machine). The performance analysis of the proposed method will be carried by K-cross fold validation method. The proposed method will be implemented in MATLAB with various images.

Texture classification of normal tissues in computed tomography using Gabor filters

NASA Astrophysics Data System (ADS)

Dettori, Lucia; Bashir, Alia; Hasemann, Julie

2007-03-01

The research presented in this article is aimed at developing an automated imaging system for classification of normal tissues in medical images obtained from Computed Tomography (CT) scans. Texture features based on a bank of Gabor filters are used to classify the following tissues of interests: liver, spleen, kidney, aorta, trabecular bone, lung, muscle, IP fat, and SQ fat. The approach consists of three steps: convolution of the regions of interest with a bank of 32 Gabor filters (4 frequencies and 8 orientations), extraction of two Gabor texture features per filter (mean and standard deviation), and creation of a Classification and Regression Tree-based classifier that automatically identifies the various tissues. The data set used consists of approximately 1000 DIACOM images from normal chest and abdominal CT scans of five patients. The regions of interest were labeled by expert radiologists. Optimal trees were generated using two techniques: 10-fold cross-validation and splitting of the data set into a training and a testing set. In both cases, perfect classification rules were obtained provided enough images were available for training (~65%). All performance measures (sensitivity, specificity, precision, and accuracy) for all regions of interest were at 100%. This significantly improves previous results that used Wavelet, Ridgelet, and Curvelet texture features, yielding accuracy values in the 85%-98% range The Gabor filters' ability to isolate features at different frequencies and orientations allows for a multi-resolution analysis of texture essential when dealing with, at times, very subtle differences in the texture of tissues in CT scans.

SU-E-J-249: Characterization of Gynecological Tumor Heterogeneity Using Texture Analysis in the Context of An 18F-FDG PET Adaptive Protocol

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nawrocki, J; Chino, J; Craciunescu, O

Purpose: We propose a method to examine gynecological tumor heterogeneity using texture analysis in the context of an adaptive PET protocol in order to establish if texture metrics from baseline PET-CT predict tumor response better than SUV metrics alone as well as determine texture features correlating with tumor response during radiation therapy. Methods: This IRB approved protocol included 29 women with node positive gynecological cancers visible on FDG-PET treated with EBRT to the PET positive nodes. A baseline and intra-treatment PET-CT was obtained. Tumor outcome was determined based on RECIST on posttreatment PET-CT. Primary GTVs were segmented using 40% thresholdmore » and a semi-automatic gradient-based contouring tool, PET Edge (MIM Software Inc., Cleveland, OH). SUV histogram features, Metabolic Volume (MV), and Total Lesion Glycolysis (TLG) were calculated. Four 3D texture matrices describing local and regional relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 texture features were calculated. Prognostic power of baseline features derived from gradientbased and threshold GTVs were determined using the Wilcoxon rank-sum test. Receiver Operating Characteristics and logistic regression was performed using JMP (SAS Institute Inc., Cary, NC) to find probabilities of predicting response. Changes in features during treatment were determined using the Wilcoxon signed-rank test. Results: Of the 29 patients, there were 16 complete responders, 7 partial responders, and 6 non-responders. Comparing CR/PR vs. NR for gradient-based GTVs, 7 texture values, TLG, and SUV kurtosis had a p < 0.05. Threshold GTVs yielded 4 texture features and TLG with p < 0.05. From baseline to intra-treatment, 14 texture features, SUVmean, SUVmax, MV, and TLG changed with p < 0.05. Conclusion: Texture analysis of PET imaged gynecological tumors is an effective method for early prognosis and should be used complimentary to SUV metrics, especially when using gradient based segmentation.« less

Texton-based analysis of paintings

NASA Astrophysics Data System (ADS)

van der Maaten, Laurens J. P.; Postma, Eric O.

2010-08-01

The visual examination of paintings is traditionally performed by skilled art historians using their eyes. Recent advances in intelligent systems may support art historians in determining the authenticity or date of creation of paintings. In this paper, we propose a technique for the examination of brushstroke structure that views the wildly overlapping brushstrokes as texture. The analysis of the painting texture is performed with the help of a texton codebook, i.e., a codebook of small prototypical textural patches. The texton codebook can be learned from a collection of paintings. Our textural analysis technique represents paintings in terms of histograms that measure the frequency by which the textons in the codebook occur in the painting (so-called texton histograms). We present experiments that show the validity and effectiveness of our technique for textural analysis on a collection of digitized high-resolution reproductions of paintings by Van Gogh and his contemporaries. As texton histograms cannot be easily be interpreted by art experts, the paper proposes to approaches to visualize the results on the textural analysis. The first approach visualizes the similarities between the histogram representations of paintings by employing a recently proposed dimensionality reduction technique, called t-SNE. We show that t-SNE reveals a clear separation of paintings created by Van Gogh and those created by other painters. In addition, the period of creation is faithfully reflected in the t-SNE visualizations. The second approach visualizes the similarities and differences between paintings by highlighting regions in a painting in which the textural structure of the painting is unusual. We illustrate the validity of this approach by means of an experiment in which we highlight regions in a painting by Monet that are not very "Van Gogh-like". Taken together, we believe the tools developed in this study are well capable of assisting for art historians in support of their study of paintings.

Wavelet-based image analysis system for soil texture analysis

NASA Astrophysics Data System (ADS)

Sun, Yun; Long, Zhiling; Jang, Ping-Rey; Plodinec, M. John

2003-05-01

Soil texture is defined as the relative proportion of clay, silt and sand found in a given soil sample. It is an important physical property of soil that affects such phenomena as plant growth and agricultural fertility. Traditional methods used to determine soil texture are either time consuming (hydrometer), or subjective and experience-demanding (field tactile evaluation). Considering that textural patterns observed at soil surfaces are uniquely associated with soil textures, we propose an innovative approach to soil texture analysis, in which wavelet frames-based features representing texture contents of soil images are extracted and categorized by applying a maximum likelihood criterion. The soil texture analysis system has been tested successfully with an accuracy of 91% in classifying soil samples into one of three general categories of soil textures. In comparison with the common methods, this wavelet-based image analysis approach is convenient, efficient, fast, and objective.

Change Detection in High-Resolution Remote Sensing Images Using Levene-Test and Fuzzy Evaluation

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Liu, H. J.

2018-04-01

High-resolution remote sensing images possess complex spatial structure and rich texture information, according to these, this paper presents a new method of change detection based on Levene-Test and Fuzzy Evaluation. It first got map-spots by segmenting two overlapping images which had been pretreated, extracted features such as spectrum and texture. Then, changed information of all map-spots which had been treated by the Levene-Test were counted to obtain the candidate changed regions, hue information (H component) was extracted through the IHS Transform and conducted change vector analysis combined with the texture information. Eventually, the threshold was confirmed by an iteration method, the subject degrees of candidate changed regions were calculated, and final change regions were determined. In this paper experimental results on multi-temporal ZY-3 high-resolution images of some area in Jiangsu Province show that: Through extracting map-spots of larger difference as the candidate changed regions, Levene-Test decreases the computing load, improves the precision of change detection, and shows better fault-tolerant capacity for those unchanged regions which are of relatively large differences. The combination of Hue-texture features and fuzzy evaluation method can effectively decrease omissions and deficiencies, improve the precision of change detection.

Time-series analysis of lung texture on bone-suppressed dynamic chest radiograph for the evaluation of pulmonary function: a preliminary study

NASA Astrophysics Data System (ADS)

Tanaka, Rie; Matsuda, Hiroaki; Sanada, Shigeru

2017-03-01

The density of lung tissue changes as demonstrated on imagery is dependent on the relative increases and decreases in the volume of air and lung vessels per unit volume of lung. Therefore, a time-series analysis of lung texture can be used to evaluate relative pulmonary function. This study was performed to assess a time-series analysis of lung texture on dynamic chest radiographs during respiration, and to demonstrate its usefulness in the diagnosis of pulmonary impairments. Sequential chest radiographs of 30 patients were obtained using a dynamic flat-panel detector (FPD; 100 kV, 0.2 mAs/pulse, 15 frames/s, SID = 2.0 m; Prototype, Konica Minolta). Imaging was performed during respiration, and 210 images were obtained over 14 seconds. Commercial bone suppression image-processing software (Clear Read Bone Suppression; Riverain Technologies, Miamisburg, Ohio, USA) was applied to the sequential chest radiographs to create corresponding bone suppression images. Average pixel values, standard deviation (SD), kurtosis, and skewness were calculated based on a density histogram analysis in lung regions. Regions of interest (ROIs) were manually located in the lungs, and the same ROIs were traced by the template matching technique during respiration. Average pixel value effectively differentiated regions with ventilatory defects and normal lung tissue. The average pixel values in normal areas changed dynamically in synchronization with the respiratory phase, whereas those in regions of ventilatory defects indicated reduced variations in pixel value. There were no significant differences between ventilatory defects and normal lung tissue in the other parameters. We confirmed that time-series analysis of lung texture was useful for the evaluation of pulmonary function in dynamic chest radiography during respiration. Pulmonary impairments were detected as reduced changes in pixel value. This technique is a simple, cost-effective diagnostic tool for the evaluation of regional pulmonary function.

Formation mechanisms of periodic longitudinal microstructure and texture patterns in friction stir welded magnesium AZ80

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hiscocks, J., E-mail: j.hiscocks@queensu.ca

Many studies of friction stir welding have shown that periodicity of metal flow around the tool pin may result in the formation of periodic differences in microstructure and texture in the weld nugget area correlated with the weld pitch. The current work investigates the periodicity of magnesium weld microtexture in the nugget region and its association with material flow using optical and electron microscopy. Two welds created in AZ80 at different processing conditions are presented in detail, one illustrating periodic longitudinal texture change, and one showing for the first time that periodic variations in texture, grain size, or composition aremore » not defining features of periodic nugget flow. While nugget texture is dominated by shear deformation, it was found here to be affected to a lesser degree by compaction of material behind the welding tool, which led to reduction in intensity of the shear texture fiber. The decreased tendency for magnesium based alloys to form periodic patterns as compared to aluminum based alloys is explained with reference to the shear textures. - Highlights: •It is shown here that periodic material flow in the nugget does not necessitate longitudinal texture patterns. •Longitudinal texture patterns are shown to be present or absent in Mg AZ80 based on processing conditions. •Texture in the nugget is mainly dictated by shear deformation, but has measurable effects from other deformation modes. •Explanation of why longitudinal texture change is frequently reported in aluminum but not magnesium alloys is provided. •A new vector visualization of material flow based on EBSD data analysis is shown.« less

Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer.

PubMed

Cheng, Nai-Ming; Fang, Yu-Hua Dean; Lee, Li-yu; Chang, Joseph Tung-Chieh; Tsan, Din-Li; Ng, Shu-Hang; Wang, Hung-Ming; Liao, Chun-Ta; Yang, Lan-Yan; Hsu, Ching-Han; Yen, Tzu-Chen

2015-03-01

The question as to whether the regional textural features extracted from PET images predict prognosis in oropharyngeal squamous cell carcinoma (OPSCC) remains open. In this study, we investigated the prognostic impact of regional heterogeneity in patients with T3/T4 OPSCC. We retrospectively reviewed the records of 88 patients with T3 or T4 OPSCC who had completed primary therapy. Progression-free survival (PFS) and disease-specific survival (DSS) were the main outcome measures. In an exploratory analysis, a standardized uptake value of 2.5 (SUV 2.5) was taken as the cut-off value for the detection of tumour boundaries. A fixed threshold at 42 % of the maximum SUV (SUVmax 42 %) and an adaptive threshold method were then used for validation. Regional textural features were extracted from pretreatment (18)F-FDG PET/CT images using the grey-level run length encoding method and grey-level size zone matrix. The prognostic significance of PET textural features was examined using receiver operating characteristic (ROC) curves and Cox regression analysis. Zone-size nonuniformity (ZSNU) was identified as an independent predictor of PFS and DSS. Its prognostic impact was confirmed using both the SUVmax 42 % and the adaptive threshold segmentation methods. Based on (1) total lesion glycolysis, (2) uniformity (a local scale texture parameter), and (3) ZSNU, we devised a prognostic stratification system that allowed the identification of four distinct risk groups. The model combining the three prognostic parameters showed a higher predictive value than each variable alone. ZSNU is an independent predictor of outcome in patients with advanced T-stage OPSCC, and may improve their prognostic stratification.

Quantitative Ultrasound Using Texture Analysis of Myofascial Pain Syndrome in the Trapezius.

PubMed

Kumbhare, Dinesh A; Ahmed, Sara; Behr, Michael G; Noseworthy, Michael D

2018-01-01

Objective-The objective of this study is to assess the discriminative ability of textural analyses to assist in the differentiation of the myofascial trigger point (MTrP) region from normal regions of skeletal muscle. Also, to measure the ability to reliably differentiate between three clinically relevant groups: healthy asymptomatic, latent MTrPs, and active MTrP. Methods-18 and 19 patients were identified with having active and latent MTrPs in the trapezius muscle, respectively. We included 24 healthy volunteers. Images were obtained by research personnel, who were blinded with respect to the clinical status of the study participant. Histograms provided first-order parameters associated with image grayscale. Haralick, Galloway, and histogram-related features were used in texture analysis. Blob analysis was conducted on the regions of interest (ROIs). Principal component analysis (PCA) was performed followed by multivariate analysis of variance (MANOVA) to determine the statistical significance of the features. Results-92 texture features were analyzed for factorability using Bartlett's test of sphericity, which was significant. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.94. PCA demonstrated rotated eigenvalues of the first eight components (each comprised of multiple texture features) explained 94.92% of the cumulative variance in the ultrasound image characteristics. The 24 features identified by PCA were included in the MANOVA as dependent variables, and the presence of a latent or active MTrP or healthy muscle were independent variables. Conclusion-Texture analysis techniques can discriminate between the three clinically relevant groups.

Texture analysis improves level set segmentation of the anterior abdominal wall

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Zhoubing; Allen, Wade M.; Baucom, Rebeccah B.

2013-12-15

Purpose: The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is fraught with failure; recurrence rates ranging from 24% to 43% have been reported, even with the use of biocompatible mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical judgments, notably, quantitative metrics based on image-processing are not used. The authors propose that image segmentation methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation on which to measure geometric properties of hernias and surrounding tissues and, therefore,more » to optimize intervention.Methods: In this study with 20 clinically acquired CT scans on postoperative patients, the authors demonstrated a novel approach to geometric classification of the abdominal. The authors’ approach uses a texture analysis based on Gabor filters to extract feature vectors and follows a fuzzy c-means clustering method to estimate voxelwise probability memberships for eight clusters. The memberships estimated from the texture analysis are helpful to identify anatomical structures with inhomogeneous intensities. The membership was used to guide the level set evolution, as well as to derive an initial start close to the abdominal wall.Results: Segmentation results on abdominal walls were both quantitatively and qualitatively validated with surface errors based on manually labeled ground truth. Using texture, mean surface errors for the outer surface of the abdominal wall were less than 2 mm, with 91% of the outer surface less than 5 mm away from the manual tracings; errors were significantly greater (2–5 mm) for methods that did not use the texture.Conclusions: The authors’ approach establishes a baseline for characterizing the abdominal wall for improving VH care. Inherent texture patterns in CT scans are helpful to the tissue classification, and texture analysis can improve the level set segmentation around the abdominal region.« less

Texture-based characterization of subskin features by specified laser speckle effects at λ = 650 nm region for more accurate parametric 'skin age' modelling.

PubMed

Orun, A B; Seker, H; Uslan, V; Goodyer, E; Smith, G

2017-06-01

The textural structure of 'skin age'-related subskin components enables us to identify and analyse their unique characteristics, thus making substantial progress towards establishing an accurate skin age model. This is achieved by a two-stage process. First by the application of textural analysis using laser speckle imaging, which is sensitive to textural effects within the λ = 650 nm spectral band region. In the second stage, a Bayesian inference method is used to select attributes from which a predictive model is built. This technique enables us to contrast different skin age models, such as the laser speckle effect against the more widely used normal light (LED) imaging method, whereby it is shown that our laser speckle-based technique yields better results. The method introduced here is non-invasive, low cost and capable of operating in real time; having the potential to compete against high-cost instrumentation such as confocal microscopy or similar imaging devices used for skin age identification purposes. © 2016 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery.

PubMed

Li, Guiying; Lu, Dengsheng; Moran, Emilio; Hetrick, Scott

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery

PubMed Central

LI, GUIYING; LU, DENGSHENG; MORAN, EMILIO; HETRICK, SCOTT

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms – maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes. PMID:22368311

Detection of sub-kilometer craters in high resolution planetary images using shape and texture features

NASA Astrophysics Data System (ADS)

Bandeira, Lourenço; Ding, Wei; Stepinski, Tomasz F.

2012-01-01

Counting craters is a paramount tool of planetary analysis because it provides relative dating of planetary surfaces. Dating surfaces with high spatial resolution requires counting a very large number of small, sub-kilometer size craters. Exhaustive manual surveys of such craters over extensive regions are impractical, sparking interest in designing crater detection algorithms (CDAs). As a part of our effort to design a CDA, which is robust and practical for planetary research analysis, we propose a crater detection approach that utilizes both shape and texture features to identify efficiently sub-kilometer craters in high resolution panchromatic images. First, a mathematical morphology-based shape analysis is used to identify regions in an image that may contain craters; only those regions - crater candidates - are the subject of further processing. Second, image texture features in combination with the boosting ensemble supervised learning algorithm are used to accurately classify previously identified candidates into craters and non-craters. The design of the proposed CDA is described and its performance is evaluated using a high resolution image of Mars for which sub-kilometer craters have been manually identified. The overall detection rate of the proposed CDA is 81%, the branching factor is 0.14, and the overall quality factor is 72%. This performance is a significant improvement over the previous CDA based exclusively on the shape features. The combination of performance level and computational efficiency offered by this CDA makes it attractive for practical application.

Research for Key Techniques of Geophysical Recognition System of Hydrocarbon-induced Magnetic Anomalies Based on Hydrocarbon Seepage Theory

NASA Astrophysics Data System (ADS)

Zhang, L.; Hao, T.; Zhao, B.

2009-12-01

Hydrocarbon seepage effects can cause magnetic alteration zones in near surface, and the magnetic anomalies induced by the alteration zones can thus be used to locate oil-gas potential regions. In order to reduce the inaccuracy and multi-resolution of the hydrocarbon anomalies recognized only by magnetic data, and to meet the requirement of integrated management and sythetic analysis of multi-source geoscientfic data, it is necessary to construct a recognition system that integrates the functions of data management, real-time processing, synthetic evaluation, and geologic mapping. In this paper research for the key techniques of the system is discussed. Image processing methods can be applied to potential field images so as to make it easier for visual interpretation and geological understanding. For gravity or magnetic images, the anomalies with identical frequency-domain characteristics but different spatial distribution will reflect differently in texture and relevant textural statistics. Texture is a description of structural arrangements and spatial variation of a dataset or an image, and has been applied in many research fields. Textural analysis is a procedure that extracts textural features by image processing methods and thus obtains a quantitative or qualitative description of texture. When the two kinds of anomalies have no distinct difference in amplitude or overlap in frequency spectrum, they may be distinguishable due to their texture, which can be considered as textural contrast. Therefore, for the recognition system we propose a new “magnetic spots” recognition method based on image processing techniques. The method can be divided into 3 major steps: firstly, separate local anomalies caused by shallow, relatively small sources from the total magnetic field, and then pre-process the local magnetic anomaly data by image processing methods such that magnetic anomalies can be expressed as points, lines and polygons with spatial correlation, which includes histogram-equalization based image display, object recognition and extraction; then, mine the spatial characteristics and correlations of the magnetic anomalies using textural statistics and analysis, and study the features of known anomalous objects (closures, hydrocarbon-bearing structures, igneous rocks, etc.) in the same research area; finally, classify the anomalies, cluster them according to their similarity, and predict hydrocarbon induced “magnetic spots” combined with geologic, drilling and rock core data. The system uses the ArcGIS as the secondary development platform, inherits the basic functions of the ArcGIS, and develops two main sepecial functional modules, the module for conventional potential-field data processing methods and the module for feature extraction and enhancement based on image processing and analysis techniques. The system can be applied to realize the geophysical detection and recognition of near-surface hydrocarbon seepage anomalies, provide technical support for locating oil-gas potential regions, and promote geophysical data processing and interpretation to advance more efficiently.

Ecological risk assessment: influence of texture on background concentration of microelements in soils of Russia.

NASA Astrophysics Data System (ADS)

Beketskaya, Olga

2010-05-01

In Russia quality standards of contaminated substances values in environment consist of ecological and sanitary rate-setting. The sanitary risk assessment base on potential risk that contaminants pose to protect human beings. The main purpose of the ecological risk assessment is to protect ecosystem. To determine negative influence on living organisms in the sanitary risk assessment in Russia we use MPC. This value of contaminants show how substances affected on different part of environment, biological activity and soil processes. The ecological risk assessment based on comparison compounds concentration with background concentration for definite territories. Taking into account high interval of microelements value in soils, we suggest using statistic method for determination of concentration levels of chemical elements concentration in soils of Russia. This method is based on determination middle levels of elements content in natural condition. The top limit of middle chemical elements concentration in soils is value, which exceed middle regional background level in three times standard deviation. The top limit of natural concentration excess we can explain as anthropogenic impact. At first we study changing in the middle content value of microelements in soils of geographic regions in European part of Russia on the basis of cartographical analysis. Cartographical analysis showed that the soil of mountainous and mountain surrounding regions is enriched with microelements. On the plain territory of European part of Russia for most of microelements was noticed general direction of increasing their concentration in soils from north to south, also in the same direction soil clay content rise for majority of soils. For all other territories a clear connection has been noticed between the distribution of sand sediment. By our own investigation and data from scientific literature data base was created. This data base consist of following soil properties: texture, organic matter content, concentration of microelements and pH value. On the basis of this data base massive of data for Forest-steppe and Steppe regions was create, which was divided by texture. For all data statistics method was done and was calculated maximum level natural microelements content for soils with different texture (?+3*δ). As a result of our statistic calculation we got middle and the top limit of background concentration of microelements in sandy and clay soils (conditional border - sandy loam) of two regions. We showed, that for all territory of European part of Russia and for Forest-steppe and Steppe regions separately middle content and maximum level natural microelements concentrations (?+3*σ) are higher in clay soils, rather then in sandy soils. Data characterizing soils, in different regions, of similar texture differs less than the data collected for sandy and clay soils of the same region. After all this calculation we can notice, that data of middle and top limit of background microelements concentration in soils, based on statistic method, can be used in the aim of ecological risk assessment. Using offered method allow to calculate top limit of background concentration for sandy and clay soils for large-scale geographic regions, exceeding which will be evidence of anthropogenic contamination of soil.

Process metallurgy simulation for metal drawing process optimization by using two-scale finite element method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nakamachi, Eiji; Yoshida, Takashi; Yamaguchi, Toshihiko

2014-10-06

We developed two-scale FE analysis procedure based on the crystallographic homogenization method by considering the hierarchical structure of poly-crystal aluminium alloy metal. It can be characterized as the combination of two-scale structure, such as the microscopic polycrystal structure and the macroscopic elastic plastic continuum. Micro polycrystal structure can be modeled as a three dimensional representative volume element (RVE). RVE is featured as by 3×3×3 eight-nodes solid finite elements, which has 216 crystal orientations. This FE analysis code can predict the deformation, strain and stress evolutions in the wire drawing processes in the macro- scales, and further the crystal texture andmore » hardening evolutions in the micro-scale. In this study, we analyzed the texture evolution in the wire drawing processes by our two-scale FE analysis code under conditions of various drawing angles of dice. We evaluates the texture evolution in the surface and center regions of the wire cross section, and to clarify the effects of processing conditions on the texture evolution.« less

Process metallurgy simulation for metal drawing process optimization by using two-scale finite element method

NASA Astrophysics Data System (ADS)

Nakamachi, Eiji; Yoshida, Takashi; Kuramae, Hiroyuki; Morimoto, Hideo; Yamaguchi, Toshihiko; Morita, Yusuke

2014-10-01

We developed two-scale FE analysis procedure based on the crystallographic homogenization method by considering the hierarchical structure of poly-crystal aluminium alloy metal. It can be characterized as the combination of two-scale structure, such as the microscopic polycrystal structure and the macroscopic elastic plastic continuum. Micro polycrystal structure can be modeled as a three dimensional representative volume element (RVE). RVE is featured as by 3×3×3 eight-nodes solid finite elements, which has 216 crystal orientations. This FE analysis code can predict the deformation, strain and stress evolutions in the wire drawing processes in the macro- scales, and further the crystal texture and hardening evolutions in the micro-scale. In this study, we analyzed the texture evolution in the wire drawing processes by our two-scale FE analysis code under conditions of various drawing angles of dice. We evaluates the texture evolution in the surface and center regions of the wire cross section, and to clarify the effects of processing conditions on the texture evolution.

Using X-Ray In-Line Phase-Contrast Imaging for the Investigation of Nude Mouse Hepatic Tumors

PubMed Central

Zhang, Lu; Luo, Shuqian

2012-01-01

The purpose of this paper is to report the noninvasive imaging of hepatic tumors without contrast agents. Both normal tissues and tumor tissues can be detected, and tumor tissues in different stages can be classified quantitatively. We implanted BEL-7402 human hepatocellular carcinoma cells into the livers of nude mice and then imaged the livers using X-ray in-line phase-contrast imaging (ILPCI). The projection images' texture feature based on gray level co-occurrence matrix (GLCM) and dual-tree complex wavelet transforms (DTCWT) were extracted to discriminate normal tissues and tumor tissues. Different stages of hepatic tumors were classified using support vector machines (SVM). Images of livers from nude mice sacrificed 6 days after inoculation with cancer cells show diffuse distribution of the tumor tissue, but images of livers from nude mice sacrificed 9, 12, or 15 days after inoculation with cancer cells show necrotic lumps in the tumor tissue. The results of the principal component analysis (PCA) of the texture features based on GLCM of normal regions were positive, but those of tumor regions were negative. The results of PCA of the texture features based on DTCWT of normal regions were greater than those of tumor regions. The values of the texture features in low-frequency coefficient images increased monotonically with the growth of the tumors. Different stages of liver tumors can be classified using SVM, and the accuracy is 83.33%. Noninvasive and micron-scale imaging can be achieved by X-ray ILPCI. We can observe hepatic tumors and small vessels from the phase-contrast images. This new imaging approach for hepatic cancer is effective and has potential use in the early detection and classification of hepatic tumors. PMID:22761929

Bio-inspired structural bistability employing elastomeric origami for morphing applications

NASA Astrophysics Data System (ADS)

Daynes, Stephen; Trask, Richard S.; Weaver, Paul M.

2014-12-01

A structural concept based upon the principles of adaptive morphing cells is presented whereby controlled bistability from a flat configuration into a textured arrangement is shown. The material consists of multiple cells made from silicone rubber with locally reinforced regions based upon kirigami principles. On pneumatic actuation these cells fold or unfold based on the fold lines created by the interaction of the geometry with the reinforced regions. Each cell is able to maintain its shape in either a retracted or deployed state, without the aid of mechanisms or sustained actuation, due to the existence of structural bistability. Mathematical quantification of the surface texture is introduced, based on out-of-plane deviations of a deployed structure compared to a reference plane. Additionally, finite element analysis is employed to characterize the geometry and stability of an individual cell during actuation and retraction. This investigation highlights the critical role that angular rotation, at the center of each cell, plays on the deployment angle as it transitions through the elastically deployed configuration. The analysis of this novel concept is presented and a pneumatically actuated proof-of-concept demonstrator is fabricated.

Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties.

PubMed

Jacobs, Richard H A H; Haak, Koen V; Thumfart, Stefan; Renken, Remco; Henson, Brian; Cornelissen, Frans W

2016-01-01

Our world is filled with texture. For the human visual system, this is an important source of information for assessing environmental and material properties. Indeed-and presumably for this reason-the human visual system has regions dedicated to processing textures. Despite their abundance and apparent relevance, only recently the relationships between texture features and high-level judgments have captured the interest of mainstream science, despite long-standing indications for such relationships. In this study, we explore such relationships, as these might be used to predict perceived texture qualities. This is relevant, not only from a psychological/neuroscience perspective, but also for more applied fields such as design, architecture, and the visual arts. In two separate experiments, observers judged various qualities of visual textures such as beauty, roughness, naturalness, elegance, and complexity. Based on factor analysis, we find that in both experiments, ~75% of the variability in the judgments could be explained by a two-dimensional space, with axes that are closely aligned to the beauty and roughness judgments. That a two-dimensional judgment space suffices to capture most of the variability in the perceived texture qualities suggests that observers use a relatively limited set of internal scales on which to base various judgments, including aesthetic ones. Finally, for both of these judgments, we determined the relationship with a large number of texture features computed for each of the texture stimuli. We find that the presence of lower spatial frequencies, oblique orientations, higher intensity variation, higher saturation, and redness correlates with higher beauty ratings. Features that captured image intensity and uniformity correlated with roughness ratings. Therefore, a number of computational texture features are predictive of these judgments. This suggests that perceived texture qualities-including the aesthetic appreciation-are sufficiently universal to be predicted-with reasonable accuracy-based on the computed feature content of the textures.

Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties

PubMed Central

Jacobs, Richard H. A. H.; Haak, Koen V.; Thumfart, Stefan; Renken, Remco; Henson, Brian; Cornelissen, Frans W.

2016-01-01

Our world is filled with texture. For the human visual system, this is an important source of information for assessing environmental and material properties. Indeed—and presumably for this reason—the human visual system has regions dedicated to processing textures. Despite their abundance and apparent relevance, only recently the relationships between texture features and high-level judgments have captured the interest of mainstream science, despite long-standing indications for such relationships. In this study, we explore such relationships, as these might be used to predict perceived texture qualities. This is relevant, not only from a psychological/neuroscience perspective, but also for more applied fields such as design, architecture, and the visual arts. In two separate experiments, observers judged various qualities of visual textures such as beauty, roughness, naturalness, elegance, and complexity. Based on factor analysis, we find that in both experiments, ~75% of the variability in the judgments could be explained by a two-dimensional space, with axes that are closely aligned to the beauty and roughness judgments. That a two-dimensional judgment space suffices to capture most of the variability in the perceived texture qualities suggests that observers use a relatively limited set of internal scales on which to base various judgments, including aesthetic ones. Finally, for both of these judgments, we determined the relationship with a large number of texture features computed for each of the texture stimuli. We find that the presence of lower spatial frequencies, oblique orientations, higher intensity variation, higher saturation, and redness correlates with higher beauty ratings. Features that captured image intensity and uniformity correlated with roughness ratings. Therefore, a number of computational texture features are predictive of these judgments. This suggests that perceived texture qualities—including the aesthetic appreciation—are sufficiently universal to be predicted—with reasonable accuracy—based on the computed feature content of the textures. PMID:27493628

A study on using texture analysis methods for identifying lobar fissure regions in isotropic CT images.

PubMed

Wei, Q; Hu, Y

2009-01-01

The major hurdle for segmenting lung lobes in computed tomographic (CT) images is to identify fissure regions, which encase lobar fissures. Accurate identification of these regions is difficult due to the variable shape and appearance of the fissures, along with the low contrast and high noise associated with CT images. This paper studies the effectiveness of two texture analysis methods - the gray level co-occurrence matrix (GLCM) and the gray level run length matrix (GLRLM) - in identifying fissure regions from isotropic CT image stacks. To classify GLCM and GLRLM texture features, we applied a feed-forward back-propagation neural network and achieved the best classification accuracy utilizing 16 quantized levels for computing the GLCM and GLRLM texture features and 64 neurons in the input/hidden layers of the neural network. Tested on isotropic CT image stacks of 24 patients with the pathologic lungs, we obtained accuracies of 86% and 87% for identifying fissure regions using the GLCM and GLRLM methods, respectively. These accuracies compare favorably with surgeons/radiologists' accuracy of 80% for identifying fissure regions in clinical settings. This shows promising potential for segmenting lung lobes using the GLCM and GLRLM methods.

[Visual Texture Agnosia in Humans].

PubMed

Suzuki, Kyoko

2015-06-01

Visual object recognition requires the processing of both geometric and surface properties. Patients with occipital lesions may have visual agnosia, which is impairment in the recognition and identification of visually presented objects primarily through their geometric features. An analogous condition involving the failure to recognize an object by its texture may exist, which can be called visual texture agnosia. Here we present two cases with visual texture agnosia. Case 1 had left homonymous hemianopia and right upper quadrantanopia, along with achromatopsia, prosopagnosia, and texture agnosia, because of damage to his left ventromedial occipitotemporal cortex and right lateral occipito-temporo-parietal cortex due to multiple cerebral embolisms. Although he showed difficulty matching and naming textures of real materials, he could readily name visually presented objects by their contours. Case 2 had right lower quadrantanopia, along with impairment in stereopsis and recognition of texture in 2D images, because of subcortical hemorrhage in the left occipitotemporal region. He failed to recognize shapes based on texture information, whereas shape recognition based on contours was well preserved. Our findings, along with those of three reported cases with texture agnosia, indicate that there are separate channels for processing texture, color, and geometric features, and that the regions around the left collateral sulcus are crucial for texture processing.

Hepatic CT image query using Gabor features

NASA Astrophysics Data System (ADS)

Zhao, Chenguang; Cheng, Hongyan; Zhuang, Tiange

2004-07-01

A retrieval scheme for liver computerize tomography (CT) images based on Gabor texture is presented. For each hepatic CT image, we manually delineate abnormal regions within liver area. Then, a continuous Gabor transform is utilized to analyze the texture of the pathology bearing region and extract the corresponding feature vectors. For a given sample image, we compare its feature vector with those of other images. Similar images with the highest rank are retrieved. In experiments, 45 liver CT images are collected, and the effectiveness of Gabor texture for content based retrieval is verified.

Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mattonen, Sarah A.; Palma, David A.; Department of Oncology, The University of Western Ontario, London, Ontario N6A 4L6

Purpose: Benign computed tomography (CT) changes due to radiation induced lung injury (RILI) are common following stereotactic ablative radiotherapy (SABR) and can be difficult to differentiate from tumor recurrence. The authors measured the ability of CT image texture analysis, compared to more traditional measures of response, to predict eventual cancer recurrence based on CT images acquired within 5 months of treatment. Methods: A total of 24 lesions from 22 patients treated with SABR were selected for this study: 13 with moderate to severe benign RILI, and 11 with recurrence. Three-dimensional (3D) consolidative and ground-glass opacity (GGO) changes were manually delineatedmore » on all follow-up CT scans. Two size measures of the consolidation regions (longest axial diameter and 3D volume) and nine appearance features of the GGO were calculated: 2 first-order features [mean density and standard deviation of density (first-order texture)], and 7 second-order texture features [energy, entropy, correlation, inverse difference moment (IDM), inertia, cluster shade, and cluster prominence]. For comparison, the corresponding response evaluation criteria in solid tumors measures were also taken for the consolidation regions. Prediction accuracy was determined using the area under the receiver operating characteristic curve (AUC) and two-fold cross validation (CV). Results: For this analysis, 46 diagnostic CT scans scheduled for approximately 3 and 6 months post-treatment were binned based on their recorded scan dates into 2–5 month and 5–8 month follow-up time ranges. At 2–5 months post-treatment, first-order texture, energy, and entropy provided AUCs of 0.79–0.81 using a linear classifier. On two-fold CV, first-order texture yielded 73% accuracy versus 76%–77% with the second-order features. The size measures of the consolidative region, longest axial diameter and 3D volume, gave two-fold CV accuracies of 60% and 57%, and AUCs of 0.72 and 0.65, respectively. Conclusions: Texture measures of the GGO appearance following SABR demonstrated the ability to predict recurrence in individual patients within 5 months of SABR treatment. Appearance changes were also shown to be more accurately predictive of recurrence, as compared to size measures within the same time period. With further validation, these results could form the substrate for a clinically useful computer-aided diagnosis tool which could provide earlier salvage of patients with recurrence.« less

3D Texture Features Mining for MRI Brain Tumor Identification

NASA Astrophysics Data System (ADS)

Rahim, Mohd Shafry Mohd; Saba, Tanzila; Nayer, Fatima; Syed, Afraz Zahra

2014-03-01

Medical image segmentation is a process to extract region of interest and to divide an image into its individual meaningful, homogeneous components. Actually, these components will have a strong relationship with the objects of interest in an image. For computer-aided diagnosis and therapy process, medical image segmentation is an initial mandatory step. Medical image segmentation is a sophisticated and challenging task because of the sophisticated nature of the medical images. Indeed, successful medical image analysis heavily dependent on the segmentation accuracy. Texture is one of the major features to identify region of interests in an image or to classify an object. 2D textures features yields poor classification results. Hence, this paper represents 3D features extraction using texture analysis and SVM as segmentation technique in the testing methodologies.

Gender and age effects in structural brain asymmetry as measured by MRI texture analysis.

PubMed

Kovalev, Vassili A; Kruggel, Frithjof; von Cramon, D Yves

2003-07-01

Effects of gender and age on structural brain asymmetry were studied by 3D texture analysis in 380 adults. Asymmetry is detected by comparing the complex 3D gray-scale image patterns in the left and right cerebral hemispheres as revealed by anatomical T1-weighted MRI datasets. The Talairach and Tournoux parcellation system was applied to study the asymmetry on five levels: the whole cerebrum, nine coronal sections, 12 axial sections, boxes resulting from both coronal and axial subdivisions, and by a sliding spherical window of 9 mm diameter. The analysis revealed that the brain asymmetry increases in the anterior-posterior direction starting from the central region onward. Male brains were found to be more asymmetric than female. This gender-related effect is noticeable in all brain areas but is most significant in the superior temporal gyrus, Heschl's gyrus, the adjacent white matter regions in the temporal stem and the knee of the optic radiation, the thalamus, and the posterior cingulate. The brain asymmetry increases significantly with age in the inferior frontal gyrus, anterior insula, anterior cingulate, parahippocampal gyrus, retrosplenial cortex, coronal radiata, and knee region of the internal capsule. Asymmetry decreases with age in the optic radiation, precentral gyrus, and angular gyrus. The texture-based method reported here is based on extended multisort cooccurrence matrices that employ intensity, gradient, and anisotropy features in a uniform way. It is sensitive, simple to reproduce, robust, and unbiased in the sense that segmentation of brain compartments and spatial transformations are not necessary. Thus, it should be considered as another tool for digital morphometry in neuroscience.

A Fourier-based textural feature extraction procedure

NASA Technical Reports Server (NTRS)

Stromberg, W. D.; Farr, T. G.

1986-01-01

A procedure is presented to discriminate and characterize regions of uniform image texture. The procedure utilizes textural features consisting of pixel-by-pixel estimates of the relative emphases of annular regions of the Fourier transform. The utility and derivation of the features are described through presentation of a theoretical justification of the concept followed by a heuristic extension to a real environment. Two examples are provided that validate the technique on synthetic images and demonstrate its applicability to the discrimination of geologic texture in a radar image of a tropical vegetated area.

Cloud field classification based upon high spatial resolution textural features. I - Gray level co-occurrence matrix approach

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Chen, D. W.

1988-01-01

Stratocumulus, cumulus, and cirrus clouds were identified on the basis of cloud textural features which were derived from a single high-resolution Landsat MSS NIR channel using a stepwise linear discriminant analysis. It is shown that, using this method, it is possible to distinguish high cirrus clouds from low clouds with high accuracy on the basis of spatial brightness patterns. The largest probability of misclassification is associated with confusion between the stratocumulus breakup regions and the fair-weather cumulus.

Vehicle Detection of Aerial Image Using TV-L1 Texture Decomposition

NASA Astrophysics Data System (ADS)

Wang, Y.; Wang, G.; Li, Y.; Huang, Y.

2016-06-01

Vehicle detection from high-resolution aerial image facilitates the study of the public traveling behavior on a large scale. In the context of road, a simple and effective algorithm is proposed to extract the texture-salient vehicle among the pavement surface. Texturally speaking, the majority of pavement surface changes a little except for the neighborhood of vehicles and edges. Within a certain distance away from the given vector of the road network, the aerial image is decomposed into a smoothly-varying cartoon part and an oscillatory details of textural part. The variational model of Total Variation regularization term and L1 fidelity term (TV-L1) is adopted to obtain the salient texture of vehicles and the cartoon surface of pavement. To eliminate the noise of texture decomposition, regions of pavement surface are refined by seed growing and morphological operation. Based on the shape saliency analysis of the central objects in those regions, vehicles are detected as the objects of rectangular shape saliency. The proposed algorithm is tested with a diverse set of aerial images that are acquired at various resolution and scenarios around China. Experimental results demonstrate that the proposed algorithm can detect vehicles at the rate of 71.5% and the false alarm rate of 21.5%, and that the speed is 39.13 seconds for a 4656 x 3496 aerial image. It is promising for large-scale transportation management and planning.

Adaptive weighted local textural features for illumination, expression, and occlusion invariant face recognition

NASA Astrophysics Data System (ADS)

Cui, Chen; Asari, Vijayan K.

2014-03-01

Biometric features such as fingerprints, iris patterns, and face features help to identify people and restrict access to secure areas by performing advanced pattern analysis and matching. Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. However, the recognition results obtained by face recognition systems are a affected by several variations that may happen to the patterns in an unrestricted environment. As a result, several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition still remains as a difficult issue that needs to be resolved. In this paper, we propose a novel approach to tackling some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern (ELBP) description of all the local regions of the face. The relationship of each pixel with respect to its neighborhood is extracted and employed to calculate the new representation. ELBP reconstructs a much better textural feature extraction vector from an original gray level image in different lighting conditions. The dimensionality of the texture image is reduced by principal component analysis performed on each local face region. Each low dimensional vector representing a local region is now weighted based on the significance of the sub-region. The weight of each sub-region is determined by employing the local variance estimate of the respective region, which represents the significance of the region. The final facial textural feature vector is obtained by concatenating the reduced dimensional weight sets of all the modules (sub-regions) of the face image. Experiments conducted on various popular face databases show promising performance of the proposed algorithm in varying lighting, expression, and partial occlusion conditions. Four databases were used for testing the performance of the proposed system: Yale Face database, Extended Yale Face database B, Japanese Female Facial Expression database, and CMU AMP Facial Expression database. The experimental results in all four databases show the effectiveness of the proposed system. Also, the computation cost is lower because of the simplified calculation steps. Research work is progressing to investigate the effectiveness of the proposed face recognition method on pose-varying conditions as well. It is envisaged that a multilane approach of trained frameworks at different pose bins and an appropriate voting strategy would lead to a good recognition rate in such situation.

Preliminary evaluation of a fully automated quantitative framework for characterizing general breast tissue histology via color histogram and color texture analysis

NASA Astrophysics Data System (ADS)

Keller, Brad M.; Gastounioti, Aimilia; Batiste, Rebecca C.; Kontos, Despina; Feldman, Michael D.

2016-03-01

Visual characterization of histologic specimens is known to suffer from intra- and inter-observer variability. To help address this, we developed an automated framework for characterizing digitized histology specimens based on a novel application of color histogram and color texture analysis. We perform a preliminary evaluation of this framework using a set of 73 trichrome-stained, digitized slides of normal breast tissue which were visually assessed by an expert pathologist in terms of the percentage of collagenous stroma, stromal collagen density, duct-lobular unit density and the presence of elastosis. For each slide, our algorithm automatically segments the tissue region based on the lightness channel in CIELAB colorspace. Within each tissue region, a color histogram feature vector is extracted using a common color palette for trichrome images generated with a previously described method. Then, using a whole-slide, lattice-based methodology, color texture maps are generated using a set of color co-occurrence matrix statistics: contrast, correlation, energy and homogeneity. The extracted features sets are compared to the visually assessed tissue characteristics. Overall, the extracted texture features have high correlations to both the percentage of collagenous stroma (r=0.95, p<0.001) and duct-lobular unit density (r=0.71, p<0.001) seen in the tissue samples, and several individual features were associated with either collagen density and/or the presence of elastosis (p<=0.05). This suggests that the proposed framework has promise as a means to quantitatively extract descriptors reflecting tissue-level characteristics and thus could be useful in detecting and characterizing histological processes in digitized histology specimens.

GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes

NASA Astrophysics Data System (ADS)

Chaddad, Ahmad; Desrosiers, Christian; Toews, Matthew

2016-03-01

Glioblastoma multiforme (GBM) is the most common malignant primary tumor of the central nervous system, characterized among other traits by rapid metastatis. Three tissue phenotypes closely associated with GBMs, namely, necrosis (N), contrast enhancement (CE), and edema/invasion (E), exhibit characteristic patterns of texture heterogeneity in magnetic resonance images (MRI). In this study, we propose a novel model to characterize GBM tissue phenotypes using gray level co-occurrence matrices (GLCM) in three anatomical planes. The GLCM encodes local image patches in terms of informative, orientation-invariant texture descriptors, which are used here to sub-classify GBM tissue phenotypes. Experiments demonstrate the model on MRI data of 41 GBM patients, obtained from the cancer genome atlas (TCGA). Intensity-based automatic image registration is applied to align corresponding pairs of fixed T1˗weighted (T1˗WI) post-contrast and fluid attenuated inversion recovery (FLAIR) images. GBM tissue regions are then segmented using the 3D Slicer tool. Texture features are computed from 12 quantifier functions operating on GLCM descriptors, that are generated from MRI intensities within segmented GBM tissue regions. Various classifier models are used to evaluate the effectiveness of texture features for discriminating between GBM phenotypes. Results based on T1-WI scans showed a phenotype classification accuracy of over 88.14%, a sensitivity of 85.37% and a specificity of 96.1%, using the linear discriminant analysis (LDA) classifier. This model has the potential to provide important characteristics of tumors, which can be used for the sub-classification of GBM phenotypes.

Computer aided diagnosis based on medical image processing and artificial intelligence methods

NASA Astrophysics Data System (ADS)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-12-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer.

PubMed

Yang, Fei; Thomas, Maria A; Dehdashti, Farrokh; Grigsby, Perry W

2013-05-01

The aim of this pilot study was to explore heterogeneity in the temporal behavior of intratumoral [(18)F]fluorodeoxyglucose (FDG) accumulation at a regional scale in patients with cervical cancer undergoing chemoradiotherapy. Included in the study were 20 patients with FIGO stages IB1 to IVA cervical cancer treated with combined chemoradiotherapy. Patients underwent FDG PET/CT before treatment, during weeks 2 and 4 of treatment, and 12 weeks after completion of therapy. Patients were classified based on response to therapy as showing a complete metabolic response (CMR), a partial metabolic response (PMR), or residual disease and the development of new disease (NEW). Based on the presence of residual primary tumor following therapy, patients were divided into two groups, CMR and PMR/NEW. Temporal profiles of intratumoral FDG heterogeneity as characterized by textural features at a regional scale were assessed and compared with those of the standardized uptake value (SUV) indices (SUVmax and SUVmean) within the context of differentiating response groups. Textural features at a regional scale with emphasis on characterizing contiguous regions of high uptake in tumors decreased significantly with time (P < 0.001) in the CMR group, while features describing contiguous regions of low uptake along with those measuring the nonuniformity of contiguous isointense regions in tumors exhibited significant temporal changes in the PMR/NEW group (P < 0.03) but showed no persistent trends with time. Both SUV indices showed significant changes during the course of the disease in both patient groups (P < 0.001 for SUVmax and SUVmean in the CMR group; P = 0.0109 and 0.0136, respectively, for SUVmax and SUVmean in the PMR/NEW group), and also decreased at a constant rate in the CMR group and decreased up to the 4th week of treatment and then increased in the PMR/NEW group. The temporal changes in the heterogeneity of intratumoral FDG distribution characterized at a regional scale using image-based textural features may provide an adjunctive or alternative option for understanding tumor response to chemoradiotherapy and interpreting FDG accumulation dynamics in patients with malignant cervical tumors during the course of the disease.

Monitoring of bone regeneration process by means of texture analysis

NASA Astrophysics Data System (ADS)

Kokkinou, E.; Boniatis, I.; Costaridou, L.; Saridis, A.; Panagiotopoulos, E.; Panayiotakis, G.

2009-09-01

An image analysis method is proposed for the monitoring of the regeneration of the tibial bone. For this purpose, 130 digitized radiographs of 13 patients, who had undergone tibial lengthening by the Ilizarov method, were studied. For each patient, 10 radiographs, taken at an equal number of postoperative successive time moments, were available. Employing available software, 3 Regions Of Interest (ROIs), corresponding to the: (a) upper, (b) central, and (c) lower aspect of the gap, where bone regeneration was expected to occur, were determined on each radiograph. Employing custom developed algorithms: (i) a number of textural features were generated from each of the ROIs, and (ii) a texture-feature based regression model was designed for the quantitative monitoring of the bone regeneration process. Statistically significant differences (p < 0.05) were derived for the initial and the final textural features values, generated from the first and the last postoperatively obtained radiographs, respectively. A quadratic polynomial regression equation fitted data adequately (r2 = 0.9, p < 0.001). The suggested method may contribute to the monitoring of the tibial bone regeneration process.

Extracting built-up areas from TerraSAR-X data using object-oriented classification method

NASA Astrophysics Data System (ADS)

Wang, SuYun; Sun, Z. C.

2017-02-01

Based on single-polarized TerraSAR-X, the approach generates homogeneous segments on an arbitrary number of scale levels by applying a region-growing algorithm which takes the intensity of backscatter and shape-related properties into account. The object-oriented procedure consists of three main steps: firstly, the analysis of the local speckle behavior in the SAR intensity data, leading to the generation of a texture image; secondly, a segmentation based on the intensity image; thirdly, the classification of each segment using the derived texture file and intensity information in order to identify and extract build-up areas. In our research, the distribution of BAs in Dongying City is derived from single-polarized TSX SM image (acquired on 17th June 2013) with average ground resolution of 3m using our proposed approach. By cross-validating the random selected validation points with geo-referenced field sites, Quick Bird high-resolution imagery, confusion matrices with statistical indicators are calculated and used for assessing the classification results. The results demonstrate that an overall accuracy 92.89 and a kappa coefficient of 0.85 could be achieved. We have shown that connect texture information with the analysis of the local speckle divergence, combining texture and intensity of construction extraction is feasible, efficient and rapid.

Quaternion-Based Texture Analysis of Multiband Satellite Images: Application to the Estimation of Aboveground Biomass in the East Region of Cameroon.

PubMed

Djiongo Kenfack, Cedrigue Boris; Monga, Olivier; Mpong, Serge Moto; Ndoundam, René

2018-03-01

Within the last decade, several approaches using quaternion numbers to handle and model multiband images in a holistic manner were introduced. The quaternion Fourier transform can be efficiently used to model texture in multidimensional data such as color images. For practical application, multispectral satellite data appear as a primary source for measuring past trends and monitoring changes in forest carbon stocks. In this work, we propose a texture-color descriptor based on the quaternion Fourier transform to extract relevant information from multiband satellite images. We propose a new multiband image texture model extraction, called FOTO++, in order to address biomass estimation issues. The first stage consists in removing noise from the multispectral data while preserving the edges of canopies. Afterward, color texture descriptors are extracted thanks to a discrete form of the quaternion Fourier transform, and finally the support vector regression method is used to deduce biomass estimation from texture indices. Our texture features are modeled using a vector composed with the radial spectrum coming from the amplitude of the quaternion Fourier transform. We conduct several experiments in order to study the sensitivity of our model to acquisition parameters. We also assess its performance both on synthetic images and on real multispectral images of Cameroonian forest. The results show that our model is more robust to acquisition parameters than the classical Fourier Texture Ordination model (FOTO). Our scheme is also more accurate for aboveground biomass estimation. We stress that a similar methodology could be implemented using quaternion wavelets. These results highlight the potential of the quaternion-based approach to study multispectral satellite images.

Textural features for radar image analysis

NASA Technical Reports Server (NTRS)

Shanmugan, K. S.; Narayanan, V.; Frost, V. S.; Stiles, J. A.; Holtzman, J. C.

1981-01-01

Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.

Texture analysis based on the Hermite transform for image classification and segmentation

NASA Astrophysics Data System (ADS)

Estudillo-Romero, Alfonso; Escalante-Ramirez, Boris; Savage-Carmona, Jesus

2012-06-01

Texture analysis has become an important task in image processing because it is used as a preprocessing stage in different research areas including medical image analysis, industrial inspection, segmentation of remote sensed imaginary, multimedia indexing and retrieval. In order to extract visual texture features a texture image analysis technique is presented based on the Hermite transform. Psychovisual evidence suggests that the Gaussian derivatives fit the receptive field profiles of mammalian visual systems. The Hermite transform describes locally basic texture features in terms of Gaussian derivatives. Multiresolution combined with several analysis orders provides detection of patterns that characterizes every texture class. The analysis of the local maximum energy direction and steering of the transformation coefficients increase the method robustness against the texture orientation. This method presents an advantage over classical filter bank design because in the latter a fixed number of orientations for the analysis has to be selected. During the training stage, a subset of the Hermite analysis filters is chosen in order to improve the inter-class separability, reduce dimensionality of the feature vectors and computational cost during the classification stage. We exhaustively evaluated the correct classification rate of real randomly selected training and testing texture subsets using several kinds of common used texture features. A comparison between different distance measurements is also presented. Results of the unsupervised real texture segmentation using this approach and comparison with previous approaches showed the benefits of our proposal.

Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound.

PubMed

Virmani, Jitendra; Kumar, Vinod; Kalra, Naveen; Khandelwal, Niranjan

2014-08-01

A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.

Parenchymal Texture Analysis in Digital Breast Tomosynthesis for Breast Cancer Risk Estimation: A Preliminary Study

PubMed Central

Kontos, Despina; Bakic, Predrag R.; Carton, Ann-Katherine; Troxel, Andrea B.; Conant, Emily F.; Maidment, Andrew D.A.

2009-01-01

Rationale and Objectives Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superimposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superimposition, offering superior parenchymal texture visualization compared to mammography. Our study investigates the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation. Materials and Methods DBT and digital mammography (DM) images of 39 women were analyzed. Texture features, shown in studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. We compared the relative performance of DBT and DM texture features in correlating with two measures of breast cancer risk: (i) the Gail and Claus risk estimates, and (ii) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk. Results No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density (PD) than DM (p ≤0.05). When dividing our study population in groups of increasing breast PD, the DBT texture features appeared to be more discriminative, having regression lines with overall lower p-values, steeper slopes, and higher R2 estimates. Conclusion Although preliminary, our results suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation. PMID:19201357

Texture Analysis of Poly-Adenylated mRNA Staining Following Global Brain Ischemia and Reperfusion

PubMed Central

Szymanski, Jeffrey J.; Jamison, Jill T.; DeGracia, Donald J.

2011-01-01

Texture analysis provides a means to quantify complex changes in microscope images. We previously showed that cytoplasmic poly-adenylated mRNAs form mRNA granules in post-ischemic neurons and that these granules correlated with protein synthesis inhibition and hence cell death. Here we utilized the texture analysis software MaZda to quantify mRNA granules in photomicrographs of the pyramidal cell layer of rat hippocampal region CA3 around 1 hour of reperfusion after 10 min of normothermic global cerebral ischemia. At 1 hour reperfusion, we observed variations in the texture of mRNA granules amongst samples that were readily quantified by texture analysis. Individual sample variation was consistent with the interpretation that animal-to-animal variations in mRNA granules reflected the time-course of mRNA granule formation. We also used texture analysis to quantify the effect of cycloheximide, given either before or after brain ischemia, on mRNA granules. If administered before ischemia, cycloheximide inhibited mRNA granule formation, but if administered after ischemia did not prevent mRNA granulation, indicating mRNA granule formation is dependent on dissociation of polysomes. We conclude that texture analysis is an effective means for quantifying the complex morphological changes induced in neurons by brain ischemia and reperfusion. PMID:21477879

Chemometric approach to texture profile analysis of kombucha fermented milk products.

PubMed

Malbaša, Radomir; Jevrić, Lidija; Lončar, Eva; Vitas, Jasmina; Podunavac-Kuzmanović, Sanja; Milanović, Spasenija; Kovačević, Strahinja

2015-09-01

In the present work, relationships between the textural characteristics of fermented milk products obtained by kombucha inoculums with various teas were investigated by using chemometric analysis. The presented data which describe numerically the textural characteristics (firmness, consistency, cohesiveness and index of viscosity) were analysed. The quadratic correlation was determined between the textural characteristics of fermented milk products obtained at fermentation temperatures of 40 and 43 °C, using milk with 0.8, 1.6 and 2.8% milk fat and kombucha inoculums cultivated on the extracts of peppermint, stinging nettle, wild thyme and winter savory. Hierarchical cluster analysis (HCA) was performed to identify the similarities among the fermented products. The best mathematical models predicting the textural characteristics of investigated samples were developed. The results of this study indicate that textural characteristics of sample based on winter savory have a significant effect on textural characteristics of samples based on peppermint, stinging nettle and wild thyme, which can be very useful in the determination of products texture profile.

Development and testing of texture discriminators for the analysis of trabecular bone in proximal femur radiographs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huber, M. B.; Carballido-Gamio, J.; Fritscher, K.

2009-11-15

Purpose: Texture analysis of femur radiographs may serve as a potential low cost technique to predict osteoporotic fracture risk and has received considerable attention in the past years. A further application of this technique may be the measurement of the quality of specific bone compartments to provide useful information for treatment of bone fractures. Two challenges of texture analysis are the selection of the best suitable texture measure and reproducible placement of regions of interest (ROIs). The goal of this in vitro study was to automatically place ROIs in radiographs of proximal femur specimens and to calculate correlations between variousmore » different texture analysis methods and the femurs' anchorage strength. Methods: Radiographs were obtained from 14 femoral specimens and bone mineral density (BMD) was measured in the femoral neck. Biomechanical testing was performed to assess the anchorage strength in terms of failure load, breakaway torque, and number of cycles. Images were segmented using a framework that is based on the usage of level sets and statistical in-shape models. Five ROIs were automatically placed in the head, upper and lower neck, trochanteric, and shaft compartment in an atlas subject. All other subjects were registered rigidly, affinely, and nonlinearly, and the resulting transformation was used to map the five ROIs onto the individual femora. Results: In each ROI, texture features were extracted using gray level co-occurence matrices (GLCM), third-order GLCM, morphological gradients (MGs), Minkowski dimensions (MDs), Minkowski functionals (MFs), Gaussian Markov random fields, and scaling index method (SIM). Coefficients of determination for each texture feature with parameters of anchorage strength were computed. In a stepwise multiregression analysis, the most predictive parameters were identified in different models. Texture features were highly correlated with anchorage strength estimated by the failure load of up to R{sup 2}=0.61 (MF and MG features, p<0.01) and were partially independent of BMD. The correlations were dependent on the choice of the ROI and the texture measure. The best predictive multiregression model for failure load R{sub adj}{sup 2}=0.86 (p<0.001) included a set of recently developed texture methods (MF and SIM) but excluded bone mineral density and commonly used texture measures. Conclusions: The results suggest that texture information contained in trabecular bone structure visualized on radiographs may predict whether an implant anchorage can be used and may determine the local bone quality from preoperative radiographs.« less

Person-independent facial expression analysis by fusing multiscale cell features

NASA Astrophysics Data System (ADS)

Zhou, Lubing; Wang, Han

2013-03-01

Automatic facial expression recognition is an interesting and challenging task. To achieve satisfactory accuracy, deriving a robust facial representation is especially important. A novel appearance-based feature, the multiscale cell local intensity increasing patterns (MC-LIIP), to represent facial images and conduct person-independent facial expression analysis is presented. The LIIP uses a decimal number to encode the texture or intensity distribution around each pixel via pixel-to-pixel intensity comparison. To boost noise resistance, MC-LIIP carries out comparison computation on the average values of scalable cells instead of individual pixels. The facial descriptor fuses region-based histograms of MC-LIIP features from various scales, so as to encode not only textural microstructures but also the macrostructures of facial images. Finally, a support vector machine classifier is applied for expression recognition. Experimental results on the CK+ and Karolinska directed emotional faces databases show the superiority of the proposed method.

Classification of Korla fragrant pears using NIR hyperspectral imaging analysis

NASA Astrophysics Data System (ADS)

Rao, Xiuqin; Yang, Chun-Chieh; Ying, Yibin; Kim, Moon S.; Chao, Kuanglin

2012-05-01

Korla fragrant pears are small oval pears characterized by light green skin, crisp texture, and a pleasant perfume for which they are named. Anatomically, the calyx of a fragrant pear may be either persistent or deciduous; the deciduouscalyx fruits are considered more desirable due to taste and texture attributes. Chinese packaging standards require that packed cases of fragrant pears contain 5% or less of the persistent-calyx type. Near-infrared hyperspectral imaging was investigated as a potential means for automated sorting of pears according to calyx type. Hyperspectral images spanning the 992-1681 nm region were acquired using an EMCCD-based laboratory line-scan imaging system. Analysis of the hyperspectral images was performed to select wavebands useful for identifying persistent-calyx fruits and for identifying deciduous-calyx fruits. Based on the selected wavebands, an image-processing algorithm was developed that targets automated classification of Korla fragrant pears into the two categories for packaging purposes.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, J; Gong, G; Cui, Y

Purpose: To predict early pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multi-region analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Methods: In this institution review board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with a high-temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitativemore » Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Results: Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast wash-out were statistically significant (p< 0.05) after correcting for multiple testing, with area under the ROC curve or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (p = 0.002) in leave-one-out cross validation. This improved upon conventional imaging predictors such as tumor volume (AUC=0.53) and texture features based on whole-tumor analysis (AUC=0.65). Conclusion: The heterogeneity of the tumor subregion associated with fast wash-out on DCE-MRI predicted early pathological response to neoadjuvant chemotherapy in breast cancer.« less

Textural analysis of early-phase spatiotemporal changes in contrast enhancement of breast lesions imaged with an ultrafast DCE-MRI protocol.

PubMed

Milenković, Jana; Dalmış, Mehmet Ufuk; Žgajnar, Janez; Platel, Bram

2017-09-01

New ultrafast view-sharing sequences have enabled breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to be performed at high spatial and temporal resolution. The aim of this study is to evaluate the diagnostic potential of textural features that quantify the spatiotemporal changes of the contrast-agent uptake in computer-aided diagnosis of malignant and benign breast lesions imaged with high spatial and temporal resolution DCE-MRI. The proposed approach is based on the textural analysis quantifying the spatial variation of six dynamic features of the early-phase contrast-agent uptake of a lesion's largest cross-sectional area. The textural analysis is performed by means of the second-order gray-level co-occurrence matrix, gray-level run-length matrix and gray-level difference matrix. This yields 35 textural features to quantify the spatial variation of each of the six dynamic features, providing a feature set of 210 features in total. The proposed feature set is evaluated based on receiver operating characteristic (ROC) curve analysis in a cross-validation scheme for random forests (RF) and two support vector machine classifiers, with linear and radial basis function (RBF) kernel. Evaluation is done on a dataset with 154 breast lesions (83 malignant and 71 benign) and compared to a previous approach based on 3D morphological features and the average and standard deviation of the same dynamic features over the entire lesion volume as well as their average for the smaller region of the strongest uptake rate. The area under the ROC curve (AUC) obtained by the proposed approach with the RF classifier was 0.8997, which was significantly higher (P = 0.0198) than the performance achieved by the previous approach (AUC = 0.8704) on the same dataset. Similarly, the proposed approach obtained a significantly higher result for both SVM classifiers with RBF (P = 0.0096) and linear kernel (P = 0.0417) obtaining AUC of 0.8876 and 0.8548, respectively, compared to AUC values of previous approach of 0.8562 and 0.8311, respectively. The proposed approach based on 2D textural features quantifying spatiotemporal changes of the contrast-agent uptake significantly outperforms the previous approach based on 3D morphology and dynamic analysis in differentiating the malignant and benign breast lesions, showing its potential to aid clinical decision making. © 2017 American Association of Physicists in Medicine.

Computer-aided diagnosis with textural features for breast lesions in sonograms.

PubMed

Chen, Dar-Ren; Huang, Yu-Len; Lin, Sheng-Hsiung

2011-04-01

Computer-aided diagnosis (CAD) systems provided second beneficial support reference and enhance the diagnostic accuracy. This paper was aimed to develop and evaluate a CAD with texture analysis in the classification of breast tumors for ultrasound images. The ultrasound (US) dataset evaluated in this study composed of 1020 sonograms of region of interest (ROI) subimages from 255 patients. Two-view sonogram (longitudinal and transverse views) and four different rectangular regions were utilized to analyze each tumor. Six practical textural features from the US images were performed to classify breast tumors as benign or malignant. However, the textural features always perform as a high dimensional vector; high dimensional vector is unfavorable to differentiate breast tumors in practice. The principal component analysis (PCA) was used to reduce the dimension of textural feature vector and then the image retrieval technique was performed to differentiate between benign and malignant tumors. In the experiments, all the cases were sampled with k-fold cross-validation (k=10) to evaluate the performance with receiver operating characteristic (ROC) curve. The area (A(Z)) under the ROC curve for the proposed CAD system with the specific textural features was 0.925±0.019. The classification ability for breast tumor with textural information is satisfactory. This system differentiates benign from malignant breast tumors with a good result and is therefore clinically useful to provide a second opinion. Copyright © 2010 Elsevier Ltd. All rights reserved.

Description of textures by a structural analysis.

PubMed

Tomita, F; Shirai, Y; Tsuji, S

1982-02-01

A structural analysis system for describing natural textures is introduced. The analyzer automatically extracts the texture elements in an input image, measures their properties, classifies them into some distinctive classes (one ``ground'' class and some ``figure'' classes), and computes the distributions of the gray level, the shape, and the placement of the texture elements in each class. These descriptions are used for classification of texture images. An analysis-by-synthesis method for evaluating texture analyzers is also presented. We propose a synthesizer which generates a texture image based on the descriptions. By comparing the reconstructed image with the original one, we can see what information is preserved and what is lost in the descriptions.

Enhancement of Stereo Imagery by Artificial Texture Projection Generated Using a LIDAR

NASA Astrophysics Data System (ADS)

Veitch-Michaelis, Joshua; Muller, Jan-Peter; Walton, David; Storey, Jonathan; Foster, Michael; Crutchley, Benjamin

2016-06-01

Passive stereo imaging is capable of producing dense 3D data, but image matching algorithms generally perform poorly on images with large regions of homogenous texture due to ambiguous match costs. Stereo systems can be augmented with an additional light source that can project some form of unique texture onto surfaces in the scene. Methods include structured light, laser projection through diffractive optical elements, data projectors and laser speckle. Pattern projection using lasers has the advantage of producing images with a high signal to noise ratio. We have investigated the use of a scanning visible-beam LIDAR to simultaneously provide enhanced texture within the scene and to provide additional opportunities for data fusion in unmatched regions. The use of a LIDAR rather than a laser alone allows us to generate highly accurate ground truth data sets by scanning the scene at high resolution. This is necessary for evaluating different pattern projection schemes. Results from LIDAR generated random dots are presented and compared to other texture projection techniques. Finally, we investigate the use of image texture analysis to intelligently project texture where it is required while exploiting the texture available in the ambient light image.

A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier.

PubMed

Nanthagopal, A Padma; Rajamony, R Sukanesh

2012-07-01

The proposed system provides new textural information for segmenting tumours, efficiently and accurately and with less computational time, from benign and malignant tumour images, especially in smaller dimensions of tumour regions of computed tomography (CT) images. Region-based segmentation of tumour from brain CT image data is an important but time-consuming task performed manually by medical experts. The objective of this work is to segment brain tumour from CT images using combined grey and texture features with new edge features and nonlinear support vector machine (SVM) classifier. The selected optimal features are used to model and train the nonlinear SVM classifier to segment the tumour from computed tomography images and the segmentation accuracies are evaluated for each slice of the tumour image. The method is applied on real data of 80 benign, malignant tumour images. The results are compared with the radiologist labelled ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and the overlap similarity measure dice metric. From the analysis and performance measures such as segmentation accuracy and dice metric, it is inferred that better segmentation accuracy and higher dice metric are achieved with the normalized cut segmentation method than with the fuzzy c-means clustering method.

Textural Analysis and Substrate Classification in the Nearshore Region of Lake Superior Using High-Resolution Multibeam Bathymetry

NASA Astrophysics Data System (ADS)

Dennison, Andrew G.

Classification of the seafloor substrate can be done with a variety of methods. These methods include Visual (dives, drop cameras); mechanical (cores, grab samples); acoustic (statistical analysis of echosounder returns). Acoustic methods offer a more powerful and efficient means of collecting useful information about the bottom type. Due to the nature of an acoustic survey, larger areas can be sampled, and by combining the collected data with visual and mechanical survey methods provide greater confidence in the classification of a mapped region. During a multibeam sonar survey, both bathymetric and backscatter data is collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on bottom type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, i.e a muddy area from a rocky area, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing of high-resolution multibeam data can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. The development of a new classification method is described here. It is based upon the analysis of textural features in conjunction with ground truth sampling. The processing and classification result of two geologically distinct areas in nearshore regions of Lake Superior; off the Lester River,MN and Amnicon River, WI are presented here, using the Minnesota Supercomputer Institute's Mesabi computing cluster for initial processing. Processed data is then calibrated using ground truth samples to conduct an accuracy assessment of the surveyed areas. From analysis of high-resolution bathymetry data collected at both survey sites is was possible to successfully calculate a series of measures that describe textural information about the lake floor. Further processing suggests that the features calculated capture a significant amount of statistical information about the lake floor terrain as well. Two sources of error, an anomalous heave and refraction error significantly deteriorated the quality of the processed data and resulting validate results. Ground truth samples used to validate the classification methods utilized for both survey sites, however, resulted in accuracy values ranging from 5 -30 percent at the Amnicon River, and between 60-70 percent for the Lester River. The final results suggest that this new processing methodology does adequately capture textural information about the lake floor and does provide an acceptable classification in the absence of significant data quality issues.

Diagnosis of human prostate carcinoma cancer stem cells enriched from DU145 cell lines changes with microscopic texture analysis in radiation and hyperthermia treatment using run-length matrix.

PubMed

Abbasian Ardakani, Ali; Rajaee, Jila; Khoei, Samideh

2017-11-01

Hyperthermia and radiation have the ability to induce structural and morphological changes on both macroscopic and microscopic level. Normal and damage cells have a different texture but may be perceived by human eye, as having the same texture. To explore the potential of texture analysis based on run-length matrix, a total of 32 sphere images for each group and treatment regime were used in this study. Cells were subjected to the treatment with different doses of 6 MeV electron radiation (0 2, 4 and 6 Gy), hyperthermia (at 43° C in 0, 30, 60 and 90 min) and radiation + hyperthermia (at 43 °C in 30 min with 2, 4 and 6 Gy dose), respectively. Twenty run-length matrix (RLM) features were extracted as descriptors for each selected region of interest for texture analysis. Linear discriminant analysis was employed to transform raw data to lower-dimensional spaces and increase discriminative power. The features were classified by the first nearest neighbor classifier. RLM features represented the best performance with sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of 100% between 0 and 6 Gy radiation, 0 and 6 Gy radiation + hyperthermia, 0 and 90 min and 30 and 90 min hyperthermia groups. The area under receiver operating characteristic curve was 1 for these groups. RLM features have a high potential to characterize cell changes during different treatment regimes.

Computation of reliable textural indices from multimodal brain MRI: suggestions based on a study of patients with diffuse intrinsic pontine glioma

NASA Astrophysics Data System (ADS)

Goya-Outi, Jessica; Orlhac, Fanny; Calmon, Raphael; Alentorn, Agusti; Nioche, Christophe; Philippe, Cathy; Puget, Stéphanie; Boddaert, Nathalie; Buvat, Irène; Grill, Jacques; Frouin, Vincent; Frouin, Frederique

2018-05-01

Few methodological studies regarding widely used textural indices robustness in MRI have been reported. In this context, this study aims to propose some rules to compute reliable textural indices from multimodal 3D brain MRI. Diagnosis and post-biopsy MR scans including T1, post-contrast T1, T2 and FLAIR images from thirty children with diffuse intrinsic pontine glioma (DIPG) were considered. The hybrid white stripe method was adapted to standardize MR intensities. Sixty textural indices were then computed for each modality in different regions of interest (ROI), including tumor and white matter (WM). Three types of intensity binning were compared : constant bin width and relative bounds; constant number of bins and relative bounds; constant number of bins and absolute bounds. The impact of the volume of the region was also tested within the WM. First, the mean Hellinger distance between patient-based intensity distributions decreased by a factor greater than 10 in WM and greater than 2.5 in gray matter after standardization. Regarding the binning strategy, the ranking of patients was highly correlated for 188/240 features when comparing with , but for only 20 when comparing with , and nine when comparing with . Furthermore, when using or texture indices reflected tumor heterogeneity as assessed visually by experts. Last, 41 features presented statistically significant differences between contralateral WM regions when ROI size slightly varies across patients, and none when using ROI of the same size. For regions with similar size, 224 features were significantly different between WM and tumor. Valuable information from texture indices can be biased by methodological choices. Recommendations are to standardize intensities in MR brain volumes, to use intensity binning with constant bin width, and to define regions with the same volumes to get reliable textural indices.

Field-Scale Evaluation of Infiltration Parameters From Soil Texture for Hydrologic Analysis

NASA Astrophysics Data System (ADS)

Springer, Everett P.; Cundy, Terrance W.

1987-02-01

Recent interest in predicting soil hydraulic properties from simple physical properties such as texture has major implications in the parameterization of physically based models of surface runoff. This study was undertaken to (1) compare, on a field scale, soil hydraulic parameters predicted from texture to those derived from field measurements and (2) compare simulated overland flow response using these two parameter sets. The parameters for the Green-Ampt infiltration equation were obtained from field measurements and using texture-based predictors for two agricultural fields, which were mapped as single soil units. Results of the analyses were that (1) the mean and variance of the field-based parameters were not preserved by the texture-based estimates, (2) spatial and cross correlations between parameters were induced by the texture-based estimation procedures, (3) the overland flow simulations using texture-based parameters were significantly different than those from field-based parameters, and (4) simulations using field-measured hydraulic conductivities and texture-based storage parameters were very close to simulations using only field-based parameters.

Idiopathic interstitial pneumonias and emphysema: detection and classification using a texture-discriminative approach

NASA Astrophysics Data System (ADS)

Fetita, C.; Chang-Chien, K. C.; Brillet, P. Y.; Pr"teux, F.; Chang, R. F.

2012-03-01

Our study aims at developing a computer-aided diagnosis (CAD) system for fully automatic detection and classification of pathological lung parenchyma patterns in idiopathic interstitial pneumonias (IIP) and emphysema using multi-detector computed tomography (MDCT). The proposed CAD system is based on three-dimensional (3-D) mathematical morphology, texture and fuzzy logic analysis, and can be divided into four stages: (1) a multi-resolution decomposition scheme based on a 3-D morphological filter was exploited to discriminate the lung region patterns at different analysis scales. (2) An additional spatial lung partitioning based on the lung tissue texture was introduced to reinforce the spatial separation between patterns extracted at the same resolution level in the decomposition pyramid. Then, (3) a hierarchic tree structure was exploited to describe the relationship between patterns at different resolution levels, and for each pattern, six fuzzy membership functions were established for assigning a probability of association with a normal tissue or a pathological target. Finally, (4) a decision step exploiting the fuzzy-logic assignments selects the target class of each lung pattern among the following categories: normal (N), emphysema (EM), fibrosis/honeycombing (FHC), and ground glass (GDG). According to a preliminary evaluation on an extended database, the proposed method can overcome the drawbacks of a previously developed approach and achieve higher sensitivity and specificity.

SU-E-E-16: The Application of Texture Analysis for Differentiation of Central Cancer From Atelectasis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, M; Fan, T; Duan, J

2015-06-15

Purpose: Prospectively assess the potential utility of texture analysis for differentiation of central cancer from atelectasis. Methods: 0 consecutive central lung cancer patients who were referred for CT imaging and PET-CT were enrolled. Radiotherapy doctor delineate the tumor and atelectasis according to the fusion imaging based on CT image and PET-CT image. The texture parameters (such as energy, correlation, sum average, difference average, difference entropy), were obtained respectively to quantitatively discriminate tumor and atelectasis based on gray level co-occurrence matrix (GLCM) Results: The texture analysis results showed that the parameters of correlation and sum average had an obviously statistical significance(P<0.05).more » Conclusion: the results of this study indicate that texture analysis may be useful for the differentiation of central lung cancer and atelectasis.« less

Advanced Ecosystem Mapping Techniques for Large Arctic Study Domains Using Calibrated High-Resolution Imagery

NASA Astrophysics Data System (ADS)

Macander, M. J.; Frost, G. V., Jr.

2015-12-01

Regional-scale mapping of vegetation and other ecosystem properties has traditionally relied on medium-resolution remote sensing such as Landsat (30 m) and MODIS (250 m). Yet, the burgeoning availability of high-resolution (<=2 m) imagery and ongoing advances in computing power and analysis tools raises the prospect of performing ecosystem mapping at fine spatial scales over large study domains. Here we demonstrate cutting-edge mapping approaches over a ~35,000 km² study area on Alaska's North Slope using calibrated and atmospherically-corrected mosaics of high-resolution WorldView-2 and GeoEye-1 imagery: (1) an a priori spectral approach incorporating the Satellite Imagery Automatic Mapper (SIAM) algorithms; (2) image segmentation techniques; and (3) texture metrics. The SIAM spectral approach classifies radiometrically-calibrated imagery to general vegetation density categories and non-vegetated classes. The SIAM classes were developed globally and their applicability in arctic tundra environments has not been previously evaluated. Image segmentation, or object-based image analysis, automatically partitions high-resolution imagery into homogeneous image regions that can then be analyzed based on spectral, textural, and contextual information. We applied eCognition software to delineate waterbodies and vegetation classes, in combination with other techniques. Texture metrics were evaluated to determine the feasibility of using high-resolution imagery to algorithmically characterize periglacial surface forms (e.g., ice-wedge polygons), which are an important physical characteristic of permafrost-dominated regions but which cannot be distinguished by medium-resolution remote sensing. These advanced mapping techniques provide products which can provide essential information supporting a broad range of ecosystem science and land-use planning applications in northern Alaska and elsewhere in the circumpolar Arctic.

Rock classification based on resistivity patterns in electrical borehole wall images

NASA Astrophysics Data System (ADS)

Linek, Margarete; Jungmann, Matthias; Berlage, Thomas; Pechnig, Renate; Clauser, Christoph

2007-06-01

Electrical borehole wall images represent grey-level-coded micro-resistivity measurements at the borehole wall. Different scientific methods have been implemented to transform image data into quantitative log curves. We introduce a pattern recognition technique applying texture analysis, which uses second-order statistics based on studying the occurrence of pixel pairs. We calculate so-called Haralick texture features such as contrast, energy, entropy and homogeneity. The supervised classification method is used for assigning characteristic texture features to different rock classes and assessing the discriminative power of these image features. We use classifiers obtained from training intervals to characterize the entire image data set recovered in ODP hole 1203A. This yields a synthetic lithology profile based on computed texture data. We show that Haralick features accurately classify 89.9% of the training intervals. We obtained misclassification for vesicular basaltic rocks. Hence, further image analysis tools are used to improve the classification reliability. We decompose the 2D image signal by the application of wavelet transformation in order to enhance image objects horizontally, diagonally and vertically. The resulting filtered images are used for further texture analysis. This combined classification based on Haralick features and wavelet transformation improved our classification up to a level of 98%. The application of wavelet transformation increases the consistency between standard logging profiles and texture-derived lithology. Texture analysis of borehole wall images offers the potential to facilitate objective analysis of multiple boreholes with the same lithology.

Significance of MPEG-7 textural features for improved mass detection in mammography.

PubMed

Eltonsy, Nevine H; Tourassi, Georgia D; Fadeev, Aleksey; Elmaghraby, Adel S

2006-01-01

The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to evaluate the performance of each set of features independently and merged into a back-propagation artificial neural network (BPANN) using the leave-one-out sampling scheme (LOOSS). The study was based on a database of 668 mammographic ROIs (340 depicting cancer regions and 328 depicting normal parenchyma). Overall, the ROC area index of the BPANN using the directional morphological features was Az=0.85+/-0.01. The MPEG-7 edge histogram descriptor-based BPNN showed an ROC area index of Az=0.71+/-0.01 while homogeneous textural descriptors using 30 and 120 channels helped the BPNN achieve similar ROC area indexes of Az=0.882+/-0.02 and Az=0.877+/-0.01 respectively. After merging the MPEG-7 homogeneous textural features with the directional neighborhood features the performance of the BPANN increased providing an ROC area index of Az=0.91+/-0.01. MPEG-7 homogeneous textural descriptor significantly improved the morphology-based detection scheme.

Classification of glioblastoma and metastasis for neuropathology intraoperative diagnosis: a multi-resolution textural approach to model the background

NASA Astrophysics Data System (ADS)

Ahmad Fauzi, Mohammad Faizal; Gokozan, Hamza Numan; Elder, Brad; Puduvalli, Vinay K.; Otero, Jose J.; Gurcan, Metin N.

2014-03-01

Brain cancer surgery requires intraoperative consultation by neuropathology to guide surgical decisions regarding the extent to which the tumor undergoes gross total resection. In this context, the differential diagnosis between glioblastoma and metastatic cancer is challenging as the decision must be made during surgery in a short time-frame (typically 30 minutes). We propose a method to classify glioblastoma versus metastatic cancer based on extracting textural features from the non-nuclei region of cytologic preparations. For glioblastoma, these regions of interest are filled with glial processes between the nuclei, which appear as anisotropic thin linear structures. For metastasis, these regions correspond to a more homogeneous appearance, thus suitable texture features can be extracted from these regions to distinguish between the two tissue types. In our work, we use the Discrete Wavelet Frames to characterize the underlying texture due to its multi-resolution capability in modeling underlying texture. The textural characterization is carried out in primarily the non-nuclei regions after nuclei regions are segmented by adapting our visually meaningful decomposition segmentation algorithm to this problem. k-nearest neighbor method was then used to classify the features into glioblastoma or metastasis cancer class. Experiment on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7% for glioblastoma, 87.5% for metastasis and 88.7% overall. Further studies are underway to incorporate nuclei region features into classification on an expanded dataset, as well as expanding the classification to more types of cancers.

Performance analysis of improved methodology for incorporation of spatial/spectral variability in synthetic hyperspectral imagery

NASA Astrophysics Data System (ADS)

Scanlan, Neil W.; Schott, John R.; Brown, Scott D.

2004-01-01

Synthetic imagery has traditionally been used to support sensor design by enabling design engineers to pre-evaluate image products during the design and development stages. Increasingly exploitation analysts are looking to synthetic imagery as a way to develop and test exploitation algorithms before image data are available from new sensors. Even when sensors are available, synthetic imagery can significantly aid in algorithm development by providing a wide range of "ground truthed" images with varying illumination, atmospheric, viewing and scene conditions. One limitation of synthetic data is that the background variability is often too bland. It does not exhibit the spatial and spectral variability present in real data. In this work, four fundamentally different texture modeling algorithms will first be implemented as necessary into the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model environment. Two of the models to be tested are variants of a statistical Z-Score selection model, while the remaining two involve a texture synthesis and a spectral end-member fractional abundance map approach, respectively. A detailed comparative performance analysis of each model will then be carried out on several texturally significant regions of the resultant synthetic hyperspectral imagery. The quantitative assessment of each model will utilize a set of three peformance metrics that have been derived from spatial Gray Level Co-Occurrence Matrix (GLCM) analysis, hyperspectral Signal-to-Clutter Ratio (SCR) measures, and a new concept termed the Spectral Co-Occurrence Matrix (SCM) metric which permits the simultaneous measurement of spatial and spectral texture. Previous research efforts on the validation and performance analysis of texture characterization models have been largely qualitative in nature based on conducting visual inspections of synthetic textures in order to judge the degree of similarity to the original sample texture imagery. The quantitative measures used in this study will in combination attempt to determine which texture characterization models best capture the correct statistical and radiometric attributes of the corresponding real image textures in both the spatial and spectral domains. The motivation for this work is to refine our understanding of the complexities of texture phenomena so that an optimal texture characterization model that can accurately account for these complexities can be eventually implemented into a synthetic image generation (SIG) model. Further, conclusions will be drawn regarding which of the candidate texture models are able to achieve realistic levels of spatial and spectral clutter, thereby permitting more effective and robust testing of hyperspectral algorithms in synthetic imagery.

Performance analysis of improved methodology for incorporation of spatial/spectral variability in synthetic hyperspectral imagery

NASA Astrophysics Data System (ADS)

Scanlan, Neil W.; Schott, John R.; Brown, Scott D.

2003-12-01

Synthetic imagery has traditionally been used to support sensor design by enabling design engineers to pre-evaluate image products during the design and development stages. Increasingly exploitation analysts are looking to synthetic imagery as a way to develop and test exploitation algorithms before image data are available from new sensors. Even when sensors are available, synthetic imagery can significantly aid in algorithm development by providing a wide range of "ground truthed" images with varying illumination, atmospheric, viewing and scene conditions. One limitation of synthetic data is that the background variability is often too bland. It does not exhibit the spatial and spectral variability present in real data. In this work, four fundamentally different texture modeling algorithms will first be implemented as necessary into the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model environment. Two of the models to be tested are variants of a statistical Z-Score selection model, while the remaining two involve a texture synthesis and a spectral end-member fractional abundance map approach, respectively. A detailed comparative performance analysis of each model will then be carried out on several texturally significant regions of the resultant synthetic hyperspectral imagery. The quantitative assessment of each model will utilize a set of three peformance metrics that have been derived from spatial Gray Level Co-Occurrence Matrix (GLCM) analysis, hyperspectral Signal-to-Clutter Ratio (SCR) measures, and a new concept termed the Spectral Co-Occurrence Matrix (SCM) metric which permits the simultaneous measurement of spatial and spectral texture. Previous research efforts on the validation and performance analysis of texture characterization models have been largely qualitative in nature based on conducting visual inspections of synthetic textures in order to judge the degree of similarity to the original sample texture imagery. The quantitative measures used in this study will in combination attempt to determine which texture characterization models best capture the correct statistical and radiometric attributes of the corresponding real image textures in both the spatial and spectral domains. The motivation for this work is to refine our understanding of the complexities of texture phenomena so that an optimal texture characterization model that can accurately account for these complexities can be eventually implemented into a synthetic image generation (SIG) model. Further, conclusions will be drawn regarding which of the candidate texture models are able to achieve realistic levels of spatial and spectral clutter, thereby permitting more effective and robust testing of hyperspectral algorithms in synthetic imagery.

Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction

NASA Astrophysics Data System (ADS)

Yip, Stephen S. F.; Coroller, Thibaud P.; Sanford, Nina N.; Huynh, Elizabeth; Mamon, Harvey; Aerts, Hugo J. W. L.; Berbeco, Ross I.

2016-01-01

Change in PET-based textural features has shown promise in predicting cancer response to treatment. However, contouring tumour volumes on longitudinal scans is time-consuming. This study investigated the usefulness of contour propagation in texture analysis for the purpose of pathologic response prediction in esophageal cancer. Forty-five esophageal cancer patients underwent PET/CT scans before and after chemo-radiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumour ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. PET images were converted into 256 discrete values. Co-occurrence, run-length, and size zone matrix textures were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs from different algorithms were compared using Dice similarity index (DSI). Contours propagated by the fast-demons, fast-free-form and rigid algorithms did not fully capture the high FDG uptake regions of tumours. Fast-demons propagated ROIs had the least agreement with other contours (DSI  =  58%). Moderate to substantial overlap were found in the ROIs propagated by all other algorithms (DSI  =  69%-79%). Rigidly propagated ROIs with co-occurrence texture failed to significantly differentiate between responders and non-responders (AUC  =  0.58, q-value  =  0.33), while the differentiation was significant with other textures (AUC  =  0.71‒0.73, p  <  0.009). Among the deformable algorithms, fast-demons (AUC  =  0.68‒0.70, q-value  <  0.03) and fast-free-form (AUC  =  0.69‒0.74, q-value  <  0.04) were the least predictive. ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC  =  0.72‒0.78, q-value  <  0.01). Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, fast-demons, fast-free-form, and rigid algorithms should be applied with care due to their inferior performance compared to other algorithms.

Accelerating Time-Varying Hardware Volume Rendering Using TSP Trees and Color-Based Error Metrics

NASA Technical Reports Server (NTRS)

Ellsworth, David; Chiang, Ling-Jen; Shen, Han-Wei; Kwak, Dochan (Technical Monitor)

2000-01-01

This paper describes a new hardware volume rendering algorithm for time-varying data. The algorithm uses the Time-Space Partitioning (TSP) tree data structure to identify regions within the data that have spatial or temporal coherence. By using this coherence, the rendering algorithm can improve performance when the volume data is larger than the texture memory capacity by decreasing the amount of textures required. This coherence can also allow improved speed by appropriately rendering flat-shaded polygons instead of textured polygons, and by not rendering transparent regions. To reduce the polygonization overhead caused by the use of the hierarchical data structure, we introduce an optimization method using polygon templates. The paper also introduces new color-based error metrics, which more accurately identify coherent regions compared to the earlier scalar-based metrics. By showing experimental results from runs using different data sets and error metrics, we demonstrate that the new methods give substantial improvements in volume rendering performance.

Objective measurement of bread crumb texture

NASA Astrophysics Data System (ADS)

Wang, Jian; Coles, Graeme D.

1995-01-01

Evaluation of bread crumb texture plays an important role in judging bread quality. This paper discusses the application of image analysis methods to the objective measurement of the visual texture of bread crumb. The application of Fast Fourier Transform and mathematical morphology methods have been discussed by the authors in their previous work, and a commercial bread texture measurement system has been developed. Based on the nature of bread crumb texture, we compare the advantages and disadvantages of the two methods, and a third method based on features derived directly from statistics of edge density in local windows of the bread image. The analysis of various methods and experimental results provides an insight into the characteristics of the bread texture image and interconnection between texture measurement algorithms. The usefulness of the application of general stochastic process modelling of texture is thus revealed; it leads to more reliable and accurate evaluation of bread crumb texture. During the development of these methods, we also gained useful insights into how subjective judges form opinions about bread visual texture. These are discussed here.

Independent Component Analysis of Textures

NASA Technical Reports Server (NTRS)

Manduchi, Roberto; Portilla, Javier

2000-01-01

A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on Independent Components Analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product over the marginals most closely approximates the joint probability density function of the filter outputs. The algorithm is implemented using a steerable filter space. Experiments involving both texture classification and synthesis show that compared to Principal Components Analysis, ICA provides superior performance for modeling of natural and synthetic textures.

Mesozoic clay diagenesis in the Appalachian Plateau

NASA Astrophysics Data System (ADS)

Boles, A.; Mulch, A.; van der Pluijm, B.

2017-12-01

Integrated investigation of authigenic clays in the Appalachian Plateau of the northeastern US Midcontinent using X-ray goniometry, Rietveld-method based illite polytype analysis, and 40Ar/39Ar geochronology yields novel insights about the structural diagenetic history of the North American sedimentary cover sequence. Texture analysis by High Resolution X-ray Texture Goniometry records the presence of a bedding-parallel diagenetic fabric, corresponding to a burial depth of 2-5 km. New development of polytype modeling using BGMN®, a quantitative X-ray powder diffraction forward modeling and whole-pattern matching program matches mineralic characteristic of illite at those depths and reduces uncertainty estimates in age analysis. Based on dating size fractions, the diagenetic age is constrained to 225-250 Ma (Triassic) by four authigenic illite samples, reflecting protracted, regional diagenesis in the area. Preliminary H isotopic analysis points to a surface-derived diagenetic fluid with δD values ranging from -48 to -72‰ (in the range of predicted Pangea meteoric fluid), with a dependence on proximity to the Appalachian Mountains that may reflect a rain shadow effect.

Texturization of as-cut p-type monocrystalline silicon wafer using different wet chemical solutions

NASA Astrophysics Data System (ADS)

Hashmi, Galib; Hasanuzzaman, Muhammad; Basher, Mohammad Khairul; Hoq, Mahbubul; Rahman, Md. Habibur

2018-06-01

Implementing texturization process on the monocrystalline silicon substrate reduces reflection and enhances light absorption of the substrate. Thus texturization is one of the key elements to increase the efficiency of solar cell. Considering as-cut monocrystalline silicon wafer as base substrate, in this work different concentrations of Na2CO3 and NaHCO3 solution, KOH-IPA (isopropyl alcohol) solution and tetramethylammonium hydroxide solution with different time intervals have been investigated for texturization process. Furthermore, saw damage removal process was conducted with 10% NaOH solution, 20 wt% KOH-13.33 wt% IPA solution and HF/nitric/acetic acid solution. The surface morphology of saw damage, saw damage removed surface and textured wafer were observed using optical microscope and field emission scanning electron microscopy. Texturization causes pyramidal micro structures on the surface of (100) oriented monocrystalline silicon wafer. The height of the pyramid on the silicon surface varies from 1.5 to 3.2 µm and the inclined planes of the pyramids are acute angle. Contact angle value indicates that the textured wafer's surface fall in between near-hydrophobic to hydrophobic range. With respect to base material absolute reflectance 1.049-0.75% within 250-800 nm wavelength region, 0.1-0.026% has been achieved within the same wavelength region when textured with 0.76 wt% KOH-4 wt% IPA solution for 20 min. Furthermore, an alternative route of using 1 wt% Na2CO3-0.2 wt% NaHCO3 solution for 50 min has been exploited in the texturization process.

Utility of texture analysis for quantifying hepatic fibrosis on proton density MRI.

PubMed

Yu, HeiShun; Buch, Karen; Li, Baojun; O'Brien, Michael; Soto, Jorge; Jara, Hernan; Anderson, Stephan W

2015-11-01

To evaluate the potential utility of texture analysis of proton density maps for quantifying hepatic fibrosis in a murine model of hepatic fibrosis. Following Institutional Animal Care and Use Committee (IACUC) approval, a dietary model of hepatic fibrosis was used and 15 ex vivo murine liver tissues were examined. All images were acquired using a 30 mm bore 11.7T magnetic resonance imaging (MRI) scanner with a multiecho spin-echo sequence. A texture analysis was employed extracting multiple texture features including histogram-based, gray-level co-occurrence matrix-based (GLCM), gray-level run-length-based features (GLRL), gray level gradient matrix (GLGM), and Laws' features. Texture features were correlated with histopathologic and digital image analysis of hepatic fibrosis. Histogram features demonstrated very weak to moderate correlations (r = -0.29 to 0.51) with hepatic fibrosis. GLCM features correlation and contrast demonstrated moderate-to-strong correlations (r = -0.71 and 0.59, respectively) with hepatic fibrosis. Moderate correlations were seen between hepatic fibrosis and the GLRL feature short run low gray-level emphasis (SRLGE) (r = -0. 51). GLGM features demonstrate very weak to weak correlations with hepatic fibrosis (r = -0.27 to 0.09). Moderate correlations were seen between hepatic fibrosis and Laws' features L6 and L7 (r = 0.58). This study demonstrates the utility of texture analysis applied to proton density MRI in a murine liver fibrosis model and validates the potential utility of texture-based features for the noninvasive, quantitative assessment of hepatic fibrosis. © 2015 Wiley Periodicals, Inc.

Inferring Species Richness and Turnover by Statistical Multiresolution Texture Analysis of Satellite Imagery

PubMed Central

Convertino, Matteo; Mangoubi, Rami S.; Linkov, Igor; Lowry, Nathan C.; Desai, Mukund

2012-01-01

Background The quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover. Methodology/Principal Findings We model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf) model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures' representative pdf's via the Kullback-Leibler divergence (KL). Species turnover, or diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species richness, or diversity, based on the Shannon entropy of pixel intensity.To test our approach, we specifically use the green band of Landsat images for a water conservation area in the Florida Everglades. We validate our predictions against data of species occurrences for a twenty-eight years long period for both wet and dry seasons. Our method correctly predicts 73% of species richness. For species turnover, the newly proposed KL divergence prediction performance is near 100% accurate. This represents a significant improvement over the more conventional Shannon entropy difference, which provides 85% accuracy. Furthermore, we find that changes in soil and water patterns, as measured by fluctuations of the Shannon entropy for the red and blue bands respectively, are positively correlated with changes in vegetation. The fluctuations are smaller in the wet season when compared to the dry season. Conclusions/Significance Texture-based statistical multiresolution image analysis is a promising method for quantifying interseasonal differences and, consequently, the degree to which vegetation, soil, and water patterns vary. The proposed automated method for quantifying species richness and turnover can also provide analysis at higher spatial and temporal resolution than is currently obtainable from expensive monitoring campaigns, thus enabling more prompt, more cost effective inference and decision making support regarding anomalous variations in biodiversity. Additionally, a matrix-based visualization of the statistical multiresolution analysis is presented to facilitate both insight and quick recognition of anomalous data. PMID:23115629

Prostate cancer detection: Fusion of cytological and textural features.

PubMed

Nguyen, Kien; Jain, Anil K; Sabata, Bikash

2011-01-01

A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i) to locate cancer regions in a large digitized tissue biopsy, and (ii) to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue regions. In this paper, we address the first stage by presenting a cancer detection approach for the whole slide tissue image. We propose a novel method to extract a cytological feature, namely the presence of cancer nuclei (nuclei with prominent nucleoli) in the tissue, and apply this feature to detect the cancer regions. Additionally, conventional image texture features which have been widely used in the literature are also considered. The performance comparison among the proposed cytological textural feature combination method, the texture-based method and the cytological feature-based method demonstrates the robustness of the extracted cytological feature. At a false positive rate of 6%, the proposed method is able to achieve a sensitivity of 78% on a dataset including six training images (each of which has approximately 4,000×7,000 pixels) and 1 1 whole-slide test images (each of which has approximately 5,000×23,000 pixels). All images are at 20X magnification.

Prostate cancer detection: Fusion of cytological and textural features

PubMed Central

Nguyen, Kien; Jain, Anil K.; Sabata, Bikash

2011-01-01

A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i) to locate cancer regions in a large digitized tissue biopsy, and (ii) to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue regions. In this paper, we address the first stage by presenting a cancer detection approach for the whole slide tissue image. We propose a novel method to extract a cytological feature, namely the presence of cancer nuclei (nuclei with prominent nucleoli) in the tissue, and apply this feature to detect the cancer regions. Additionally, conventional image texture features which have been widely used in the literature are also considered. The performance comparison among the proposed cytological textural feature combination method, the texture-based method and the cytological feature-based method demonstrates the robustness of the extracted cytological feature. At a false positive rate of 6%, the proposed method is able to achieve a sensitivity of 78% on a dataset including six training images (each of which has approximately 4,000×7,000 pixels) and 1 1 whole-slide test images (each of which has approximately 5,000×23,000 pixels). All images are at 20X magnification. PMID:22811959

Optical differentiation between malignant and benign lymphadenopathy by grey scale texture analysis of endobronchial ultrasound convex probe images.

PubMed

Nguyen, Phan; Bashirzadeh, Farzad; Hundloe, Justin; Salvado, Olivier; Dowson, Nicholas; Ware, Robert; Masters, Ian Brent; Bhatt, Manoj; Kumar, Aravind Ravi; Fielding, David

2012-03-01

Morphologic and sonographic features of endobronchial ultrasound (EBUS) convex probe images are helpful in predicting metastatic lymph nodes. Grey scale texture analysis is a well-established methodology that has been applied to ultrasound images in other fields of medicine. The aim of this study was to determine if this methodology could differentiate between benign and malignant lymphadenopathy of EBUS images. Lymph nodes from digital images of EBUS procedures were manually mapped to obtain a region of interest and were analyzed in a prediction set. The regions of interest were analyzed for the following grey scale texture features in MATLAB (version 7.8.0.347 [R2009a]): mean pixel value, difference between maximal and minimal pixel value, SEM pixel value, entropy, correlation, energy, and homogeneity. Significant grey scale texture features were used to assess a validation set compared with fluoro-D-glucose (FDG)-PET-CT scan findings where available. Fifty-two malignant nodes and 48 benign nodes were in the prediction set. Malignant nodes had a greater difference in the maximal and minimal pixel values, SEM pixel value, entropy, and correlation, and a lower energy (P < .0001 for all values). Fifty-one lymph nodes were in the validation set; 44 of 51 (86.3%) were classified correctly. Eighteen of these lymph nodes also had FDG-PET-CT scan assessment, which correctly classified 14 of 18 nodes (77.8%), compared with grey scale texture analysis, which correctly classified 16 of 18 nodes (88.9%). Grey scale texture analysis of EBUS convex probe images can be used to differentiate malignant and benign lymphadenopathy. Preliminary results are comparable to FDG-PET-CT scan.

Comparison of radiograph-based texture analysis and bone mineral density with three-dimensional microarchitecture of trabecular bone.

PubMed

Ranjanomennahary, P; Ghalila, S Sevestre; Malouche, D; Marchadier, A; Rachidi, M; Benhamou, Cl; Chappard, C

2011-01-01

Hip fracture is a serious health problem and textural methods are being developed to assess bone quality. The authors aimed to perform textural analysis at femur on high-resolution digital radiographs compared to three-dimensional (3D) microarchitecture comparatively to bone mineral density. Sixteen cadaveric femurs were imaged with an x-ray device using a C-MOS sensor. One 17 mm square region of interest (ROI) was selected in the femoral head (FH) and one in the great trochanter (GT). Two-dimensional (2D) textural features from the co-occurrence matrices were extracted. Site-matched measurements of bone mineral density were performed. Inside each ROI, a 16 mm diameter core was extracted. Apparent density (Dapp) and bone volume proportion (BV/TV(Arch)) were measured from a defatted bone core using Archimedes' principle. Microcomputed tomography images of the entire length of the core were obtained (Skyscan 1072) at 19.8 microm of resolution and usual 3D morphometric parameters were computed on the binary volume after calibration from BV/TV(Arch). Then, bone surface/bone volume, trabecular thickness, trabecular separation, and trabecular number were obtained by direct methods without model assumption and the structure model index was calculated. In univariate analysis, the correlation coefficients between 2D textural features and 3D morphological parameters reached 0.83 at the FH and 0.79 at the GT. In multivariate canonical correlation analysis, coefficients of the first component reached 0.95 at the FH and 0.88 at the GT. Digital radiographs, widely available and economically viable, are an alternative method for evaluating bone microarchitectural structure.

Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map.

PubMed

Tan, Yihua; Li, Qingyun; Li, Yansheng; Tian, Jinwen

2015-09-11

This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradient distribution detector is used to obtain a gradient textural saliency map in the favor of the candidate regions. In addition, the final targets will be detected by segmenting the saliency map using CFAR-type algorithm. The real high-resolution airborne SAR image data is used to verify the proposed algorithm. The results demonstrate that this algorithm can detect aircraft targets quickly and accurately, and decrease the false alarm rate.

A neural network detection model of spilled oil based on the texture analysis of SAR image

NASA Astrophysics Data System (ADS)

An, Jubai; Zhu, Lisong

2006-01-01

A Radial Basis Function Neural Network (RBFNN) Model is investigated for the detection of spilled oil based on the texture analysis of SAR imagery. In this paper, to take the advantage of the abundant texture information of SAR imagery, the texture features are extracted by both wavelet transform and the Gray Level Co-occurrence matrix. The RBFNN Model is fed with a vector of these texture features. The RBFNN Model is trained and tested by the sample data set of the feature vectors. Finally, a SAR image is classified by this model. The classification results of a spilled oil SAR image show that the classification accuracy for oil spill is 86.2 by the RBFNN Model using both wavelet texture and gray texture, while the classification accuracy for oil spill is 78.0 by same RBFNN Model using only wavelet texture as the input of this RBFNN model. The model using both wavelet transform and the Gray Level Co-occurrence matrix is more effective than that only using wavelet texture. Furthermore, it keeps the complicated proximity and has a good performance of classification.

Detection of small bowel tumors in capsule endoscopy frames using texture analysis based on the discrete wavelet transform.

PubMed

Barbosa, Daniel J C; Ramos, Jaime; Lima, Carlos S

2008-01-01

Capsule endoscopy is an important tool to diagnose tumor lesions in the small bowel. The capsule endoscopic images possess vital information expressed by color and texture. This paper presents an approach based in the textural analysis of the different color channels, using the wavelet transform to select the bands with the most significant texture information. A new image is then synthesized from the selected wavelet bands, trough the inverse wavelet transform. The features of each image are based on second-order textural information, and they are used in a classification scheme using a multilayer perceptron neural network. The proposed methodology has been applied in real data taken from capsule endoscopic exams and reached 98.7% sensibility and 96.6% specificity. These results support the feasibility of the proposed algorithm.

Skin cancer texture analysis of OCT images based on Haralick, fractal dimension and the complex directional field features

NASA Astrophysics Data System (ADS)

Raupov, Dmitry S.; Myakinin, Oleg O.; Bratchenko, Ivan A.; Kornilin, Dmitry V.; Zakharov, Valery P.; Khramov, Alexander G.

2016-04-01

Optical coherence tomography (OCT) is usually employed for the measurement of tumor topology, which reflects structural changes of a tissue. We investigated the possibility of OCT in detecting changes using a computer texture analysis method based on Haralick texture features, fractal dimension and the complex directional field method from different tissues. These features were used to identify special spatial characteristics, which differ healthy tissue from various skin cancers in cross-section OCT images (B-scans). Speckle reduction is an important pre-processing stage for OCT image processing. In this paper, an interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in OCT images was used. The Haralick texture feature set includes contrast, correlation, energy, and homogeneity evaluated in different directions. A box-counting method is applied to compute fractal dimension of investigated tissues. Additionally, we used the complex directional field calculated by the local gradient methodology to increase of the assessment quality of the diagnosis method. The complex directional field (as well as the "classical" directional field) can help describe an image as set of directions. Considering to a fact that malignant tissue grows anisotropically, some principal grooves may be observed on dermoscopic images, which mean possible existence of principal directions on OCT images. Our results suggest that described texture features may provide useful information to differentiate pathological from healthy patients. The problem of recognition melanoma from nevi is decided in this work due to the big quantity of experimental data (143 OCT-images include tumors as Basal Cell Carcinoma (BCC), Malignant Melanoma (MM) and Nevi). We have sensitivity about 90% and specificity about 85%. Further research is warranted to determine how this approach may be used to select the regions of interest automatically.

CFS-SMO based classification of breast density using multiple texture models.

PubMed

Sharma, Vipul; Singh, Sukhwinder

2014-06-01

It is highly acknowledged in the medical profession that density of breast tissue is a major cause for the growth of breast cancer. Increased breast density was found to be linked with an increased risk of breast cancer growth, as high density makes it difficult for radiologists to see an abnormality which leads to false negative results. Therefore, there is need for the development of highly efficient techniques for breast tissue classification based on density. This paper presents a hybrid scheme for classification of fatty and dense mammograms using correlation-based feature selection (CFS) and sequential minimal optimization (SMO). In this work, texture analysis is done on a region of interest selected from the mammogram. Various texture models have been used to quantify the texture of parenchymal patterns of breast. To reduce the dimensionality and to identify the features which differentiate between breast tissue densities, CFS is used. Finally, classification is performed using SMO. The performance is evaluated using 322 images of mini-MIAS database. Highest accuracy of 96.46% is obtained for two-class problem (fatty and dense) using proposed approach. Performance of selected features by CFS is also evaluated by Naïve Bayes, Multilayer Perceptron, RBF Network, J48 and kNN classifier. The proposed CFS-SMO method outperforms all other classifiers giving a sensitivity of 100%. This makes it suitable to be taken as a second opinion in classifying breast tissue density.

Computation of reliable textural indices from multimodal brain MRI: suggestions based on a study of patients with diffuse intrinsic pontine glioma.

PubMed

Goya-Outi, Jessica; Orlhac, Fanny; Calmon, Raphael; Alentorn, Agusti; Nioche, Christophe; Philippe, Cathy; Puget, Stéphanie; Boddaert, Nathalie; Buvat, Irène; Grill, Jacques; Frouin, Vincent; Frouin, Frederique

2018-05-10

Few methodological studies regarding widely used textural indices robustness in MRI have been reported. In this context, this study aims to propose some rules to compute reliable textural indices from multimodal 3D brain MRI. Diagnosis and post-biopsy MR scans including T1, post-contrast T1, T2 and FLAIR images from thirty children with diffuse intrinsic pontine glioma (DIPG) were considered. The hybrid white stripe method was adapted to standardize MR intensities. Sixty textural indices were then computed for each modality in different regions of interest (ROI), including tumor and white matter (WM). Three types of intensity binning were compared [Formula: see text]: constant bin width and relative bounds; [Formula: see text] constant number of bins and relative bounds; [Formula: see text] constant number of bins and absolute bounds. The impact of the volume of the region was also tested within the WM. First, the mean Hellinger distance between patient-based intensity distributions decreased by a factor greater than 10 in WM and greater than 2.5 in gray matter after standardization. Regarding the binning strategy, the ranking of patients was highly correlated for 188/240 features when comparing [Formula: see text] with [Formula: see text], but for only 20 when comparing [Formula: see text] with [Formula: see text], and nine when comparing [Formula: see text] with [Formula: see text]. Furthermore, when using [Formula: see text] or [Formula: see text] texture indices reflected tumor heterogeneity as assessed visually by experts. Last, 41 features presented statistically significant differences between contralateral WM regions when ROI size slightly varies across patients, and none when using ROI of the same size. For regions with similar size, 224 features were significantly different between WM and tumor. Valuable information from texture indices can be biased by methodological choices. Recommendations are to standardize intensities in MR brain volumes, to use intensity binning with constant bin width, and to define regions with the same volumes to get reliable textural indices.

Computerized multiple image analysis on mammograms: performance improvement of nipple identification for registration of multiple views using texture convergence analyses

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Chan, Heang-Ping; Sahiner, Berkman; Hadjiiski, Lubomir M.; Paramagul, Chintana

2004-05-01

Automated registration of multiple mammograms for CAD depends on accurate nipple identification. We developed two new image analysis techniques based on geometric and texture convergence analyses to improve the performance of our previously developed nipple identification method. A gradient-based algorithm is used to automatically track the breast boundary. The nipple search region along the boundary is then defined by geometric convergence analysis of the breast shape. Three nipple candidates are identified by detecting the changes along the gray level profiles inside and outside the boundary and the changes in the boundary direction. A texture orientation-field analysis method is developed to estimate the fourth nipple candidate based on the convergence of the tissue texture pattern towards the nipple. The final nipple location is determined from the four nipple candidates by a confidence analysis. Our training and test data sets consisted of 419 and 368 randomly selected mammograms, respectively. The nipple location identified on each image by an experienced radiologist was used as the ground truth. For 118 of the training and 70 of the test images, the radiologist could not positively identify the nipple, but provided an estimate of its location. These were referred to as invisible nipple images. In the training data set, 89.37% (269/301) of the visible nipples and 81.36% (96/118) of the invisible nipples could be detected within 1 cm of the truth. In the test data set, 92.28% (275/298) of the visible nipples and 67.14% (47/70) of the invisible nipples were identified within 1 cm of the truth. In comparison, our previous nipple identification method without using the two convergence analysis techniques detected 82.39% (248/301), 77.12% (91/118), 89.93% (268/298) and 54.29% (38/70) of the nipples within 1 cm of the truth for the visible and invisible nipples in the training and test sets, respectively. The results indicate that the nipple on mammograms can be detected accurately. This will be an important step towards automatic multiple image analysis for CAD techniques.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mateos, M-J; Brandan, M-E; Gastelum, A

Purpose: To evaluate the time evolution of texture parameters, based on the gray level co-occurrence matrix (GLCM), in subtracted images of 17 patients (10 malignant and 7 benign) subjected to contrast-enhanced digital mammography (CEDM). The goal is to determine the sensitivity of texture to iodine uptake at the lesion, and its correlation (or lack of) with mean-pixel-value (MPV). Methods: Acquisition of clinical images followed a single-energy CEDM protocol using Rh/Rh/48 kV plus external 0.5 cm Al from a Senographe DS unit. Prior to the iodine-based contrast medium (CM) administration a mask image was acquired; four CM images were obtained 1,more » 2, 3, and 5 minutes after CM injection. Temporal series were obtained by logarithmic subtraction of registered CM minus mask images.Regions of interest (ROI) for the lesion were drawn by a radiologist and the texture was analyzed. GLCM was evaluated at a 3 pixel distance, 0° angle, and 64 gray-levels. Pixels identified as registration errors were excluded from the computation. 17 texture parameters were chosen, classified according to similarity into 7 groups, and analyzed. Results: In all cases the texture parameters within a group have similar dynamic behavior. Two texture groups (associated to cluster and sum mean) show a strong correlation with MPV; their average correlation coefficient (ACC) is r{sup 2}=0.90. Other two groups (contrast, homogeneity) remain constant with time, that is, a low-sensitivity to CM uptake. Three groups (regularity, lacunarity and diagonal moment) are sensitive to CM uptake but less correlated with MPV; their ACC is r{sup 2}=0.78. Conclusion: This analysis has shown that, at least groups associated to regularity, lacunarity and diagonal moment offer dynamical information additional to the mean pixel value due to the presence of CM at the lesion. The next step will be the analysis in terms of the lesion pathology. Authors thank PAPIIT-IN105813 for support. Consejo Nacional de Ciencia Y Tecnologia, PAPIIT-IN105813.« less

Effect of slice thickness on brain magnetic resonance image texture analysis

PubMed Central

2010-01-01

Background The accuracy of texture analysis in clinical evaluation of magnetic resonance images depends considerably on imaging arrangements and various image quality parameters. In this paper, we study the effect of slice thickness on brain tissue texture analysis using a statistical approach and classification of T1-weighted images of clinically confirmed multiple sclerosis patients. Methods We averaged the intensities of three consecutive 1-mm slices to simulate 3-mm slices. Two hundred sixty-four texture parameters were calculated for both the original and the averaged slices. Wilcoxon's signed ranks test was used to find differences between the regions of interest representing white matter and multiple sclerosis plaques. Linear and nonlinear discriminant analyses were applied with several separate training and test sets to determine the actual classification accuracy. Results Only moderate differences in distributions of the texture parameter value for 1-mm and simulated 3-mm-thick slices were found. Our study also showed that white matter areas are well separable from multiple sclerosis plaques even if the slice thickness differs between training and test sets. Conclusions Three-millimeter-thick magnetic resonance image slices acquired with a 1.5 T clinical magnetic resonance scanner seem to be sufficient for texture analysis of multiple sclerosis plaques and white matter tissue. PMID:20955567

An efficient cloud detection method for high resolution remote sensing panchromatic imagery

NASA Astrophysics Data System (ADS)

Li, Chaowei; Lin, Zaiping; Deng, Xinpu

2018-04-01

In order to increase the accuracy of cloud detection for remote sensing satellite imagery, we propose an efficient cloud detection method for remote sensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.

Predicting Future Morphological Changes of Lesions from Radiotracer Uptake in 18F-FDG-PET Images

PubMed Central

Bagci, Ulas; Yao, Jianhua; Miller-Jaster, Kirsten; Chen, Xinjian; Mollura, Daniel J.

2013-01-01

We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on 18F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 18F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75±1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUVmax (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUVmax. We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, p<2e-16). PMID:23431398

Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map

PubMed Central

Tan, Yihua; Li, Qingyun; Li, Yansheng; Tian, Jinwen

2015-01-01

This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradient distribution detector is used to obtain a gradient textural saliency map in the favor of the candidate regions. In addition, the final targets will be detected by segmenting the saliency map using CFAR-type algorithm. The real high-resolution airborne SAR image data is used to verify the proposed algorithm. The results demonstrate that this algorithm can detect aircraft targets quickly and accurately, and decrease the false alarm rate. PMID:26378543

Texture analysis of medical images for radiotherapy applications

PubMed Central

Rizzo, Giovanna

2017-01-01

The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique. PMID:27885836

Comparison between layers stacks of 67P/CG comet and spectrophotometric variability obtained from OSIRIS data

NASA Astrophysics Data System (ADS)

Ferrari, S.; Penasa, L.; La Forgia, F.; Massironi, M.; Naletto, G.; Lazzarin, M.; Fornasier, S.; Barucci, M. A.; Lucchetti, A.; Pajola, M.; Frattin, E.; Bertini, I.; Ferri, F.; Cremonese, G.

2017-09-01

The Rosetta/OSIRIS cameras unveiled the layered nature of comet 67P/Churyumov-Gerasimenko, suggesting that the comet bilobate shape results from the low-velocity merging of two independent onion-like objects. Several physiographical regions of the southern-hemisphere big lobe show stacks of layers forming high scarps, terraces and mesas. A spectrophotometric analysis of OSIRIS images based on multispectral data classifications was conducted in order to identify possible morphological, textural and/or compositional characters that allow to distinguish regional stacks of layers.

Computerized Liquid Crystal Phase Identification by Neural Networks Analysis of Polarizing Microscopy Textures

NASA Astrophysics Data System (ADS)

Karaszi, Zoltan; Konya, Andrew; Dragan, Feodor; Jakli, Antal; CPIP/LCI; CS Dept. of Kent State University Collaboration

Polarizing optical microscopy (POM) is traditionally the best-established method of studying liquid crystals, and using POM started already with Otto Lehman in 1890. An expert, who is familiar with the science of optics of anisotropic materials and typical textures of liquid crystals, can identify phases with relatively large confidence. However, for unambiguous identification usually other expensive and time-consuming experiments are needed. Replacement of the subjective and qualitative human eye-based liquid crystal texture analysis with quantitative computerized image analysis technique started only recently and were used to enhance the detection of smooth phase transitions, determine order parameter and birefringence of specific liquid crystal phases. We investigate if the computer can recognize and name the phase where the texture was taken. To judge the potential of reliable image recognition based on this procedure, we used 871 images of liquid crystal textures belonging to five main categories: Nematic, Smectic A, Smectic C, Cholesteric and Crystal, and used a Neural Network Clustering Technique included in the data mining software package in Java ``WEKA''. A neural network trained on a set of 827 LC textures classified the remaining 44 textures with 80% accuracy.

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards

PubMed Central

Nyflot, Matthew J.; Yang, Fei; Byrd, Darrin; Bowen, Stephen R.; Sandison, George A.; Kinahan, Paul E.

2015-01-01

Abstract. Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes. PMID:26251842

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards.

PubMed

Nyflot, Matthew J; Yang, Fei; Byrd, Darrin; Bowen, Stephen R; Sandison, George A; Kinahan, Paul E

2015-10-01

Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes.

Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer.

PubMed

Tixier, Florent; Le Rest, Catherine Cheze; Hatt, Mathieu; Albarghach, Nidal; Pradier, Olivier; Metges, Jean-Philippe; Corcos, Laurent; Visvikis, Dimitris

2011-03-01

(18)F-FDG PET is often used in clinical routine for diagnosis, staging, and response to therapy assessment or prediction. The standardized uptake value (SUV) in the primary or regional area is the most common quantitative measurement derived from PET images used for those purposes. The aim of this study was to propose and evaluate new parameters obtained by textural analysis of baseline PET scans for the prediction of therapy response in esophageal cancer. Forty-one patients with newly diagnosed esophageal cancer treated with combined radiochemotherapy were included in this study. All patients underwent pretreatment whole-body (18)F-FDG PET. Patients were treated with radiotherapy and alkylatinlike agents (5-fluorouracil-cisplatin or 5-fluorouracil-carboplatin). Patients were classified as nonresponders (progressive or stable disease), partial responders, or complete responders according to the Response Evaluation Criteria in Solid Tumors. Different image-derived indices obtained from the pretreatment PET tumor images were considered. These included usual indices such as maximum SUV, peak SUV, and mean SUV and a total of 38 features (such as entropy, size, and magnitude of local and global heterogeneous and homogeneous tumor regions) extracted from the 5 different textures considered. The capacity of each parameter to classify patients with respect to response to therapy was assessed using the Kruskal-Wallis test (P < 0.05). Specificity and sensitivity (including 95% confidence intervals) for each of the studied parameters were derived using receiver-operating-characteristic curves. Relationships between pairs of voxels, characterizing local tumor metabolic nonuniformities, were able to significantly differentiate all 3 patient groups (P < 0.0006). Regional measures of tumor characteristics, such as size of nonuniform metabolic regions and corresponding intensity nonuniformities within these regions, were also significant factors for prediction of response to therapy (P = 0.0002). Receiver-operating-characteristic curve analysis showed that tumor textural analysis can provide nonresponder, partial-responder, and complete-responder patient identification with higher sensitivity (76%-92%) than any SUV measurement. Textural features of tumor metabolic distribution extracted from baseline (18)F-FDG PET images allow for the best stratification of esophageal carcinoma patients in the context of therapy-response prediction.

Evolution of fabric in Chitradurga granite (south India) - A study based on microstructure, anisotropy of magnetic susceptibility (AMS) and vorticity analysis

NASA Astrophysics Data System (ADS)

Mondal, Tridib Kumar

2018-01-01

In this paper, the fabric in massive granite ( 2.6 Ga) from the Chitradurga region (Western Dharwar Craton, south India) is analyzed using microstructure, anisotropy of magnetic susceptibility (AMS) study and kinematic vorticity analysis. The microstructural investigation on the granite shows a progressive textural overprint from magmatic, through high-T to low-T solid-state deformation textures. The mean magnetic foliation in the rocks of the region is dominantly NW-SE striking which have developed during regional D1/D2 deformation on account of NE-SW shortening. The plunge of the magnetic lineation varies from NW to vertical to SE, and interpreted to be a consequence of regional D3 deformation on account of NW-SE to E-W shortening. The vorticity analysis from magnetic fabric in the region reveals that the NW-SE oriented fabric formed under pure shear condition during D1/D2 regional deformation. However, some parts of the region particularly close to the adjacent Chitradurga Shear Zone show that the magnetic fabrics are oblique to the foliation as well as shear zone orientation and inferred to be controlled by simple shearing during D3 regional deformation. The shape preferred orientation (SPO) analysis from oriented thin sections suggest that the shape of the recrystallized quartz grains define the magnetic fabric in Chitradurga granite and the degree of the SPO reduces away from the Chitradurga Shear Zone. It is interpreted that the change in magnetic fabrics in some parts of the granite in the region are dominantly controlled by the late stage sinistral shearing which occurred during the development of Chitradurga Shear Zone. Anisotropy of magnetic susceptibility (AMS) data of granite from the Chitradurga region (West Dharwar Craton, southern India). Km = Mean susceptibility; Pj = corrected degree of magnetic anisotropy; T = shape parameter. K1 and K3 are the maximum and minimum principal axes of the AMS ellipsoid, respectively. dec = Declination; inc = Inclination.

Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs

PubMed Central

Abdullah, Bassem A; Younis, Akmal A; John, Nigel M

2012-01-01

In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI. PMID:22741026

SU-F-R-40: Robustness Test of Computed Tomography Textures of Lung Tissues to Varying Scanning Protocols Using a Realistic Phantom Environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, S; Markel, D; Hegyi, G

2016-06-15

Purpose: The reliability of computed tomography (CT) textures is an important element of radiomics analysis. This study investigates the dependency of lung CT textures on different breathing phases and changes in CT image acquisition protocols in a realistic phantom setting. Methods: We investigated 11 CT texture features for radiation-induced lung disease from 3 categories (first-order, grey level co-ocurrence matrix (GLCM), and Law’s filter). A biomechanical swine lung phantom was scanned at two breathing phases (inhale/exhale) and two scanning protocols set for PET/CT and diagnostic CT scanning. Lung volumes acquired from the CT images were divided into 2-dimensional sub-regions with amore » grid spacing of 31 mm. The distribution of the evaluated texture features from these sub-regions were compared between the two scanning protocols and two breathing phases. The significance of each factor on feature values were tested at 95% significance level using analysis of covariance (ANCOVA) model with interaction terms included. Robustness of a feature to a scanning factor was defined as non-significant dependence on the factor. Results: Three GLCM textures (variance, sum entropy, difference entropy) were robust to breathing changes. Two GLCM (variance, sum entropy) and 3 Law’s filter textures (S5L5, E5L5, W5L5) were robust to scanner changes. Moreover, the two GLCM textures (variance, sum entropy) were consistent across all 4 scanning conditions. First-order features, especially Hounsfield unit intensity features, presented the most drastic variation up to 39%. Conclusion: Amongst the studied features, GLCM and Law’s filter texture features were more robust than first-order features. However, the majority of the features were modified by either breathing phase or scanner changes, suggesting a need for calibration when retrospectively comparing scans obtained at different conditions. Further investigation is necessary to identify the sensitivity of individual image acquisition parameters.« less

Fiber vs Rolling Texture: Stress State Dependence for Cold-Drawn Wire

NASA Astrophysics Data System (ADS)

Zorina, M. A.; Karabanalov, M. S.; Stepanov, S. I.; Demakov, S. L.; Loginov, Yu. N.; Lobanov, M. L.

2018-02-01

The texture of the cold-drawn copper wire was investigated along the radius using electron backscatter diffraction. The complex fiber texture of the central region of the wire was considered as the rolling texture consisting of a set of preferred orientations. The texture of the periphery region was revealed to be similar to the shear texture. The orientation-dependent properties of the wire were proven to be determined by the texture of the near-surface layers.

X-ray texture analysis of paper coating pigments and the correlation with chemical composition analysis

NASA Astrophysics Data System (ADS)

Roine, J.; Tenho, M.; Murtomaa, M.; Lehto, V.-P.; Kansanaho, R.

2007-10-01

The present research experiments the applicability of x-ray texture analysis in investigating the properties of paper coatings. The preferred orientations of kaolin, talc, ground calcium carbonate, and precipitated calcium carbonate particles used in four different paper coatings were determined qualitatively based on the measured crystal orientation data. The extent of the orientation, namely, the degree of the texture of each pigment, was characterized quantitatively using a single parameter. As a result, the effect of paper calendering is clearly seen as an increase on the degree of texture of the coating pigments. The effect of calendering on the preferred orientation of kaolin was also evident in an independent energy dispersive spectrometer analysis on micrometer scale and an electron spectroscopy for chemical analysis on nanometer scale. Thus, the present work proves x-ray texture analysis to be a potential research tool for characterizing the properties of paper coating layers.

Hydrological inferences through morphometric analysis of lower Kosi river basin of India for water resource management based on remote sensing data

NASA Astrophysics Data System (ADS)

Rai, Praveen Kumar; Chandel, Rajeev Singh; Mishra, Varun Narayan; Singh, Prafull

2018-03-01

Satellite based remote sensing technology has proven to be an effectual tool in analysis of drainage networks, study of surface morphological features and their correlation with groundwater management prospect at basin level. The present study highlights the effectiveness and advantage of remote sensing and GIS-based analysis for quantitative and qualitative assessment of flood plain region of lower Kosi river basin based on morphometric analysis. In this study, ASTER DEM is used to extract the vital hydrological parameters of lower Kosi river basin in ARC GIS software. Morphometric parameters, e.g., stream order, stream length, bifurcation ratio, drainage density, drainage frequency, drainage texture, form factor, circularity ratio, elongation ratio, etc., have been calculated for the Kosi basin and their hydrological inferences were discussed. Most of the morphometric parameters such as bifurcation ratio, drainage density, drainage frequency, drainage texture concluded that basin has good prospect for water management program for various purposes and also generated data base that can provide scientific information for site selection of water-harvesting structures and flood management activities in the basin. Land use land cover (LULC) of the basin were also prepared from Landsat data of 2005, 2010 and 2015 to assess the change in dynamic of the basin and these layers are very noteworthy for further watershed prioritization.

A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping

PubMed Central

Chuang, Yung-Chung Matt; Shiu, Yi-Shiang

2016-01-01

Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply. PMID:27128915

A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping.

PubMed

Chuang, Yung-Chung Matt; Shiu, Yi-Shiang

2016-04-26

Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply.

A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

NASA Astrophysics Data System (ADS)

Vallières, M.; Freeman, C. R.; Skamene, S. R.; El Naqa, I.

2015-07-01

This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS

PubMed Central

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data. PMID:26090852

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS.

PubMed

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds

PubMed Central

Zhang, Xiaolei; Liu, Fei; He, Yong; Li, Xiaoli

2012-01-01

Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380–1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds. PMID:23235456

Early classification of Alzheimer's disease using hippocampal texture from structural MRI

NASA Astrophysics Data System (ADS)

Zhao, Kun; Ding, Yanhui; Wang, Pan; Dou, Xuejiao; Zhou, Bo; Yao, Hongxiang; An, Ningyu; Zhang, Yongxin; Zhang, Xi; Liu, Yong

2017-03-01

Convergent evidence has been collected to support that Alzheimer's disease (AD) is associated with reduction in hippocampal volume based on anatomical magnetic resonance imaging (MRI) and impaired functional connectivity based on functional MRI. Radiomics texture analysis has been previously successfully used to identify MRI biomarkers of several diseases, including AD, mild cognitive impairment and multiple sclerosis. In this study, our goal was to determine if MRI hippocampal textures, including the intensity, shape, texture and wavelet features, could be served as an MRI biomarker of AD. For this purpose, the texture marker was trained and evaluated from MRI data of 48 AD and 39 normal samples. The result highlights the presence of hippocampal texture abnormalities in AD, and the possibility that texture may serve as a neuroimaging biomarker for AD.

Cloud cover determination in polar regions from satellite imagery

NASA Technical Reports Server (NTRS)

Barry, R. G.; Key, J.

1989-01-01

The objectives are to develop a suitable validation data set for evaluating the effectiveness of the International Satellite Cloud Climatology Project (ISCCP) algorithm for cloud retrieval in polar regions, to identify limitations of current procedures and to explore potential means to remedy them using textural classifiers, and to compare synoptic cloud data from model runs with observations. Toward the first goal, a polar data set consisting of visible, thermal, and passive microwave data was developed. The AVHRR and SMMR data were digitally merged to a polar stereographic projection with an effective pixel size of 5 sq km. With this data set, two unconventional methods of classifying the imagery for the analysis of polar clouds and surfaces were examined: one based on fuzzy sets theory and another based on a trained neural network. An algorithm for cloud detection was developed from an early test version of the ISCCP algorithm. This algorithm includes the identification of surface types with passive microwave, then temporal tests at each pixel location in the cloud detection phase. Cloud maps and clear sky radiance composites for 5 day periods are produced. Algorithm testing and validation was done with both actural AVHRR/SMMR data, and simulated imagery. From this point in the algorithm, groups of cloud pixels are examined for their spectral and textural characteristics, and a procedure is developed for the analysis of cloud patterns utilizing albedo, IR temperature, and texture. In a completion of earlier work, empirical analyses of arctic cloud cover were explored through manual interpretations of DMSP imagery and compared to U.S. Air Force 3D-nephanalysis. Comparisons of observed cloudiness from existing climatologies to patterns computed by the GISS climate model were also made.

Structure-Property Correlations in Al-Li Alloy Integrally Stiffened Extrusions

NASA Technical Reports Server (NTRS)

Hales, Stephen J.; Hafley, Robert A.

2001-01-01

The objective of this investigation was to establish the relationship between mechanical property anisotropy, microstructure and crystallographic texture in integrally 'T'-stiffened extruded panels fabricated from the Al-Li alloys 2195, 2098 and 2096. In-plane properties were measured as a function of orientation at two locations in the panels, namely mid-way between (Skin), and directly beneath (Base), the integral 'T' stiffeners. The 2195 extrusion exhibited the best combination of strength and toughness, but was the most anisotropic. The 2098 extrusion exhibited lower strength and comparable toughness, but was more isotropic than 2195. The 2096 extrusion exhibited the lowest strength and poor toughness, but was the most isotropic. All three alloys exhibited highly elongated grain structures and similar location-dependent variations in grain morphology. The textural characteristics comprised a beta + <100> fiber texture, similar to rolled product, in the Skin regions and alpha <111> + <100> fiber texture, comparable to axisymmetric extruded product, in the Base regions. In an attempt to quantitatively correlate texture with yield strength anisotropy, the original 'full constraint' Taylor model and a variant of the 'relaxed constraint' model, explored by Wert et al., were applied to the data. A comparison of the results revealed that the Wert model was consistently more accurate than the Taylor model.

Texture analysis of high-resolution FLAIR images for TLE

NASA Astrophysics Data System (ADS)

Jafari-Khouzani, Kourosh; Soltanian-Zadeh, Hamid; Elisevich, Kost

2005-04-01

This paper presents a study of the texture information of high-resolution FLAIR images of the brain with the aim of determining the abnormality and consequently the candidacy of the hippocampus for temporal lobe epilepsy (TLE) surgery. Intensity and volume features of the hippocampus from FLAIR images of the brain have been previously shown to be useful in detecting the abnormal hippocampus in TLE. However, the small size of the hippocampus may limit the texture information. High-resolution FLAIR images show more details of the abnormal intensity variations of the hippocampi and therefore are more suitable for texture analysis. We study and compare the low and high-resolution FLAIR images of six epileptic patients. The hippocampi are segmented manually by an expert from T1-weighted MR images. Then the segmented regions are mapped on the corresponding FLAIR images for texture analysis. The 2-D wavelet transforms of the hippocampi are employed for feature extraction. We compare the ability of the texture features from regular and high-resolution FLAIR images to distinguish normal and abnormal hippocampi. Intracranial EEG results as well as surgery outcome are used as gold standard. The results show that the intensity variations of the hippocampus are related to the abnormalities in the TLE.

Clustering document fragments using background color and texture information

NASA Astrophysics Data System (ADS)

Chanda, Sukalpa; Franke, Katrin; Pal, Umapada

2012-01-01

Forensic analysis of questioned documents sometimes can be extensively data intensive. A forensic expert might need to analyze a heap of document fragments and in such cases to ensure reliability he/she should focus only on relevant evidences hidden in those document fragments. Relevant document retrieval needs finding of similar document fragments. One notion of obtaining such similar documents could be by using document fragment's physical characteristics like color, texture, etc. In this article we propose an automatic scheme to retrieve similar document fragments based on visual appearance of document paper and texture. Multispectral color characteristics using biologically inspired color differentiation techniques are implemented here. This is done by projecting document color characteristics to Lab color space. Gabor filter-based texture analysis is used to identify document texture. It is desired that document fragments from same source will have similar color and texture. For clustering similar document fragments of our test dataset we use a Self Organizing Map (SOM) of dimension 5×5, where the document color and texture information are used as features. We obtained an encouraging accuracy of 97.17% from 1063 test images.

Wavelets analysis for differentiating solid, non-macroscopic fat containing, enhancing renal masses: a pilot study

NASA Astrophysics Data System (ADS)

Varghese, Bino; Hwang, Darryl; Mohamed, Passant; Cen, Steven; Deng, Christopher; Chang, Michael; Duddalwar, Vinay

2017-11-01

Purpose: To evaluate potential use of wavelets analysis in discriminating benign and malignant renal masses (RM) Materials and Methods: Regions of interest of the whole lesion were manually segmented and co-registered from multiphase CT acquisitions of 144 patients (98 malignant RM: renal cell carcinoma (RCC) and 46 benign RM: oncocytoma, lipid-poor angiomyolipoma). Here, the Haar wavelet was used to analyze the grayscale images of the largest segmented tumor in the axial direction. Six metrics (energy, entropy, homogeneity, contrast, standard deviation (SD) and variance) derived from 3-levels of image decomposition in 3 directions (horizontal, vertical and diagonal) respectively, were used to quantify tumor texture. Independent t-test or Wilcoxon rank sum test depending on data normality were used as exploratory univariate analysis. Stepwise logistic regression and receiver operator characteristics (ROC) curve analysis were used to select predictors and assess prediction accuracy, respectively. Results: Consistently, 5 out of 6 wavelet-based texture measures (except homogeneity) were higher for malignant tumors compared to benign, when accounting for individual texture direction. Homogeneity was consistently lower in malignant than benign tumors irrespective of direction. SD and variance measured in the diagonal direction on the corticomedullary phase showed significant (p<0.05) difference between benign versus malignant tumors. The multivariate model with variance (3 directions) and SD (vertical direction) extracted from the excretory and pre-contrast phase, respectively showed an area under the ROC curve (AUC) of 0.78 (p < 0.05) in discriminating malignant from benign. Conclusion: Wavelet analysis is a valuable texture evaluation tool to add to a radiomics platforms geared at reliably characterizing and stratifying renal masses.

Accuracy and Precision of Silicon Based Impression Media for Quantitative Areal Texture Analysis

PubMed Central

Goodall, Robert H.; Darras, Laurent P.; Purnell, Mark A.

2015-01-01

Areal surface texture analysis is becoming widespread across a diverse range of applications, from engineering to ecology. In many studies silicon based impression media are used to replicate surfaces, and the fidelity of replication defines the quality of data collected. However, while different investigators have used different impression media, the fidelity of surface replication has not been subjected to quantitative analysis based on areal texture data. Here we present the results of an analysis of the accuracy and precision with which different silicon based impression media of varying composition and viscosity replicate rough and smooth surfaces. Both accuracy and precision vary greatly between different media. High viscosity media tested show very low accuracy and precision, and most other compounds showed either the same pattern, or low accuracy and high precision, or low precision and high accuracy. Of the media tested, mid viscosity President Jet Regular Body and low viscosity President Jet Light Body (Coltène Whaledent) are the only compounds to show high levels of accuracy and precision on both surface types. Our results show that data acquired from different impression media are not comparable, supporting calls for greater standardisation of methods in areal texture analysis. PMID:25991505

Wood texture classification by fuzzy neural networks

NASA Astrophysics Data System (ADS)

Gonzaga, Adilson; de Franca, Celso A.; Frere, Annie F.

1999-03-01

The majority of scientific papers focusing on wood classification for pencil manufacturing take into account defects and visual appearance. Traditional methodologies are base don texture analysis by co-occurrence matrix, by image modeling, or by tonal measures over the plate surface. In this work, we propose to classify plates of wood without biological defects like insect holes, nodes, and cracks, by analyzing their texture. By this methodology we divide the plate image in several rectangular windows or local areas and reduce the number of gray levels. From each local area, we compute the histogram of difference sand extract texture features, given them as input to a Local Neuro-Fuzzy Network. Those features are from the histogram of differences instead of the image pixels due to their better performance and illumination independence. Among several features like media, contrast, second moment, entropy, and IDN, the last three ones have showed better results for network training. Each LNN output is taken as input to a Partial Neuro-Fuzzy Network (PNFN) classifying a pencil region on the plate. At last, the outputs from the PNFN are taken as input to a Global Fuzzy Logic doing the plate classification. Each pencil classification within the plate is done taking into account each quality index.

Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images.

PubMed

Xu, Yingying; Lin, Lanfen; Hu, Hongjie; Wang, Dan; Zhu, Wenchao; Wang, Jian; Han, Xian-Hua; Chen, Yen-Wei

2018-01-01

The bag of visual words (BoVW) model is a powerful tool for feature representation that can integrate various handcrafted features like intensity, texture, and spatial information. In this paper, we propose a novel BoVW-based method that incorporates texture and spatial information for the content-based image retrieval to assist radiologists in clinical diagnosis. This paper presents a texture-specific BoVW method to represent focal liver lesions (FLLs). Pixels in the region of interest (ROI) are classified into nine texture categories using the rotation-invariant uniform local binary pattern method. The BoVW-based features are calculated for each texture category. In addition, a spatial cone matching (SCM)-based representation strategy is proposed to describe the spatial information of the visual words in the ROI. In a pilot study, eight radiologists with different clinical experience performed diagnoses for 20 cases with and without the top six retrieved results. A total of 132 multiphase computed tomography volumes including five pathological types were collected. The texture-specific BoVW was compared to other BoVW-based methods using the constructed dataset of FLLs. The results show that our proposed model outperforms the other three BoVW methods in discriminating different lesions. The SCM method, which adds spatial information to the orderless BoVW model, impacted the retrieval performance. In the pilot trial, the average diagnosis accuracy of the radiologists was improved from 66 to 80% using the retrieval system. The preliminary results indicate that the texture-specific features and the SCM-based BoVW features can effectively characterize various liver lesions. The retrieval system has the potential to improve the diagnostic accuracy and the confidence of the radiologists.

Investigation of magnetic resonance imaging texture analysis as an aid tool for characterization of refractory epilepsies.

PubMed

Baldissin, Maurício Martins; Souza, Edna Marina de

2013-12-01

Refractory epilepsies are syndromes for which therapies that employ two or more antiepileptic drugs, separately or in association, do not result in control of crisis. Patients may present focal cortical dysplasia or diffuse dysplasia and/or hippocampal atrophic alterations that may not be detectable by a simple visual analysis in magnetic resonance imaging. The aim of this study was to evaluate MRI texture in regions of interest located in the hippocampi, limbic association cortex and prefrontal cortex of 20 patients with refractory epilepsy and to compare them with the same areas in 20 healthy individuals, in order to find out if the texture parameters could be related to the presence of the disease. Of the 11 texture parameters calculated, three indicated the existence of statistically significant differences between the studied groups. Such findings suggest the possibility of this technique contributing to studies of refractory epilepsies.

Texture analysis of aeromagnetic data for enhancing geologic features using co-occurrence matrices in Elallaqi area, South Eastern Desert of Egypt

NASA Astrophysics Data System (ADS)

Eldosouky, Ahmed M.; Elkhateeb, Sayed O.

2018-06-01

Enhancement of aeromagnetic data for qualitative purposes depends on the variations of texture and amplitude to outline various geologic features within the data. The texture of aeromagnetic data consists continuity of adjacent anomalies, size, and pattern. Variations in geology, or particularly rock magnetization, in a study area cause fluctuations in texture. In the present study, the anomalous features of Elallaqi area were extracted from aeromagnetic data. In order to delineate textures from the aeromagnetic data, the Red, Green, and Blue Co-occurrence Matrices (RGBCM) were applied to the reduced to the pole (RTP) grid of Elallaqi district in the South Eastern Desert of Egypt. The RGBCM are fashioned of sets of spatial analytical parameters that transform magnetic data into texture forms. Six texture features (parameters), i.e. Correlation, Contrast, Entropy, Homogeneity, Second Moment, and Variance, of RGB Co-occurrence Matrices (RGBCM) are used for analyzing the texture of the RTP grid in this study. These six RGBCM texture characteristics were mixed into a single image using principal component analysis. The calculated texture images present geologic characteristics and structures with much greater sidelong resolution than the original RTP grid. The estimated texture images enabled us to distinguish multiple geologic regions and structures within Elallaqi area including geologic terranes, lithologic boundaries, cracks, and faults. The faults of RGBCM maps were more represented than those of magnetic derivatives providing enhancement of the fine structures of Elallaqi area like the NE direction which scattered WNW metavolcanics and metasediments trending in the northwestern division of Elallaqi area.

Textural Maturity Analysis and Sedimentary Environment Discrimination Based on Grain Shape Data

NASA Astrophysics Data System (ADS)

Tunwal, M.; Mulchrone, K. F.; Meere, P. A.

2017-12-01

Morphological analysis of clastic sedimentary grains is an important source of information regarding the processes involved in their formation, transportation and deposition. However, a standardised approach for quantitative grain shape analysis is generally lacking. In this contribution we report on a study where fully automated image analysis techniques were applied to loose sediment samples collected from glacial, aeolian, beach and fluvial environments. A range of shape parameters are evaluated for their usefulness in textural characterisation of populations of grains. The utility of grain shape data in ranking textural maturity of samples within a given sedimentary environment is evaluated. Furthermore, discrimination of sedimentary environment on the basis of grain shape information is explored. The data gathered demonstrates a clear progression in textural maturity in terms of roundness, angularity, irregularity, fractal dimension, convexity, solidity and rectangularity. Textural maturity can be readily categorised using automated grain shape parameter analysis. However, absolute discrimination between different depositional environments on the basis of shape parameters alone is less certain. For example, the aeolian environment is quite distinct whereas fluvial, glacial and beach samples are inherently variable and tend to overlap each other in terms of textural maturity. This is most likely due to a collection of similar processes and sources operating within these environments. This study strongly demonstrates the merit of quantitative population-based shape parameter analysis of texture and indicates that it can play a key role in characterising both loose and consolidated sediments. This project is funded by the Irish Petroleum Infrastructure Programme (www.pip.ie)

Texture analysis of T1-w and T2-w MR images allows a quantitative evaluation of radiation-induced changes of internal obturator muscles after radiotherapy for prostate cancer.

PubMed

Scalco, Elisa; Rancati, Tiziana; Pirovano, Ileana; Mastropietro, Alfonso; Palorini, Federica; Cicchetti, Alessandro; Messina, Antonella; Avuzzi, Barbara; Valdagni, Riccardo; Rizzo, Giovanna

2018-04-01

To investigate the potential of texture analysis applied on T2-w and postcontrast T1-w images acquired before radiotherapy for prostate cancer (PCa) and 12 months after its completion in quantitatively characterizing local radiation effect on the muscular component of internal obturators, as organs potentially involved in urinary toxicity. T2-w and postcontrast T1-w MR images were acquired at 1.5 T before treatment (MRI1) and at 12 months of follow-up (MRI2) in 13 patients treated with radiotherapy for PCa. Right and left internal obturator muscle contours were manually delineated upon MRI1 and then automatically propagated on MRI2 by an elastic registration method. Planning CT images were coregistered to both MRIs and dose maps were deformed accordingly. A high-dose region receiving >55 Gy and a low-dose region receiving <55 Gy were identified in each muscle volume. Eighteen textural features were extracted from each region of interest and differences between MRI1 and MRI2 were evaluated. A signal increase was highlighted in both T2-w and T1-w images in the portion of the obturators near the prostate, i.e., in the region receiving medium-high doses. A change in the spatial organization was identified, as an increase in homogeneity and a decrease in contrast and complexity, compatible with an inflammatory status. In particular, the region receiving medium-high doses presented more significant or, at least, stronger differences. Texture analysis applied on T1-w and T2-w MR images has demonstrated its ability in quantitative evaluating radiation-induced changes in obturator muscles after PCa radiotherapy. © 2018 American Association of Physicists in Medicine.

Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images

NASA Astrophysics Data System (ADS)

Kairuddin, Wan Nur Hafsha Wan; Mahmud, Wan Mahani Hafizah Wan

2017-08-01

Image feature extraction is a technique to identify the characteristic of the image. The objective of this work is to discover the texture features that best describe a tissue characteristic of a healthy kidney from ultrasound (US) image. Three ultrasound machines that have different specifications are used in order to get a different quality (different resolution) of the image. Initially, the acquired images are pre-processed to de-noise the speckle to ensure the image preserve the pixels in a region of interest (ROI) for further extraction. Gaussian Low- pass Filter is chosen as the filtering method in this work. 150 of enhanced images then are segmented by creating a foreground and background of image where the mask is created to eliminate some unwanted intensity values. Statistical based texture features method is used namely Intensity Histogram (IH), Gray-Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM).This method is depends on the spatial distribution of intensity values or gray levels in the kidney region. By using One-Way ANOVA in SPSS, the result indicated that three features (Contrast, Difference Variance and Inverse Difference Moment Normalized) from GLCM are not statistically significant; this concludes that these three features describe a healthy kidney characteristics regardless of the ultrasound image quality.

Segmentation of tumor ultrasound image in HIFU therapy based on texture and boundary encoding

NASA Astrophysics Data System (ADS)

Zhang, Dong; Xu, Menglong; Quan, Long; Yang, Yan; Qin, Qianqing; Zhu, Wenbin

2015-02-01

It is crucial in high intensity focused ultrasound (HIFU) therapy to detect the tumor precisely with less manual intervention for enhancing the therapy efficiency. Ultrasound image segmentation becomes a difficult task due to signal attenuation, speckle effect and shadows. This paper presents an unsupervised approach based on texture and boundary encoding customized for ultrasound image segmentation in HIFU therapy. The approach oversegments the ultrasound image into some small regions, which are merged by using the principle of minimum description length (MDL) afterwards. Small regions belonging to the same tumor are clustered as they preserve similar texture features. The mergence is completed by obtaining the shortest coding length from encoding textures and boundaries of these regions in the clustering process. The tumor region is finally selected from merged regions by a proposed algorithm without manual interaction. The performance of the method is tested on 50 uterine fibroid ultrasound images from HIFU guiding transducers. The segmentations are compared with manual delineations to verify its feasibility. The quantitative evaluation with HIFU images shows that the mean true positive of the approach is 93.53%, the mean false positive is 4.06%, the mean similarity is 89.92%, the mean norm Hausdorff distance is 3.62% and the mean norm maximum average distance is 0.57%. The experiments validate that the proposed method can achieve favorable segmentation without manual initialization and effectively handle the poor quality of the ultrasound guidance image in HIFU therapy, which indicates that the approach is applicable in HIFU therapy.

Prostate-specific membrane antigen PET/MRI validation of MR textural analysis for detection of transition zone prostate cancer.

PubMed

Bates, Anthony; Miles, Kenneth

2017-12-01

To validate MR textural analysis (MRTA) for detection of transition zone (TZ) prostate cancer through comparison with co-registered prostate-specific membrane antigen (PSMA) PET-MR. Retrospective analysis was performed for 30 men who underwent simultaneous PSMA PET-MR imaging for staging of prostate cancer. Thirty texture features were derived from each manually contoured T2-weighted, transaxial, prostatic TZ using texture analysis software that applies a spatial band-pass filter and quantifies texture through histogram analysis. Texture features of the TZ were compared to PSMA expression on the corresponding PET images. The Benjamini-Hochberg correction controlled the false discovery rate at <5%. Eighty-eight T2-weighted images in 18 patients demonstrated abnormal PSMA expression within the TZ on PET-MR. 123 images were PSMA negative. Based on the corrected p-value of 0.005, significant differences between PSMA positive and negative slices were found for 16 texture parameters: Standard deviation and mean of positive pixels for all spatial filters (p = <0.0001 for both at all spatial scaling factor (SSF) values) and mean intensity following filtration for SSF 3-6 mm (p = 0.0002-0.0018). Abnormal expression of PSMA within the TZ is associated with altered texture on T2-weighted MR, providing validation of MRTA for the detection of TZ prostate cancer. • Prostate transition zone (TZ) MR texture analysis may assist in prostate cancer detection. • Abnormal transition zone PSMA expression correlates with altered texture on T2-weighted MR. • TZ with abnormal PSMA expression demonstrates significantly reduced MI, SD and MPP.

Efficient thermal noise removal of Sentinel-1 image and its impacts on sea ice applications

NASA Astrophysics Data System (ADS)

Park, Jeong-Won; Korosov, Anton; Babiker, Mohamed

2017-04-01

Wide swath SAR observation from several spaceborne SAR missions played an important role in studying sea ice in the polar region. Sentinel 1A and 1B are producing dual-polarization observation data with the highest temporal resolution ever. For a proper use of dense time-series, radiometric properties must be qualified. Thermal noise is often neglected in many sea ice applications, but is impacting seriously the utility of dual-polarization SAR data. Sentinel-1 TOPSAR image intensity is disturbed by additive thermal noise particularly in cross-polarization channel. Although ESA provides calibrated noise vectors for noise power subtraction, residual noise contribution is significant considering relatively narrow backscattering distribution of cross-polarization channel. In this study, we investigate the noise characteristics and propose an efficient method for noise reduction based on three types of correction: azimuth de-scalloping, noise scaling, and inter-swath power balancing. The core idea is to find optimum correction coefficients resulting in the most noise-uncorrelated gentle backscatter profile over homogeneous region and to combine them with scalloping gain for reconstruction of complete two-dimensional noise field. Denoising is accomplished by subtracting the reconstructed noise field from the original image. The resulting correction coefficients determined by extensive experiments showed different noise characteristics for different Instrument Processing Facility (IPF) versions of Level 1 product generation. Even after thermal noise subtraction, the image still suffers from residual noise, which distorts local statistics. Since this residual noise depends on local signal-to-noise ratio, it can be compensated by variance normalization with coefficients determined from an empirical model. Denoising improved not only visual interpretability but also performances in SAR intensity-based sea ice applications. Results from two applications showed the effectiveness of the proposed method: feature tracking based sea ice drift and texture analysis based sea ice classification. For feature tracking, large spatial asymmetry of keypoint distribution caused by higher noise level in the nearest subswath was decresed so that the matched features to be selected evenly in space. For texture analysis, inter-subswath texture differences caused by different noise equivalent sigma zero were normalized so that the texture features estimated in any subswath have similar value with those in other subswaths.

Highly dynamic biological seabed alterations revealed by side scan sonar tracking of Lanice conchilega beds offshore the island of Sylt (German Bight)

NASA Astrophysics Data System (ADS)

Heinrich, C.; Feldens, P.; Schwarzer, K.

2017-06-01

Hydroacoustic surveys are common tools for habitat investigation and monitoring that aid in the realisation of the aims of the EU Marine Directives. However, the creation of habitat maps is difficult, especially when benthic organisms densely populate the seafloor. This study assesses the sensitivity of entropy and homogeneity image texture parameters derived from backscatter strength data to benthic habitats dominated by the tubeworm Lanice conchilega. Side scan sonar backscatter surveys were carried out in 2010 and 2011 in the German Bight (southern North Sea) at two sites approx. 20 km offshore of the island of Sylt. Abiotic and biotic seabed facies, such as sorted bedforms, areas of fine to medium sand and L. conchilega beds with different tube densities, were identified and characterised based on manual expert analysis and image texture analysis. Ground truthing was performed by grab sampling and underwater video observations. Compared to the manual expert analysis, the k- means classification of image textures proves to be a semi-automated method to investigate small-scale differences in a biologically altered seabed from backscatter data. The texture parameters entropy and homogeneity appear linearly interrelated with tube density, the former positively and the latter negatively. Reinvestigation of one site after 1 year showed an extensive change in the distribution of the L. conchilega-altered seabed. Such marked annual fluctuations in L. conchilega tube cover demonstrate the need for dense time series and high spatial coverage to meaningfully monitor ecological patterns on the seafloor with acoustic backscatter methods in the study region and similar settings worldwide, particularly because the sand mason plays a pivotal role in promoting biodiversity. In this context, image texture analysis provides a cost-effective and reproducible method to track biologically altered seabeds from side scan sonar backscatter signatures.

Separation of specular and diffuse components using tensor voting in color images.

PubMed

Nguyen, Tam; Vo, Quang Nhat; Yang, Hyung-Jeong; Kim, Soo-Hyung; Lee, Guee-Sang

2014-11-20

Most methods for the detection and removal of specular reflections suffer from nonuniform highlight regions and/or nonconverged artifacts induced by discontinuities in the surface colors, especially when dealing with highly textured, multicolored images. In this paper, a novel noniterative and predefined constraint-free method based on tensor voting is proposed to detect and remove the highlight components of a single color image. The distribution of diffuse and specular pixels in the original image is determined using tensors' saliency analysis, instead of comparing color information among neighbor pixels. The achieved diffuse reflectance distribution is used to remove specularity components. The proposed method is evaluated quantitatively and qualitatively over a dataset of highly textured, multicolor images. The experimental results show that our result outperforms other state-of-the-art techniques.

Time-frequency feature representation using multi-resolution texture analysis and acoustic activity detector for real-life speech emotion recognition.

PubMed

Wang, Kun-Ching

2015-01-14

The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech.

Texture based segmentation method to detect atherosclerotic plaque from optical tomography images

NASA Astrophysics Data System (ADS)

Prakash, Ammu; Hewko, Mark; Sowa, Michael; Sherif, Sherif

2013-06-01

Optical coherence tomography (OCT) imaging has been widely employed in assessing cardiovascular disease. Atherosclerosis is one of the major cause cardio vascular diseases. However visual detection of atherosclerotic plaque from OCT images is often limited and further complicated by high frame rates. We developed a texture based segmentation method to automatically detect plaque and non plaque regions from OCT images. To verify our results we compared them to photographs of the vascular tissue with atherosclerotic plaque that we used to generate the OCT images. Our results show a close match with photographs of vascular tissue with atherosclerotic plaque. Our texture based segmentation method for plaque detection could be potentially used in clinical cardiovascular OCT imaging for plaque detection.

Development of type transfer functions for regional-scale nonpoint source groundwater vulnerability assessments

NASA Astrophysics Data System (ADS)

Stewart, Iris T.; Loague, Keith

2003-12-01

Groundwater vulnerability assessments of nonpoint source agrochemical contamination at regional scales are either qualitative in nature or require prohibitively costly computational efforts. By contrast, the type transfer function (TTF) modeling approach for vadose zone pesticide leaching presented here estimates solute concentrations at a depth of interest, only uses available soil survey, climatic, and irrigation information, and requires minimal computational cost for application. TTFs are soil texture based travel time probability density functions that describe a characteristic leaching behavior for soil profiles with similar soil hydraulic properties. Seven sets of TTFs, representing different levels of upscaling, were developed for six loam soil textural classes with the aid of simulated breakthrough curves from synthetic data sets. For each TTF set, TTFs were determined from a group or subgroup of breakthrough curves for each soil texture by identifying the effective parameters of the function that described the average leaching behavior of the group. The grouping of the breakthrough curves was based on the TTF index, a measure of the magnitude of the peak concentration, the peak arrival time, and the concentration spread. Comparison to process-based simulations show that the TTFs perform well with respect to mass balance, concentration magnitude, and the timing of concentration peaks. Sets of TTFs based on individual soil textures perform better for all the evaluation criteria than sets that span all textures. As prediction accuracy and computational cost increase with the number of TTFs in a set, the selection of a TTF set is determined by a given application.

Light extraction efficiency of GaN-based LED with pyramid texture by using ray path analysis.

PubMed

Pan, Jui-Wen; Wang, Chia-Shen

2012-09-10

We study three different gallium-nitride (GaN) based light emitting diode (LED) cases based on the different locations of the pyramid textures. In case 1, the pyramid texture is located on the sapphire top surface, in case 2, the pyramid texture is locate on the P-GaN top surface, while in case 3, the pyramid texture is located on both the sapphire and P-GaN top surfaces. We study the relationship between the light extraction efficiency (LEE) and angle of slant of the pyramid texture. The optimization of total LEE was highest for case 3 among the three cases. Moreover, the seven escape paths along which most of the escaped photon flux propagated were selected in a simulation of the LEDs. The seven escape paths were used to estimate the slant angle for the optimization of LEE and to precisely analyze the photon escape path.

Quantification of Reflection Patterns in Ground-Penetrating Radar Data

NASA Astrophysics Data System (ADS)

Moysey, S.; Knight, R. J.; Jol, H. M.; Allen-King, R. M.; Gaylord, D. R.

2005-12-01

Radar facies analysis provides a way of interpreting the large-scale structure of the subsurface from ground-penetrating radar (GPR) data. Radar facies are often distinguished from each other by the presence of patterns, such as flat-lying, dipping, or chaotic reflections, in different regions of a radar image. When these patterns can be associated with radar facies in a repeated and predictable manner we refer to them as `radar textures'. While it is often possible to qualitatively differentiate between radar textures visually, pattern recognition tools, like neural networks, require a quantitative measure to discriminate between them. We investigate whether currently available tools, such as instantaneous attributes or metrics adapted from standard texture analysis techniques, can be used to improve the classification of radar facies. To this end, we use a neural network to perform cross-validation tests that assess the efficacy of different textural measures for classifying radar facies in GPR data collected from the William River delta, Saskatchewan, Canada. We found that the highest classification accuracies (>93%) were obtained for measures of texture that preserve information about the spatial arrangement of reflections in the radar image, e.g., spatial covariance. Lower accuracy (87%) was obtained for classifications based directly on windows of amplitude data extracted from the radar image. Measures that did not account for the spatial arrangement of reflections in the image, e.g., instantaneous attributes and amplitude variance, yielded classification accuracies of less than 65%. Optimal classifications were obtained for textural measures that extracted sufficient information from the radar data to discriminate between radar facies but were insensitive to other facies specific characteristics. For example, the rotationally invariant Fourier-Mellin transform delivered better classification results than the spatial covariance because dip angle of the reflections, but not dip direction, was an important discriminator between radar facies at the William River delta. To extend the use of radar texture beyond the identification of radar facies to sedimentary facies we are investigating how sedimentary features are encoded in GPR data at Borden, Ontario, Canada. At this site, we have collected extensive sedimentary and hydrologic data over the area imaged by GPR. Analysis of this data coupled with synthetic modeling of the radar signal has allowed us to develop insight into the generation of radar texture in complex geologic environments.

Validation of Satellite-Based Objective Overshooting Cloud-Top Detection Methods Using CloudSat Cloud Profiling Radar Observations

NASA Technical Reports Server (NTRS)

Bedka, Kristopher M.; Dworak, Richard; Brunner, Jason; Feltz, Wayne

2012-01-01

Two satellite infrared-based overshooting convective cloud-top (OT) detection methods have recently been described in the literature: 1) the 11-mm infrared window channel texture (IRW texture) method, which uses IRW channel brightness temperature (BT) spatial gradients and thresholds, and 2) the water vapor minus IRW BT difference (WV-IRW BTD). While both methods show good performance in published case study examples, it is important to quantitatively validate these methods relative to overshooting top events across the globe. Unfortunately, no overshooting top database currently exists that could be used in such study. This study examines National Aeronautics and Space Administration CloudSat Cloud Profiling Radar data to develop an OT detection validation database that is used to evaluate the IRW-texture and WV-IRW BTD OT detection methods. CloudSat data were manually examined over a 1.5-yr period to identify cases in which the cloud top penetrates above the tropopause height defined by a numerical weather prediction model and the surrounding cirrus anvil cloud top, producing 111 confirmed overshooting top events. When applied to Moderate Resolution Imaging Spectroradiometer (MODIS)-based Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager proxy data, the IRW-texture (WV-IRW BTD) method offered a 76% (96%) probability of OT detection (POD) and 16% (81%) false-alarm ratio. Case study examples show that WV-IRW BTD.0 K identifies much of the deep convective cloud top, while the IRW-texture method focuses only on regions with a spatial scale near that of commonly observed OTs. The POD decreases by 20% when IRW-texture is applied to current geostationary imager data, highlighting the importance of imager spatial resolution for observing and detecting OT regions.

Spectral dependence of texture features integrated with hyperspectral data for area target classification improvement

NASA Astrophysics Data System (ADS)

Bangs, Corey F.; Kruse, Fred A.; Olsen, Chris R.

2013-05-01

Hyperspectral data were assessed to determine the effect of integrating spectral data and extracted texture feature data on classification accuracy. Four separate spectral ranges (hundreds of spectral bands total) were used from the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) portions of the electromagnetic spectrum. Haralick texture features (contrast, entropy, and correlation) were extracted from the average gray-level image for each of the four spectral ranges studied. A maximum likelihood classifier was trained using a set of ground truth regions of interest (ROIs) and applied separately to the spectral data, texture data, and a fused dataset containing both. Classification accuracy was measured by comparison of results to a separate verification set of test ROIs. Analysis indicates that the spectral range (source of the gray-level image) used to extract the texture feature data has a significant effect on the classification accuracy. This result applies to texture-only classifications as well as the classification of integrated spectral data and texture feature data sets. Overall classification improvement for the integrated data sets was near 1%. Individual improvement for integrated spectral and texture classification of the "Urban" class showed approximately 9% accuracy increase over spectral-only classification. Texture-only classification accuracy was highest for the "Dirt Path" class at approximately 92% for the spectral range from 947 to 1343nm. This research demonstrates the effectiveness of texture feature data for more accurate analysis of hyperspectral data and the importance of selecting the correct spectral range to be used for the gray-level image source to extract these features.

An automatically generated texture-based atlas of the lungs

NASA Astrophysics Data System (ADS)

Dicente Cid, Yashin; Puonti, Oula; Platon, Alexandra; Van Leemput, Koen; Müller, Henning; Poletti, Pierre-Alexandre

2018-02-01

Many pulmonary diseases can be characterized by visual abnormalities on lung CT scans. Some diseases manifest similar defects but require completely different treatments, as is the case for Pulmonary Hypertension (PH) and Pulmonary Embolism (PE): both present hypo- and hyper-perfused regions but with different distribution across the lung and require different treatment protocols. Finding these distributions by visual inspection is not trivial even for trained radiologists who currently use invasive catheterism to diagnose PH. A Computer-Aided Diagnosis (CAD) tool that could facilitate the non-invasive diagnosis of these diseases can benefit both the radiologists and the patients. Most of the visual differences in the parenchyma can be characterized using texture descriptors. Current CAD systems often use texture information but the texture is either computed in a patch-based fashion, or based on an anatomical division of the lung. The difficulty of precisely finding these divisions in abnormal lungs calls for new tools for obtaining new meaningful divisions of the lungs. In this paper we present a method for unsupervised segmentation of lung CT scans into subregions that are similar in terms of texture and spatial proximity. To this extent, we combine a previously validated Riesz-wavelet texture descriptor with a well-known superpixel segmentation approach that we extend to 3D. We demonstrate the feasibility and accuracy of our approach on a simulated texture dataset, and show preliminary results for CT scans of the lung comparing subjects suffering either from PH or PE. The resulting texture-based atlas of individual lungs can potentially help physicians in diagnosis or be used for studying common texture distributions related to other diseases.

Medical Image Tamper Detection Based on Passive Image Authentication.

PubMed

Ulutas, Guzin; Ustubioglu, Arda; Ustubioglu, Beste; V Nabiyev, Vasif; Ulutas, Mustafa

2017-12-01

Telemedicine has gained popularity in recent years. Medical images can be transferred over the Internet to enable the telediagnosis between medical staffs and to make the patient's history accessible to medical staff from anywhere. Therefore, integrity protection of the medical image is a serious concern due to the broadcast nature of the Internet. Some watermarking techniques are proposed to control the integrity of medical images. However, they require embedding of extra information (watermark) into image before transmission. It decreases visual quality of the medical image and can cause false diagnosis. The proposed method uses passive image authentication mechanism to detect the tampered regions on medical images. Structural texture information is obtained from the medical image by using local binary pattern rotation invariant (LBPROT) to make the keypoint extraction techniques more successful. Keypoints on the texture image are obtained with scale invariant feature transform (SIFT). Tampered regions are detected by the method by matching the keypoints. The method improves the keypoint-based passive image authentication mechanism (they do not detect tampering when the smooth region is used for covering an object) by using LBPROT before keypoint extraction because smooth regions also have texture information. Experimental results show that the method detects tampered regions on the medical images even if the forged image has undergone some attacks (Gaussian blurring/additive white Gaussian noise) or the forged regions are scaled/rotated before pasting.

Crop identification of SAR data using digital textural analysis

NASA Technical Reports Server (NTRS)

Nuesch, D. R.

1983-01-01

After preprocessing SEASAT SAR data which included slant to ground range transformation, registration to LANDSAT MSS data and appropriate filtering of the raw SAR data to minimize coherent speckle, textural features were developed based upon the spatial gray level dependence method (SGLDM) to compute entropy and inertia as textural measures. It is indicated that the consideration of texture features are very important in SAR data analysis. The SEASAT SAR data are useful for the improvement of field boundary definitions and for an earlier season estimate of corn and soybean area location than is supported by LANDSAT alone.

Image segmentation using association rule features.

PubMed

Rushing, John A; Ranganath, Heggere; Hinke, Thomas H; Graves, Sara J

2002-01-01

A new type of texture feature based on association rules is described. Association rules have been used in applications such as market basket analysis to capture relationships present among items in large data sets. It is shown that association rules can be adapted to capture frequently occurring local structures in images. The frequency of occurrence of these structures can be used to characterize texture. Methods for segmentation of textured images based on association rule features are described. Simulation results using images consisting of man made and natural textures show that association rule features perform well compared to other widely used texture features. Association rule features are used to detect cumulus cloud fields in GOES satellite images and are found to achieve higher accuracy than other statistical texture features for this problem.

Pneumothorax detection in chest radiographs using local and global texture signatures

NASA Astrophysics Data System (ADS)

Geva, Ofer; Zimmerman-Moreno, Gali; Lieberman, Sivan; Konen, Eli; Greenspan, Hayit

2015-03-01

A novel framework for automatic detection of pneumothorax abnormality in chest radiographs is presented. The suggested method is based on a texture analysis approach combined with supervised learning techniques. The proposed framework consists of two main steps: at first, a texture analysis process is performed for detection of local abnormalities. Labeled image patches are extracted in the texture analysis procedure following which local analysis values are incorporated into a novel global image representation. The global representation is used for training and detection of the abnormality at the image level. The presented global representation is designed based on the distinctive shape of the lung, taking into account the characteristics of typical pneumothorax abnormalities. A supervised learning process was performed on both the local and global data, leading to trained detection system. The system was tested on a dataset of 108 upright chest radiographs. Several state of the art texture feature sets were experimented with (Local Binary Patterns, Maximum Response filters). The optimal configuration yielded sensitivity of 81% with specificity of 87%. The results of the evaluation are promising, establishing the current framework as a basis for additional improvements and extensions.

Texture- and deformability-based surface recognition by tactile image analysis.

PubMed

Khasnobish, Anwesha; Pal, Monalisa; Tibarewala, D N; Konar, Amit; Pal, Kunal

2016-08-01

Deformability and texture are two unique object characteristics which are essential for appropriate surface recognition by tactile exploration. Tactile sensation is required to be incorporated in artificial arms for rehabilitative and other human-computer interface applications to achieve efficient and human-like manoeuvring. To accomplish the same, surface recognition by tactile data analysis is one of the prerequisites. The aim of this work is to develop effective technique for identification of various surfaces based on deformability and texture by analysing tactile images which are obtained during dynamic exploration of the item by artificial arms whose gripper is fitted with tactile sensors. Tactile data have been acquired, while human beings as well as a robot hand fitted with tactile sensors explored the objects. The tactile images are pre-processed, and relevant features are extracted from the tactile images. These features are provided as input to the variants of support vector machine (SVM), linear discriminant analysis and k-nearest neighbour (kNN) for classification. Based on deformability, six household surfaces are recognized from their corresponding tactile images. Moreover, based on texture five surfaces of daily use are classified. The method adopted in the former two cases has also been applied for deformability- and texture-based recognition of four biomembranes, i.e. membranes prepared from biomaterials which can be used for various applications such as drug delivery and implants. Linear SVM performed best for recognizing surface deformability with an accuracy of 83 % in 82.60 ms, whereas kNN classifier recognizes surfaces of daily use having different textures with an accuracy of 89 % in 54.25 ms and SVM with radial basis function kernel recognizes biomembranes with an accuracy of 78 % in 53.35 ms. The classifiers are observed to generalize well on the unseen test datasets with very high performance to achieve efficient material recognition based on its deformability and texture.

Benefits of Red-Edge Spectral Band and Texture Features for the Object-based Classification using RapidEye sSatellite Image data

NASA Astrophysics Data System (ADS)

Kim, H. O.; Yeom, J. M.

2014-12-01

Space-based remote sensing in agriculture is particularly relevant to issues such as global climate change, food security, and precision agriculture. Recent satellite missions have opened up new perspectives by offering high spatial resolution, various spectral properties, and fast revisit rates to the same regions. Here, we examine the utility of broadband red-edge spectral information in multispectral satellite image data for classifying paddy rice crops in South Korea. Additionally, we examine how object-based spectral features affect the classification of paddy rice growth stages. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the crop condition in heterogeneous paddy rice crop environments, particularly when single-season image data were used. This positive effect appeared to be offset by the multi-temporal image data. Additional texture information brought only a minor improvement or a slight decline, although it is well known to be advantageous for object-based classification in general. We conclude that broadband red-edge information derived from conventional multispectral satellite data has the potential to improve space-based crop monitoring. Because the positive or negative effects of texture features for object-based crop classification could barely be interpreted, the relationships between the textual properties and paddy rice crop parameters at the field scale should be further examined in depth.

Inferring pterosaur diets through quantitative 3D textural analysis of tooth microwear in extant analogues

NASA Astrophysics Data System (ADS)

Bestwick, Jordan; Unwin, David; Butler, Richard; Henderson, Don; Purnell, Mark

2017-04-01

Pterosaurs (Pterosauria) were a successful group of Mesozoic flying reptiles. For 150 million years they were integral components of terrestrial and coastal ecosystems, yet their feeding ecology remains poorly constrained. Postulated pterosaur diets include insectivory, piscivory and/or carnivory, but many dietary hypotheses are speculative and/or based on little evidence, highlighting the need for alternative approaches to provide robust data. One method involves quantitative analysis of the micron-scale 3D textures of worn pterosaur tooth surfaces - dental microwear texture analysis. Microwear is produced as scratches and chips generated by food items create characteristic tooth surface textures. Microwear analysis has never been applied to pterosaurs, but we might expect microwear textures to differ between pterosaurs with different diets. An important step in investigating pterosaur microwear is to examine microwear from extant organisms with known diets to provide a comparative data set. This has been achieved through analysis of non-occlusal microwear textures in extant bats, crocodilians and monitor lizards, clades within which species exhibit insectivorous, piscivorous and carnivorous diets. The results - the first test of the hypothesis that non-occlusal microwear textures in these extant clades vary with diet - provide the context for the first robust quantitative tests of pterosaur diets.

Entropy-Based Adaptive Nuclear Texture Features are Independent Prognostic Markers in a Total Population of Uterine Sarcomas

PubMed Central

Nielsen, Birgitte; Hveem, Tarjei Sveinsgjerd; Kildal, Wanja; Abeler, Vera M; Kristensen, Gunnar B; Albregtsen, Fritz; Danielsen, Håvard E; Rohde, Gustavo K

2015-01-01

Nuclear texture analysis measures the spatial arrangement of the pixel gray levels in a digitized microscopic nuclear image and is a promising quantitative tool for prognosis of cancer. The aim of this study was to evaluate the prognostic value of entropy-based adaptive nuclear texture features in a total population of 354 uterine sarcomas. Isolated nuclei (monolayers) were prepared from 50 µm tissue sections and stained with Feulgen-Schiff. Local gray level entropy was measured within small windows of each nuclear image and stored in gray level entropy matrices, and two superior adaptive texture features were calculated from each matrix. The 5-year crude survival was significantly higher (P < 0.001) for patients with high texture feature values (72%) than for patients with low feature values (36%). When combining DNA ploidy classification (diploid/nondiploid) and texture (high/low feature value), the patients could be stratified into three risk groups with 5-year crude survival of 77, 57, and 34% (Hazard Ratios (HR) of 1, 2.3, and 4.1, P < 0.001). Entropy-based adaptive nuclear texture was an independent prognostic marker for crude survival in multivariate analysis including relevant clinicopathological features (HR = 2.1, P = 0.001), and should therefore be considered as a potential prognostic marker in uterine sarcomas. © The Authors. Published 2014 International Society for Advancement of Cytometry PMID:25483227

Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning.

PubMed

Yu, Huan; Caldwell, Curtis; Mah, Katherine; Mozeg, Daniel

2009-03-01

Coregistered fluoro-deoxy-glucose (FDG) positron emission tomography/computed tomography (PET/CT) has shown potential to improve the accuracy of radiation targeting of head and neck cancer (HNC) when compared to the use of CT simulation alone. The objective of this study was to identify textural features useful in distinguishing tumor from normal tissue in head and neck via quantitative texture analysis of coregistered 18F-FDG PET and CT images. Abnormal and typical normal tissues were manually segmented from PET/CT images of 20 patients with HNC and 20 patients with lung cancer. Texture features including some derived from spatial grey-level dependence matrices (SGLDM) and neighborhood gray-tone-difference matrices (NGTDM) were selected for characterization of these segmented regions of interest (ROIs). Both K nearest neighbors (KNNs) and decision tree (DT)-based KNN classifiers were employed to discriminate images of abnormal and normal tissues. The area under the curve (AZ) of receiver operating characteristics (ROC) was used to evaluate the discrimination performance of features in comparison to an expert observer. The leave-one-out and bootstrap techniques were used to validate the results. The AZ of DT-based KNN classifier was 0.95. Sensitivity and specificity for normal and abnormal tissue classification were 89% and 99%, respectively. In summary, NGTDM features such as PET Coarseness, PET Contrast, and CT Coarseness extracted from FDG PET/CT images provided good discrimination performance. The clinical use of such features may lead to improvement in the accuracy of radiation targeting of HNC.

Computerized margin and texture analyses for differentiating bacterial pneumonia and invasive mucinous adenocarcinoma presenting as consolidation.

PubMed

Koo, Hyun Jung; Kim, Mi Young; Koo, Ja Hwan; Sung, Yu Sub; Jung, Jiwon; Kim, Sung-Han; Choi, Chang-Min; Kim, Hwa Jung

2017-01-01

Radiologists have used margin characteristics based on routine visual analysis; however, the attenuation changes at the margin of the lesion on CT images have not been quantitatively assessed. We established a CT-based margin analysis method by comparing a target lesion with normal lung attenuation, drawing a slope to represent the attenuation changes. This approach was applied to patients with invasive mucinous adenocarcinoma (n = 40) or bacterial pneumonia (n = 30). Correlations among multiple regions of interest (ROIs) were obtained using intraclass correlation coefficient (ICC) values. CT visual assessment, margin and texture parameters were compared for differentiating the two disease entities. The attenuation and margin parameters in multiple ROIs showed excellent ICC values. Attenuation slopes obtained at the margins revealed a difference between invasive mucinous adenocarcinoma and pneumonia (P<0.001), and mucinous adenocarcinoma produced a sharply declining attenuation slope. On multivariable logistic regression analysis, pneumonia had an ill-defined margin (odds ratio (OR), 4.84; 95% confidence interval (CI), 1.26-18.52; P = 0.02), ground-glass opacity (OR, 8.55; 95% CI, 2.09-34.95; P = 0.003), and gradually declining attenuation at the margin (OR, 12.63; 95% CI, 2.77-57.51, P = 0.001). CT-based margin analysis method has a potential to act as an imaging parameter for differentiating invasive mucinous adenocarcinoma and bacterial pneumonia.

Reliable Classification of Geologic Surfaces Using Texture Analysis

NASA Astrophysics Data System (ADS)

Foil, G.; Howarth, D.; Abbey, W. J.; Bekker, D. L.; Castano, R.; Thompson, D. R.; Wagstaff, K.

2012-12-01

Communication delays and bandwidth constraints are major obstacles for remote exploration spacecraft. Due to such restrictions, spacecraft could make use of onboard science data analysis to maximize scientific gain, through capabilities such as the generation of bandwidth-efficient representative maps of scenes, autonomous instrument targeting to exploit targets of opportunity between communications, and downlink prioritization to ensure fast delivery of tactically-important data. Of particular importance to remote exploration is the precision of such methods and their ability to reliably reproduce consistent results in novel environments. Spacecraft resources are highly oversubscribed, so any onboard data analysis must provide a high degree of confidence in its assessment. The TextureCam project is constructing a "smart camera" that can analyze surface images to autonomously identify scientifically interesting targets and direct narrow field-of-view instruments. The TextureCam instrument incorporates onboard scene interpretation and mapping to assist these autonomous science activities. Computer vision algorithms map scenes such as those encountered during rover traverses. The approach, based on a machine learning strategy, trains a statistical model to recognize different geologic surface types and then classifies every pixel in a new scene according to these categories. We describe three methods for increasing the precision of the TextureCam instrument. The first uses ancillary data to segment challenging scenes into smaller regions having homogeneous properties. These subproblems are individually easier to solve, preventing uncertainty in one region from contaminating those that can be confidently classified. The second involves a Bayesian approach that maximizes the likelihood of correct classifications by abstaining from ambiguous ones. We evaluate these two techniques on a set of images acquired during field expeditions in the Mojave Desert. Finally, the algorithm was expanded to perform robust texture classification across a wide range of lighting conditions. We characterize both the increase in precision achieved using different input data representations as well as the range of conditions under which reliable performance can be achieved. An ensemble learning approach is used to increase performance by leveraging the illumination-dependent statistics of an image. Our results show that the three algorithmic modifications lead to a significant increase in classification performance as well as an increase in precision using an adjustable and human-understandable metric of confidence.

Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition

PubMed Central

Wang, Kun-Ching

2015-01-01

The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech. PMID:25594590

Exemplar-Based Image Inpainting Using a Modified Priority Definition.

PubMed

Deng, Liang-Jian; Huang, Ting-Zhu; Zhao, Xi-Le

2015-01-01

Exemplar-based algorithms are a popular technique for image inpainting. They mainly have two important phases: deciding the filling-in order and selecting good exemplars. Traditional exemplar-based algorithms are to search suitable patches from source regions to fill in the missing parts, but they have to face a problem: improper selection of exemplars. To improve the problem, we introduce an independent strategy through investigating the process of patches propagation in this paper. We first define a new separated priority definition to propagate geometry and then synthesize image textures, aiming to well recover image geometry and textures. In addition, an automatic algorithm is designed to estimate steps for the new separated priority definition. Comparing with some competitive approaches, the new priority definition can recover image geometry and textures well.

Exemplar-Based Image Inpainting Using a Modified Priority Definition

PubMed Central

Deng, Liang-Jian; Huang, Ting-Zhu; Zhao, Xi-Le

2015-01-01

Exemplar-based algorithms are a popular technique for image inpainting. They mainly have two important phases: deciding the filling-in order and selecting good exemplars. Traditional exemplar-based algorithms are to search suitable patches from source regions to fill in the missing parts, but they have to face a problem: improper selection of exemplars. To improve the problem, we introduce an independent strategy through investigating the process of patches propagation in this paper. We first define a new separated priority definition to propagate geometry and then synthesize image textures, aiming to well recover image geometry and textures. In addition, an automatic algorithm is designed to estimate steps for the new separated priority definition. Comparing with some competitive approaches, the new priority definition can recover image geometry and textures well. PMID:26492491

Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning

NASA Astrophysics Data System (ADS)

Zhao, Lei; Wang, Zengcai; Wang, Xiaojin; Qi, Yazhou; Liu, Qing; Zhang, Guoxin

2016-09-01

Human fatigue is an important cause of traffic accidents. To improve the safety of transportation, we propose, in this paper, a framework for fatigue expression recognition using image-based facial dynamic multi-information and a bimodal deep neural network. First, the landmark of face region and the texture of eye region, which complement each other in fatigue expression recognition, are extracted from facial image sequences captured by a single camera. Then, two stacked autoencoder neural networks are trained for landmark and texture, respectively. Finally, the two trained neural networks are combined by learning a joint layer on top of them to construct a bimodal deep neural network. The model can be used to extract a unified representation that fuses landmark and texture modalities together and classify fatigue expressions accurately. The proposed system is tested on a human fatigue dataset obtained from an actual driving environment. The experimental results demonstrate that the proposed method performs stably and robustly, and that the average accuracy achieves 96.2%.

Differentiating malignant from benign breast tumors on acoustic radiation force impulse imaging using fuzzy-based neural networks with principle component analysis

NASA Astrophysics Data System (ADS)

Liu, Hsiao-Chuan; Chou, Yi-Hong; Tiu, Chui-Mei; Hsieh, Chi-Wen; Liu, Brent; Shung, K. Kirk

2017-03-01

Many modalities have been developed as screening tools for breast cancer. A new screening method called acoustic radiation force impulse (ARFI) imaging was created for distinguishing breast lesions based on localized tissue displacement. This displacement was quantitated by virtual touch tissue imaging (VTI). However, VTIs sometimes express reverse results to intensity information in clinical observation. In the study, a fuzzy-based neural network with principle component analysis (PCA) was proposed to differentiate texture patterns of malignant breast from benign tumors. Eighty VTIs were randomly retrospected. Thirty four patients were determined as BI-RADS category 2 or 3, and the rest of them were determined as BI-RADS category 4 or 5 by two leading radiologists. Morphological method and Boolean algebra were performed as the image preprocessing to acquire region of interests (ROIs) on VTIs. Twenty four quantitative parameters deriving from first-order statistics (FOS), fractal dimension and gray level co-occurrence matrix (GLCM) were utilized to analyze the texture pattern of breast tumors on VTIs. PCA was employed to reduce the dimension of features. Fuzzy-based neural network as a classifier to differentiate malignant from benign breast tumors. Independent samples test was used to examine the significance of the difference between benign and malignant breast tumors. The area Az under the receiver operator characteristic (ROC) curve, sensitivity, specificity and accuracy were calculated to evaluate the performance of the system. Most all of texture parameters present significant difference between malignant and benign tumors with p-value of less than 0.05 except the average of fractal dimension. For all features classified by fuzzy-based neural network, the sensitivity, specificity, accuracy and Az were 95.7%, 97.1%, 95% and 0.964, respectively. However, the sensitivity, specificity, accuracy and Az can be increased to 100%, 97.1%, 98.8% and 0.985, respectively if PCA was performed to reduce the dimension of features. Patterns of breast tumors on VTIs can effectively be recognized by quantitative texture parameters, and differentiated malignant from benign lesions by fuzzy-based neural network with PCA.

Classification of endoscopic capsule images by using color wavelet features, higher order statistics and radial basis functions.

PubMed

Lima, C S; Barbosa, D; Ramos, J; Tavares, A; Monteiro, L; Carvalho, L

2008-01-01

This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing capsule endoscopic images. Endoscopic images possess rich information expressed by texture. Texture information can be efficiently extracted from medium scales of the wavelet transform. The set of features proposed in this paper to code textural information is named color wavelet covariance (CWC). CWC coefficients are based on the covariances of second order textural measures, an optimum subset of them is proposed. Third and forth order moments are added to cope with distributions that tend to become non-Gaussian, especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data containing 6 full endoscopic exams and reached 95% specificity and 93% sensitivity.

Material quality assessment of silk nanofibers based on swarm intelligence

NASA Astrophysics Data System (ADS)

Brandoli Machado, Bruno; Nunes Gonçalves, Wesley; Martinez Bruno, Odemir

2013-02-01

In this paper, we propose a novel approach for texture analysis based on artificial crawler model. Our method assumes that each agent can interact with the environment and each other. The evolution process converges to an equilibrium state according to the set of rules. For each textured image, the feature vector is composed by signatures of the live agents curve at each time. Experimental results revealed that combining the minimum and maximum signatures into one increase the classification rate. In addition, we pioneer the use of autonomous agents for characterizing silk fibroin scaffolds. The results strongly suggest that our approach can be successfully employed for texture analysis.

SU-E-J-251: Incorporation of Pre-Therapy 18F-FDG Uptake with CT Texture Features in a Predictive Model for Radiation Pneumonitis Development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anthony, G; Cunliffe, A; Armato, S

2015-06-15

Purpose: To determine whether the addition of standardized uptake value (SUV) statistical variables to CT lung texture features can improve a predictive model of radiation pneumonitis (RP) development in patients undergoing radiation therapy. Methods: Anonymized data from 96 esophageal cancer patients (18 RP-positive cases of Grade ≥ 2) were retrospectively collected including pre-therapy PET/CT scans, pre-/posttherapy diagnostic CT scans and RP status. Twenty texture features (firstorder, fractal, Laws’ filter and gray-level co-occurrence matrix) were calculated from diagnostic CT scans and compared in anatomically matched regions of the lung. The mean, maximum, standard deviation, and 50th–95th percentiles of the SUV valuesmore » for all lung voxels in the corresponding PET scans were acquired. For each texture feature, a logistic regression-based classifier consisting of (1) the average change in that texture feature value between the pre- and post-therapy CT scans and (2) the pre-therapy SUV standard deviation (SUV{sub SD}) was created. The RP-classification performance of each logistic regression model was compared to the performance of its texture feature alone by computing areas under the receiver operating characteristic curves (AUCs). T-tests were performed to determine whether the mean AUC across texture features changed significantly when SUV{sub SD} was added to the classifier. Results: The AUC for single-texturefeature classifiers ranged from 0.58–0.81 in high-dose (≥ 30 Gy) regions of the lungs and from 0.53–0.71 in low-dose (< 10 Gy) regions. Adding SUVSD in a logistic regression model using a 50/50 data partition for training and testing significantly increased the mean AUC by 0.08, 0.06 and 0.04 in the low-, medium- and high-dose regions, respectively. Conclusion: Addition of SUVSD from a pre-therapy PET scan to a single CT-based texture feature improves RP-classification performance on average. These findings demonstrate the potential for more accurate prediction of RP using information from multiple imaging modalities. Supported, in part, by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under grant number T32 EB002103; SGA receives royalties and licensing fees through the University of Chicago for computer-aided diagnosis technology. HA receives royalties through the University of Chicago for computer-aided diagnosis technology.« less

Surface inspection of flat products by means of texture analysis: on-line implementation using neural networks

NASA Astrophysics Data System (ADS)

Fernandez, Carlos; Platero, Carlos; Campoy, Pascual; Aracil, Rafael

1994-11-01

This paper describes some texture-based techniques that can be applied to quality assessment of flat products continuously produced (metal strips, wooden surfaces, cork, textile products, ...). Since the most difficult task is that of inspecting for product appearance, human-like inspection ability is required. A common feature to all these products is the presence of non- deterministic texture on their surfaces. Two main subjects are discussed: statistical techniques for both surface finishing determination and surface defect analysis as well as real-time implementation for on-line inspection in high-speed applications. For surface finishing determination a Gray Level Difference technique is presented to perform over low resolution images, that is, no-zoomed images. Defect analysis is performed by means of statistical texture analysis over defective portions of the surface. On-line implementation is accomplished by means of neural networks. When a defect arises, textural analysis is applied which result in a data-vector, acting as input of a neural net, previously trained in a supervised way. This approach tries to reach on-line performance in automated visual inspection applications when texture is presented in flat product surfaces.

The Time Course of Segmentation and Cue-Selectivity in the Human Visual Cortex

PubMed Central

Appelbaum, Lawrence G.; Ales, Justin M.; Norcia, Anthony M.

2012-01-01

Texture discontinuities are a fundamental cue by which the visual system segments objects from their background. The neural mechanisms supporting texture-based segmentation are therefore critical to visual perception and cognition. In the present experiment we employ an EEG source-imaging approach in order to study the time course of texture-based segmentation in the human brain. Visual Evoked Potentials were recorded to four types of stimuli in which periodic temporal modulation of a central 3° figure region could either support figure-ground segmentation, or have identical local texture modulations but not produce changes in global image segmentation. The image discontinuities were defined either by orientation or phase differences across image regions. Evoked responses to these four stimuli were analyzed both at the scalp and on the cortical surface in retinotopic and functional regions-of-interest (ROIs) defined separately using fMRI on a subject-by-subject basis. Texture segmentation (tsVEP: segmenting versus non-segmenting) and cue-specific (csVEP: orientation versus phase) responses exhibited distinctive patterns of activity. Alternations between uniform and segmented images produced highly asymmetric responses that were larger after transitions from the uniform to the segmented state. Texture modulations that signaled the appearance of a figure evoked a pattern of increased activity starting at ∼143 ms that was larger in V1 and LOC ROIs, relative to identical modulations that didn't signal figure-ground segmentation. This segmentation-related activity occurred after an initial response phase that did not depend on the global segmentation structure of the image. The two cue types evoked similar tsVEPs up to 230 ms when they differed in the V4 and LOC ROIs. The evolution of the response proceeded largely in the feed-forward direction, with only weak evidence for feedback-related activity. PMID:22479566

Measurement of Vibrated Bulk Density of Coke Particle Blends Using Image Texture Analysis

NASA Astrophysics Data System (ADS)

Azari, Kamran; Bogoya-Forero, Wilinthon; Duchesne, Carl; Tessier, Jayson

2017-09-01

A rapid and nondestructive machine vision sensor was developed for predicting the vibrated bulk density (VBD) of petroleum coke particles based on image texture analysis. It could be used for making corrective adjustments to a paste plant operation to reduce green anode variability (e.g., changes in binder demand). Wavelet texture analysis (WTA) and gray level co-occurrence matrix (GLCM) algorithms were used jointly for extracting the surface textural features of coke aggregates from images. These were correlated with the VBD using partial least-squares (PLS) regression. Coke samples of several sizes and from different sources were used to test the sensor. Variations in the coke surface texture introduced by coke size and source allowed for making good predictions of the VBD of individual coke samples and mixtures of them (blends involving two sources and different sizes). Promising results were also obtained for coke blends collected from an industrial-baked carbon anode manufacturer.

Texture analysis of Napoleonic War Era copper bolts

NASA Astrophysics Data System (ADS)

Malamud, Florencia; Northover, Shirley; James, Jon; Northover, Peter; Kelleher, Joe

2016-04-01

Neutron diffraction techniques are suitable for volume texture analyses due to high penetration of thermal neutrons in most materials. We have implemented a new data analysis methodology that employed the spatial resolution achievable by a time-of-flight neutron strain scanner to non-destructively determine the crystallographic texture at selected locations within a macroscopic sample. The method is based on defining the orientation distribution function of the crystallites from several incomplete pole figures, and it has been implemented on ENGIN-X, a neutron strain scanner at the Isis Facility in the UK. Here, we demonstrate the application of this new texture analysis methodology in determining the crystallographic texture at selected locations within museum quality archaeological objects up to 1 m in length. The results were verified using samples of similar, but less valuable, objects by comparing the results of applying this method with those obtained using both electron backscatter diffraction and X-ray diffraction on their cross sections.

Variations in algorithm implementation among quantitative texture analysis software packages

NASA Astrophysics Data System (ADS)

Foy, Joseph J.; Mitta, Prerana; Nowosatka, Lauren R.; Mendel, Kayla R.; Li, Hui; Giger, Maryellen L.; Al-Hallaq, Hania; Armato, Samuel G.

2018-02-01

Open-source texture analysis software allows for the advancement of radiomics research. Variations in texture features, however, result from discrepancies in algorithm implementation. Anatomically matched regions of interest (ROIs) that captured normal breast parenchyma were placed in the magnetic resonance images (MRI) of 20 patients at two time points. Six first-order features and six gray-level co-occurrence matrix (GLCM) features were calculated for each ROI using four texture analysis packages. Features were extracted using package-specific default GLCM parameters and using GLCM parameters modified to yield the greatest consistency among packages. Relative change in the value of each feature between time points was calculated for each ROI. Distributions of relative feature value differences were compared across packages. Absolute agreement among feature values was quantified by the intra-class correlation coefficient. Among first-order features, significant differences were found for max, range, and mean, and only kurtosis showed poor agreement. All six second-order features showed significant differences using package-specific default GLCM parameters, and five second-order features showed poor agreement; with modified GLCM parameters, no significant differences among second-order features were found, and all second-order features showed poor agreement. While relative texture change discrepancies existed across packages, these differences were not significant when consistent parameters were used.

Mammographic phenotypes of breast cancer risk driven by breast anatomy

NASA Astrophysics Data System (ADS)

Gastounioti, Aimilia; Oustimov, Andrew; Hsieh, Meng-Kang; Pantalone, Lauren; Conant, Emily F.; Kontos, Despina

2017-03-01

Image-derived features of breast parenchymal texture patterns have emerged as promising risk factors for breast cancer, paving the way towards personalized recommendations regarding women's cancer risk evaluation and screening. The main steps to extract texture features of the breast parenchyma are the selection of regions of interest (ROIs) where texture analysis is performed, the texture feature calculation and the texture feature summarization in case of multiple ROIs. In this study, we incorporate breast anatomy in these three key steps by (a) introducing breast anatomical sampling for the definition of ROIs, (b) texture feature calculation aligned with the structure of the breast and (c) weighted texture feature summarization considering the spatial position and the underlying tissue composition of each ROI. We systematically optimize this novel framework for parenchymal tissue characterization in a case-control study with digital mammograms from 424 women. We also compare the proposed approach with a conventional methodology, not considering breast anatomy, recently shown to enhance the case-control discriminatory capacity of parenchymal texture analysis. The case-control classification performance is assessed using elastic-net regression with 5-fold cross validation, where the evaluation measure is the area under the curve (AUC) of the receiver operating characteristic. Upon optimization, the proposed breast-anatomy-driven approach demonstrated a promising case-control classification performance (AUC=0.87). In the same dataset, the performance of conventional texture characterization was found to be significantly lower (AUC=0.80, DeLong's test p-value<0.05). Our results suggest that breast anatomy may further leverage the associations of parenchymal texture features with breast cancer, and may therefore be a valuable addition in pipelines aiming to elucidate quantitative mammographic phenotypes of breast cancer risk.

Characterisation of group behaviour surface texturing with multi-layers fitting method

NASA Astrophysics Data System (ADS)

Kang, Zhengyang; Fu, Yonghong; Ji, Jinghu; Wang, Hao

2016-07-01

Surface texturing was widely applied in improving the tribological properties of mechanical components, but study of measurement of this technology was still insufficient. This study proposed the multi-layers fitting (MLF) method to characterise the dimples array texture surface. Based on the synergistic effect among the dimples, the 3D morphology of texture surface was rebuilt by 2D stylus profiler in the MLF method. The feasible regions of texture patterns and sensitive parameters were confirmed by non-linear programming, and the processing software of MLF method was developed based on the Matlab®. The characterisation parameters system of dimples was defined mathematically, and the accuracy of MLF method was investigated by comparison experiment. The surface texture specimens were made by laser surface texturing technology, in which high consistency of dimples' size and distribution was achieved. Then, 2D profiles of different dimples were captured by employing Hommel-T1000 stylus profiler, and the data were further processed by MLF software to rebuild 3D morphology of single dimple. The experiment results indicated that the MLF characterisation results were similar to those of Wyko T1100, the white light interference microscope. It was also found that the stability of MLF characterisation results highly depended on the number of captured cross-sections.

Factorization-based texture segmentation

DOE PAGES

Yuan, Jiangye; Wang, Deliang; Cheriyadat, Anil M.

2015-06-17

This study introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative features, we factor the feature matrix into two matrices-one consisting of the representative features and the other containing the weights of representative features at each pixel used for linear combination. The factorization method is based on singular value decomposition and nonnegative matrix factorization. The method uses local spectral histogramsmore » to discriminate region appearances in a computationally efficient way and at the same time accurately localizes region boundaries. Finally, the experiments conducted on public segmentation data sets show the promise of this simple yet powerful approach.« less

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices.

PubMed

Keller, Brad M; Oustimov, Andrew; Wang, Yan; Chen, Jinbo; Acciavatti, Raymond J; Zheng, Yuanjie; Ray, Shonket; Gee, James C; Maidment, Andrew D A; Kontos, Despina

2015-04-01

An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges-Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., [Formula: see text]) and with a larger offset length (i.e., [Formula: see text]), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.

SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andrews, M; Abazeed, M; Woody, N

Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported tomore » R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.« less

Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients.

PubMed

Yue, Yong; Osipov, Arsen; Fraass, Benedick; Sandler, Howard; Zhang, Xiao; Nissen, Nicholas; Hendifar, Andrew; Tuli, Richard

2017-02-01

To stratify risks of pancreatic adenocarcinoma (PA) patients using pre- and post-radiotherapy (RT) PET/CT images, and to assess the prognostic value of texture variations in predicting therapy response of patients. Twenty-six PA patients treated with RT from 2011-2013 with pre- and post-treatment 18F-FDG-PET/CT scans were identified. Tumor locoregional texture was calculated using 3D kernel-based approach, and texture variations were identified by fitting discrepancies of texture maps of pre- and post-treatment images. A total of 48 texture and clinical variables were identified and evaluated for association with overall survival (OS). The prognostic heterogeneity features were selected using lasso/elastic net regression, and further were evaluated by multivariate Cox analysis. Median age was 69 y (range, 46-86 y). The texture map and temporal variations between pre- and post-treatment were well characterized by histograms and statistical fitting. The lasso analysis identified seven predictors (age, node stage, post-RT SUVmax, variations of homogeneity, variance, sum mean, and cluster tendency). The multivariate Cox analysis identified five significant variables: age, node stage, variations of homogeneity, variance, and cluster tendency (with P=0.020, 0.040, 0.065, 0.078, and 0.081, respectively). The patients were stratified into two groups based on the risk score of multivariate analysis with log-rank P=0.001: a low risk group (n=11) with a longer mean OS (29.3 months) and higher texture variation (>30%), and a high risk group (n=15) with a shorter mean OS (17.7 months) and lower texture variation (<15%). Locoregional metabolic texture response provides a feasible approach for evaluating and predicting clinical outcomes following treatment of PA with RT. The proposed method can be used to stratify patient risk and help select appropriate treatment strategies for individual patients toward implementing response-driven adaptive RT.

Regional shape-based feature space for segmenting biomedical images using neural networks

NASA Astrophysics Data System (ADS)

Sundaramoorthy, Gopal; Hoford, John D.; Hoffman, Eric A.

1993-07-01

In biomedical images, structure of interest, particularly the soft tissue structures, such as the heart, airways, bronchial and arterial trees often have grey-scale and textural characteristics similar to other structures in the image, making it difficult to segment them using only gray- scale and texture information. However, these objects can be visually recognized by their unique shapes and sizes. In this paper we discuss, what we believe to be, a novel, simple scheme for extracting features based on regional shapes. To test the effectiveness of these features for image segmentation (classification), we use an artificial neural network and a statistical cluster analysis technique. The proposed shape-based feature extraction algorithm computes regional shape vectors (RSVs) for all pixels that meet a certain threshold criteria. The distance from each such pixel to a boundary is computed in 8 directions (or in 26 directions for a 3-D image). Together, these 8 (or 26) values represent the pixel's (or voxel's) RSV. All RSVs from an image are used to train a multi-layered perceptron neural network which uses these features to 'learn' a suitable classification strategy. To clearly distinguish the desired object from other objects within an image, several examples from inside and outside the desired object are used for training. Several examples are presented to illustrate the strengths and weaknesses of our algorithm. Both synthetic and actual biomedical images are considered. Future extensions to this algorithm are also discussed.

Mobile robots traversability awareness based on terrain visual sensory data fusion

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir

2007-04-01

In this paper, we have presented methods that significantly improve the robot awareness of its terrain traversability conditions. The terrain traversability awareness is achieved by association of terrain image appearances from different poses and fusion of extracted information from multimodality imaging and range sensor data for localization and clustering environment landmarks. Initially, we describe methods for extraction of salient features of the terrain for the purpose of landmarks registration from two or more images taken from different via points along the trajectory path of the robot. The method of image registration is applied as a means of overlaying (two or more) of the same terrain scene at different viewpoints. The registration geometrically aligns salient landmarks of two images (the reference and sensed images). A Similarity matching techniques is proposed for matching the terrain salient landmarks. Secondly, we present three terrain classifier models based on rule-based, supervised neural network, and fuzzy logic for classification of terrain condition under uncertainty and mapping the robot's terrain perception to apt traversability measures. This paper addresses the technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain spatial and textural cues.

Experimental Study on the Perception Characteristics of Haptic Texture by Multidimensional Scaling.

PubMed

Wu, Juan; Li, Na; Liu, Wei; Song, Guangming; Zhang, Jun

2015-01-01

Recent works regarding real texture perception demonstrate that physical factors such as stiffness and spatial period play a fundamental role in texture perception. This research used a multidimensional scaling (MDS) analysis to further characterize and quantify the effects of the simulation parameters on haptic texture rendering and perception. In a pilot experiment, 12 haptic texture samples were generated by using a 3-degrees-of-freedom (3-DOF) force-feedback device with varying spatial period, height, and stiffness coefficient parameter values. The subjects' perceptions of the virtual textures indicate that roughness, denseness, flatness and hardness are distinguishing characteristics of texture. In the main experiment, 19 participants rated the dissimilarities of the textures and estimated the magnitudes of their characteristics. The MDS method was used to recover the underlying perceptual space and reveal the significance of the space from the recorded data. The physical parameters and their combinations have significant effects on the perceptual characteristics. A regression model was used to quantitatively analyze the parameters and their effects on the perceptual characteristics. This paper is to illustrate that haptic texture perception based on force feedback can be modeled in two- or three-dimensional space and provide suggestions on improving perception-based haptic texture rendering.

Texture operator for snow particle classification into snowflake and graupel

NASA Astrophysics Data System (ADS)

Nurzyńska, Karolina; Kubo, Mamoru; Muramoto, Ken-ichiro

2012-11-01

In order to improve the estimation of precipitation, the coefficients of Z-R relation should be determined for each snow type. Therefore, it is necessary to identify the type of falling snow. Consequently, this research addresses a problem of snow particle classification into snowflake and graupel in an automatic manner (as these types are the most common in the study region). Having correctly classified precipitation events, it is believed that it will be possible to estimate the related parameters accurately. The automatic classification system presented here describes the images with texture operators. Some of them are well-known from the literature: first order features, co-occurrence matrix, grey-tone difference matrix, run length matrix, and local binary pattern, but also a novel approach to design simple local statistic operators is introduced. In this work the following texture operators are defined: mean histogram, min-max histogram, and mean-variance histogram. Moreover, building a feature vector, which is based on the structure created in many from mentioned algorithms is also suggested. For classification, the k-nearest neighbourhood classifier was applied. The results showed that it is possible to achieve correct classification accuracy above 80% by most of the techniques. The best result of 86.06%, was achieved for operator built from a structure achieved in the middle stage of the co-occurrence matrix calculation. Next, it was noticed that describing an image with two texture operators does not improve the classification results considerably. In the best case the correct classification efficiency was 87.89% for a pair of texture operators created from local binary pattern and structure build in a middle stage of grey-tone difference matrix calculation. This also suggests that the information gathered by each texture operator is redundant. Therefore, the principal component analysis was applied in order to remove the unnecessary information and additionally reduce the length of the feature vectors. The improvement of the correct classification efficiency for up to 100% is possible for methods: min-max histogram, texture operator built from structure achieved in a middle stage of co-occurrence matrix calculation, texture operator built from a structure achieved in a middle stage of grey-tone difference matrix creation, and texture operator based on a histogram, when the feature vector stores 99% of initial information.

Histogram contrast analysis and the visual segregation of IID textures.

PubMed

Chubb, C; Econopouly, J; Landy, M S

1994-09-01

A new psychophysical methodology is introduced, histogram contrast analysis, that allows one to measure stimulus transformations, f, used by the visual system to draw distinctions between different image regions. The method involves the discrimination of images constructed by selecting texture micropatterns randomly and independently (across locations) on the basis of a given micropattern histogram. Different components of f are measured by use of different component functions to modulate the micropattern histogram until the resulting textures are discriminable. When no discrimination threshold can be obtained for a given modulating component function, a second titration technique may be used to measure the contribution of that component to f. The method includes several strong tests of its own assumptions. An example is given of the method applied to visual textures composed of small, uniform squares with randomly chosen gray levels. In particular, for a fixed mean gray level mu and a fixed gray-level variance sigma 2, histogram contrast analysis is used to establish that the class S of all textures composed of small squares with jointly independent, identically distributed gray levels with mean mu and variance sigma 2 is perceptually elementary in the following sense: there exists a single, real-valued function f S of gray level, such that two textures I and J in S are discriminable only if the average value of f S applied to the gray levels in I is significantly different from the average value of f S applied to the gray levels in J. Finally, histogram contrast analysis is used to obtain a seventh-order polynomial approximation of f S.

Soft computing-based terrain visual sensing and data fusion for unmanned ground robotic systems

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir

2006-05-01

In this paper, we have primarily discussed technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain visual clues. The Kalman Filtering technique is applied for aggregative fusion of sub-terrain assessment results. The last two terrain classifiers are shown to have remarkable capability for terrain traversability assessment of natural terrains. We have conducted a comparative performance evaluation of all three terrain classifiers and presented the results in this paper.

Identification of low variability textural features for heterogeneity quantification of 18F-FDG PET/CT imaging.

PubMed

Cortes-Rodicio, J; Sanchez-Merino, G; Garcia-Fidalgo, M A; Tobalina-Larrea, I

To identify those textural features that are insensitive to both technical and biological factors in order to standardise heterogeneity studies on 18 F-FDG PET imaging. Two different studies were performed. First, nineteen series from a cylindrical phantom filled with different 18 F-FDG activity concentration were acquired and reconstructed using three different protocols. Seventy-two texture features were calculated inside a circular region of interest. The variability of each feature was obtained. Second, the data for 15 patients showing non-pathological liver were acquired. Anatomical and physiological features such as patient's weight, height, body mass index, metabolic active volume, blood glucose level, SUV and SUV standard deviation were also recorded. A liver covering region of interest was delineated and low variability textural features calculated in each patient. Finally, a multivariate Spearman's correlation analysis between biological factors and texture features was performed. Only eight texture features analysed show small variability (<5%) with activity concentration and reconstruction protocol making them suitable for heterogeneity quantification. On the other hand, there is a high statistically significant correlation between MAV and entropy (P<0.05). Entropy feature is, indeed, correlated (P<0.05) with all patient parameters, except body mass index. The textural features that are correlated with neither technical nor biological factors are run percentage, short-zone emphasis and intensity, making them suitable for quantifying functional changes or classifying patients. Other textural features are correlated with technical and biological factors and are, therefore, a source of errors if used for this purpose. Copyright © 2016 Elsevier España, S.L.U. y SEMNIM. All rights reserved.

Climate and soil properties limit the positive effects of land use reversion on carbon storage in Eastern Australia

NASA Astrophysics Data System (ADS)

Rabbi, S. M. F.; Tighe, Matthew; Delgado-Baquerizo, Manuel; Cowie, Annette; Robertson, Fiona; Dalal, Ram; Page, Kathryn; Crawford, Doug; Wilson, Brian R.; Schwenke, Graeme; McLeod, Malem; Badgery, Warwick; Dang, Yash P.; Bell, Mike; O'Leary, Garry; Liu, De Li; Baldock, Jeff

2015-12-01

Australia’s “Direct Action” climate change policy relies on purchasing greenhouse gas abatement from projects undertaking approved abatement activities. Management of soil organic carbon (SOC) in agricultural soils is an approved activity, based on the expectation that land use change can deliver significant changes in SOC. However, there are concerns that climate, topography and soil texture will limit changes in SOC stocks. This work analyses data from 1482 sites surveyed across the major agricultural regions of Eastern Australia to determine the relative importance of land use vs. other drivers of SOC. Variation in land use explained only 1.4% of the total variation in SOC, with aridity and soil texture the main regulators of SOC stock under different land uses. Results suggest the greatest potential for increasing SOC stocks in Eastern Australian agricultural regions lies in converting from cropping to pasture on heavy textured soils in the humid regions.

Climate and soil properties limit the positive effects of land use reversion on carbon storage in Eastern Australia

PubMed Central

Rabbi, S.M.F.; Tighe, Matthew; Delgado-Baquerizo, Manuel; Cowie, Annette; Robertson, Fiona; Dalal, Ram; Page, Kathryn; Crawford, Doug; Wilson, Brian R.; Schwenke, Graeme; Mcleod, Malem; Badgery, Warwick; Dang, Yash P.; Bell, Mike; O’Leary, Garry; Liu, De Li; Baldock, Jeff

2015-01-01

Australia’s “Direct Action” climate change policy relies on purchasing greenhouse gas abatement from projects undertaking approved abatement activities. Management of soil organic carbon (SOC) in agricultural soils is an approved activity, based on the expectation that land use change can deliver significant changes in SOC. However, there are concerns that climate, topography and soil texture will limit changes in SOC stocks. This work analyses data from 1482 sites surveyed across the major agricultural regions of Eastern Australia to determine the relative importance of land use vs. other drivers of SOC. Variation in land use explained only 1.4% of the total variation in SOC, with aridity and soil texture the main regulators of SOC stock under different land uses. Results suggest the greatest potential for increasing SOC stocks in Eastern Australian agricultural regions lies in converting from cropping to pasture on heavy textured soils in the humid regions. PMID:26639009

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

PubMed Central

Hu, Leland S.; Ning, Shuluo; Eschbacher, Jennifer M.; Baxter, Leslie C.; Gaw, Nathan; Ranjbar, Sara; Plasencia, Jonathan; Dueck, Amylou C.; Peng, Sen; Smith, Kris A.; Nakaji, Peter; Karis, John P.; Quarles, C. Chad; Wu, Teresa; Loftus, Joseph C.; Jenkins, Robert B.; Sicotte, Hugues; Kollmeyer, Thomas M.; O'Neill, Brian P.; Elmquist, William; Hoxworth, Joseph M.; Frakes, David; Sarkaria, Jann; Swanson, Kristin R.; Tran, Nhan L.; Li, Jing; Mitchell, J. Ross

2017-01-01

Background Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. Methods We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). Results We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). Conclusion MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology. PMID:27502248

Intraoral radiographs texture analysis for dental implant planning.

PubMed

Mundim, Mayara B V; Dias, Danilo R; Costa, Ronaldo M; Leles, Cláudio R; Azevedo-Marques, Paulo M; Ribeiro-Rotta, Rejane F

2016-11-01

Computer vision extracts features or attributes from images improving diagnosis accuracy and aiding in clinical decisions. This study aims to investigate the feasibility of using texture analysis of periapical radiograph images as a tool for dental implant treatment planning. Periapical radiograph images of 127 jawbone sites were obtained before and after implant placement. From the superimposition of the pre- and post-implant images, four regions of interest (ROI) were delineated on the pre-implant images for each implant site: mesial, distal and apical peri-implant areas and a central area. Each ROI was analysed using Matlab® software and seven image attributes were extracted: mean grey level (MGL), standard deviation of grey levels (SDGL), coefficient of variation (CV), entropy (En), contrast, correlation (Cor) and angular second moment (ASM). Images were grouped by bone types-Lekholm and Zarb classification (1,2,3,4). Peak insertion torque (PIT) and resonance frequency analysis (RFA) were recorded during implant placement. Differences among groups were tested for each image attribute. Agreement between measurements of the peri-implant ROIs and overall ROI (peri-implant + central area) was tested, as well as the association between primary stability measures (PIT and RFA) and texture attributes. Differences among bone type groups were found for MGL (p = 0.035), SDGL (p = 0.024), CV (p < 0.001) and En (p < 0.001). The apical ROI showed a significant difference from the other regions for all attributes, except Cor. Concordance correlation coefficients were all almost perfect (ρ > 0.93), except for ASM (ρ = 0.62). Texture attributes were significantly associated with the implant stability measures. Texture analysis of periapical radiographs may be a reliable non-invasive quantitative method for the assessment of jawbone and prediction of implant stability, with potential clinical applications. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features

PubMed Central

Gangeh, Mehrdad; Tadayyon, Hadi; Sadeghi-Naini, Ali; Gandhi, Sonal; Wright, Frances C.; Slodkowska, Elzbieta; Curpen, Belinda; Tran, William; Czarnota, Gregory J.

2018-01-01

Background Pathological response of breast cancer to chemotherapy is a prognostic indicator for long-term disease free and overall survival. Responses of locally advanced breast cancer in the neoadjuvant chemotherapy (NAC) settings are often variable, and the prediction of response is imperfect. The purpose of this study was to detect primary tumor responses early after the start of neoadjuvant chemotherapy using quantitative ultrasound (QUS), textural analysis and molecular features in patients with locally advanced breast cancer. Methods The study included ninety six patients treated with neoadjuvant chemotherapy. Breast tumors were scanned with a clinical ultrasound system prior to chemotherapy treatment, during the first, fourth and eighth week of treatment, and prior to surgery. Quantitative ultrasound parameters and scatterer-based features were calculated from ultrasound radio frequency (RF) data within tumor regions of interest. Additionally, texture features were extracted from QUS parametric maps. Prior to therapy, all patients underwent a core needle biopsy and histological subtypes and biomarker ER, PR, and HER2 status were determined. Patients were classified into three treatment response groups based on combination of clinical and pathological analyses: complete responders (CR), partial responders (PR), and non-responders (NR). Response classifications from QUS parameters, receptors status and pathological were compared. Discriminant analysis was performed on extracted parameters using a support vector machine classifier to categorize subjects into CR, PR, and NR groups at all scan times. Results Of the 96 patients, the number of CR, PR and NR patients were 21, 52, and 23, respectively. The best prediction of treatment response was achieved with the combination mean QUS values, texture and molecular features with accuracies of 78%, 86% and 83% at weeks 1, 4, and 8, after treatment respectively. Mean QUS parameters or clinical receptors status alone predicted the three response groups with accuracies less than 60% at all scan time points. Recurrence free survival (RFS) of response groups determined based on combined features followed similar trend as determined based on clinical and pathology. Conclusions This work demonstrates the potential of using QUS, texture and molecular features for predicting the response of primary breast tumors to chemotherapy early, and guiding the treatment planning of refractory patients. PMID:29298305

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

PubMed Central

Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. PMID:25371701

Surface Texture-Based Surface Treatments on Ti6Al4V Titanium Alloys for Tribological and Biological Applications: A Mini Review

PubMed Central

Li, Dali; Zou, Jiaojuan; Xie, Ruizhen; Wang, Zhihua; Tang, Bin

2018-01-01

Surface texture (ST) has been confirmed as an effective and economical surface treatment technique that can be applied to a great range of materials and presents growing interests in various engineering fields. Ti6Al4V which is the most frequently and successfully used titanium alloy has long been restricted in tribological-related operations due to the shortcomings of low surface hardness, high friction coefficient, and poor abrasive wear resistance. Ti6Al4V has benefited from surface texture-based surface treatments over the last decade. This review begins with a brief introduction, analysis approaches, and processing methods of surface texture. The specific applications of the surface texture-based surface treatments for improving surface performance of Ti6Al4V are thoroughly reviewed from the point of view of tribology and biology. PMID:29587358

Grounding the figure: surface attachment influences figure-ground organization.

PubMed

Vecera, Shaun P; Palmer, Stephen E

2006-08-01

We investigated whether the lower region effect on figure-ground organization (Vecera, Vogel, and Woodman, 2002) would generalize to contextual depth planes in vertical orientations, as is predicted by a theoretical analysis based on the ecological statistics of edges arising from objects that are attached to surfaces of support. Observers viewed left/right ambiguous figure-ground displays that occluded middle sections of four types of contextual inducers: two types of attached, receding, vertical planes (walls) that used linear perspective and/or texture gradients to induce perceived depth and two types of similar trapezoidal control figures that used either uniform color or random texture to reduce or eliminate perceived depth. The results showed a reliable bias toward seeing as "figure" the side of the figure-ground display that was attached to the receding depth plane, but no such bias for the corresponding side in either of the control conditions. The results are interpreted as being consistent with the attachment hypothesis that the lower region cue to figure-ground organization results from ecological biases in edge interpretation that arise when objects are attached to supporting surfaces in the terrestrial gravitational field.

Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks.

PubMed

Sun, X; Chen, K J; Berg, E P; Newman, D J; Schwartz, C A; Keller, W L; Maddock Carlin, K R

2014-02-01

The objective was to use digital color image texture features to predict troponin-T degradation in beef. Image texture features, including 88 gray level co-occurrence texture features, 81 two-dimension fast Fourier transformation texture features, and 48 Gabor wavelet filter texture features, were extracted from color images of beef strip steaks (longissimus dorsi, n = 102) aged for 10d obtained using a digital camera and additional lighting. Steaks were designated degraded or not-degraded based on troponin-T degradation determined on d 3 and d 10 postmortem by immunoblotting. Statistical analysis (STEPWISE regression model) and artificial neural network (support vector machine model, SVM) methods were designed to classify protein degradation. The d 3 and d 10 STEPWISE models were 94% and 86% accurate, respectively, while the d 3 and d 10 SVM models were 63% and 71%, respectively, in predicting protein degradation in aged meat. STEPWISE and SVM models based on image texture features show potential to predict troponin-T degradation in meat. © 2013.

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.

PubMed

Ortiz-Ramón, Rafael; Larroza, Andrés; Ruiz-España, Silvia; Arana, Estanislao; Moratal, David

2018-05-14

To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.

SU-F-J-207: Non-Small Cell Lung Cancer Patient Survival Prediction with Quantitative Tumor Textures Analysis in Baseline CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Y; Zou, J; Murillo, P

Purpose: Chemo-radiation therapy (CRT) is widely used in treating patients with locally advanced non-small cell lung cancer (NSCLC). Determination of the likelihood of patient response to treatment and optimization of treatment regime is of clinical significance. Up to date, no imaging biomarker has reliably correlated to NSCLC patient survival rate. This pilot study is to extract CT texture information from tumor regions for patient survival prediction. Methods: Thirteen patients with stage II-III NSCLC were treated using CRT with a median dose of 6210 cGy. Non-contrast-enhanced CT images were acquired for treatment planning and retrospectively collected for this study. Texture analysismore » was applied in segmented tumor regions using the Local Binary Pattern method (LBP). By comparing its HU with neighboring voxels, the LBPs of a voxel were measured in multiple scales with different group radiuses and numbers of neighbors. The LBP histograms formed a multi-dimensional texture vector for each patient, which was then used to establish and test a Support Vector Machine (SVM) model to predict patients’ one year survival. The leave-one-out cross validation strategy was used recursively to enlarge the training set and derive a reliable predictor. The predictions were compared with the true clinical outcomes. Results: A 10-dimensional LBP histogram was extracted from 3D segmented tumor region for each of the 13 patients. Using the SVM model with the leave-one-out strategy, only 1 out of 13 patients was misclassified. The experiments showed an accuracy of 93%, sensitivity of 100%, and specificity of 86%. Conclusion: Within the framework of a Support Vector Machine based model, the Local Binary Pattern method is able to extract a quantitative imaging biomarker in the prediction of NSCLC patient survival. More patients are to be included in the study.« less

Pet fur color and texture classification

NASA Astrophysics Data System (ADS)

Yen, Jonathan; Mukherjee, Debarghar; Lim, SukHwan; Tretter, Daniel

2007-01-01

Object segmentation is important in image analysis for imaging tasks such as image rendering and image retrieval. Pet owners have been known to be quite vocal about how important it is to render their pets perfectly. We present here an algorithm for pet (mammal) fur color classification and an algorithm for pet (animal) fur texture classification. Per fur color classification can be applied as a necessary condition for identifying the regions in an image that may contain pets much like the skin tone classification for human flesh detection. As a result of the evolution, fur coloration of all mammals is caused by a natural organic pigment called Melanin and Melanin has only very limited color ranges. We have conducted a statistical analysis and concluded that mammal fur colors can be only in levels of gray or in two colors after the proper color quantization. This pet fur color classification algorithm has been applied for peteye detection. We also present here an algorithm for animal fur texture classification using the recently developed multi-resolution directional sub-band Contourlet transform. The experimental results are very promising as these transforms can identify regions of an image that may contain fur of mammals, scale of reptiles and feather of birds, etc. Combining the color and texture classification, one can have a set of strong classifiers for identifying possible animals in an image.

Topological image texture analysis for quality assessment

NASA Astrophysics Data System (ADS)

Asaad, Aras T.; Rashid, Rasber Dh.; Jassim, Sabah A.

2017-05-01

Image quality is a major factor influencing pattern recognition accuracy and help detect image tampering for forensics. We are concerned with investigating topological image texture analysis techniques to assess different type of degradation. We use Local Binary Pattern (LBP) as a texture feature descriptor. For any image construct simplicial complexes for selected groups of uniform LBP bins and calculate persistent homology invariants (e.g. number of connected components). We investigated image quality discriminating characteristics of these simplicial complexes by computing these models for a large dataset of face images that are affected by the presence of shadows as a result of variation in illumination conditions. Our tests demonstrate that for specific uniform LBP patterns, the number of connected component not only distinguish between different levels of shadow effects but also help detect the infected regions as well.

P04.19 Recommendations for computation of textural measures obtained from 3D brain tumor MRIs: A robustness analysis points out the need for standardization.

PubMed Central

Molina, D.; Pérez-Beteta, J.; Martínez-González, A.; Velásquez, C.; Martino, J.; Luque, B.; Revert, A.; Herruzo, I.; Arana, E.; Pérez-García, V. M.

2017-01-01

Abstract Introduction: Textural analysis refers to a variety of mathematical methods used to quantify the spatial variations in grey levels within images. In brain tumors, textural features have a great potential as imaging biomarkers having been shown to correlate with survival, tumor grade, tumor type, etc. However, these measures should be reproducible under dynamic range and matrix size changes for their clinical use. Our aim is to study this robustness in brain tumors with 3D magnetic resonance imaging, not previously reported in the literature. Materials and methods: 3D T1-weighted images of 20 patients with glioblastoma (64.80 ± 9.12 years-old) obtained from a 3T scanner were analyzed. Tumors were segmented using an in-house semi-automatic 3D procedure. A set of 16 3D textural features of the most common types (co-occurrence and run-length matrices) were selected, providing regional (run-length based measures) and local information (co-ocurrence matrices) on the tumor heterogeneity. Feature robustness was assessed by means of the coefficient of variation (CV) under both dynamic range (16, 32 and 64 gray levels) and/or matrix size (256x256 and 432x432) changes. Results: None of the textural features considered were robust under dynamic range changes. The textural co-occurrence matrix feature Entropy was the only textural feature robust (CV < 10%) under spatial resolution changes. Conclusions: In general, textural measures of three-dimensional brain tumor images are neither robust under dynamic range nor under matrix size changes. Thus, it becomes mandatory to fix standards for image rescaling after acquisition before the textural features are computed if they are to be used as imaging biomarkers. For T1-weighted images a dynamic range of 16 grey levels and a matrix size of 256x256 (and isotropic voxel) is found to provide reliable and comparable results and is feasible with current MRI scanners. The implications of this work go beyond the specific tumor type and MRI sequence studied here and pose the need for standardization in textural feature calculation of oncological images. FUNDING: James S. Mc. Donnell Foundation (USA) 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer [Collaborative award 220020450 and planning grant 220020420], MINECO/FEDER [MTM2015-71200-R], JCCM [PEII-2014-031-P].

Relevance of 2D radiographic texture analysis for the assessment of 3D bone micro-architecture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Apostol, Lian; Boudousq, Vincent; Basset, Oliver

Although the diagnosis of osteoporosis is mainly based on dual x-ray absorptiometry, it has been shown that trabecular bone micro-architecture is also an important factor in regard to fracture risk. In vivo, techniques based on high-resolution x-ray radiography associated to texture analysis have been proposed to investigate bone micro-architecture, but their relevance for giving pertinent 3D information is unclear. Thirty-three calcaneus and femoral neck bone samples including the cortical shells (diameter: 14 mm, height: 30-40 mm) were imaged using 3D-synchrotron x-ray micro-CT at the ESRF. The 3D reconstructed images with a cubic voxel size of 15 {mu}m were further usedmore » for two purposes: (1) quantification of three-dimensional trabecular bone micro-architecture (2) simulation of realistic x-ray radiographs under different acquisition conditions. The simulated x-ray radiographs were then analyzed using a large variety of texture analysis methods (co-occurrence, spectral density, fractal, morphology, etc.). The range of micro-architecture parameters was in agreement with previous studies and rather large, suggesting that the population was representative. More than 350 texture parameters were tested. A small number of them were selected based on their correlation to micro-architectural morphometric parameters. Using this subset of texture parameters, multiple regression allowed one to predict up to 93% of the variance of micro-architecture parameters using three texture features. 2D texture features predicting 3D micro-architecture parameters other than BV/TV were identified. The methodology proposed for evaluating the relationships between 3D micro-architecture and 2D texture parameters may also be used for optimizing the conditions for radiographic imaging. Further work will include the application of the method to physical radiographs. In the future, this approach could be used in combination with DXA to refine osteoporosis diagnosis.« less

Fast Image Texture Classification Using Decision Trees

NASA Technical Reports Server (NTRS)

Thompson, David R.

2011-01-01

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

Dust emission parameterization scheme over the MENA region: Sensitivity analysis to soil moisture and soil texture

NASA Astrophysics Data System (ADS)

Gherboudj, Imen; Beegum, S. Naseema; Marticorena, Beatrice; Ghedira, Hosni

2015-10-01

The mineral dust emissions from arid/semiarid soils were simulated over the MENA (Middle East and North Africa) region using the dust parameterization scheme proposed by Alfaro and Gomes (2001), to quantify the effect of the soil moisture and clay fraction in the emissions. For this purpose, an extensive data set of Soil Moisture and Ocean Salinity soil moisture, European Centre for Medium-Range Weather Forecasting wind speed at 10 m height, Food Agricultural Organization soil texture maps, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index, and erodibility of the soil surface were collected for the a period of 3 years, from 2010 to 2013. Though the considered data sets have different temporal and spatial resolution, efforts have been made to make them consistent in time and space. At first, the simulated sandblasting flux over the region were validated qualitatively using MODIS Deep Blue aerosol optical depth and EUMETSAT MSG (Meteosat Seciond Generation) dust product from SEVIRI (Meteosat Spinning Enhanced Visible and Infrared Imager) and quantitatively based on the available ground-based measurements of near-surface particulate mass concentrations (PM10) collected over four stations in the MENA region. Sensitivity analyses were performed to investigate the effect of soil moisture and clay fraction on the emissions flux. The results showed that soil moisture and soil texture have significant roles in the dust emissions over the MENA region, particularly over the Arabian Peninsula. An inversely proportional dependency is observed between the soil moisture and the sandblasting flux, where a steep reduction in flux is observed at low friction velocity and a gradual reduction is observed at high friction velocity. Conversely, a directly proportional dependency is observed between the soil clay fraction and the sandblasting flux where a steep increase in flux is observed at low friction velocity and a gradual increase is observed at high friction velocity. The magnitude of the percentage reduction/increase in the sandblasting flux decreases with the increase of the friction velocity for both soil moisture and soil clay fraction. Furthermore, these variables are interdependent leading to a gradual decrease in the percentage increase in the sandblasting flux for higher soil moisture values.

Investigation of quartz grain surface textures by atomic force microscopy for forensic analysis.

PubMed

Konopinski, D I; Hudziak, S; Morgan, R M; Bull, P A; Kenyon, A J

2012-11-30

This paper presents a study of quartz sand grain surface textures using atomic force microscopy (AFM) to image the surface. Until now scanning electron microscopy (SEM) has provided the primary technique used in the forensic surface texture analysis of quartz sand grains as a means of establishing the provenance of the grains for forensic reconstructions. The ability to independently corroborate the grain type classifications is desirable and provides additional weight to the findings of SEM analysis of the textures of quartz grains identified in forensic soil/sediment samples. AFM offers a quantitative means of analysis that complements SEM examination, and is a non-destructive technique that requires no sample preparation prior to scanning. It therefore has great potential to be used for forensic analysis where sample preservation is highly valuable. By taking quantitative topography scans, it is possible to produce 3D representations of microscopic surface textures and diagnostic features for examination. Furthermore, various empirical measures can be obtained from analysing the topography scans, including arithmetic average roughness, root-mean-square surface roughness, skewness, kurtosis, and multiple gaussian fits to height distributions. These empirical measures, combined with qualitative examination of the surfaces can help to discriminate between grain types and provide independent analysis that can corroborate the morphological grain typing based on the surface textures assigned using SEM. Furthermore, the findings from this study also demonstrate that quartz sand grain surfaces exhibit a statistically self-similar fractal nature that remains unchanged across scales. This indicates the potential for a further quantitative measure that could be utilised in the discrimination of quartz grains based on their provenance for forensic investigations. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.

PubMed

Okumura, Eiichiro; Kawashita, Ikuo; Ishida, Takayuki

2017-08-01

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

Modelling submerged coastal environments: Remote sensing technologies, techniques, and comparative analysis

NASA Astrophysics Data System (ADS)

Dillon, Chris

Built upon remote sensing and GIS littoral zone characterization methodologies of the past decade, a series of loosely coupled models aimed to test, compare and synthesize multi-beam SONAR (MBES), Airborne LiDAR Bathymetry (ALB), and satellite based optical data sets in the Gulf of St. Lawrence, Canada, eco-region. Bathymetry and relative intensity metrics for the MBES and ALB data sets were run through a quantitative and qualitative comparison, which included outputs from the Benthic Terrain Modeller (BTM) tool. Substrate classification based on relative intensities of respective data sets and textural indices generated using grey level co-occurrence matrices (GLCM) were investigated. A spatial modelling framework built in ArcGIS(TM) for the derivation of bathymetric data sets from optical satellite imagery was also tested for proof of concept and validation. Where possible, efficiencies and semi-automation for repeatable testing was achieved using ArcGIS(TM) ModelBuilder. The findings from this study could assist future decision makers in the field of coastal management and hydrographic studies. Keywords: Seafloor terrain characterization, Benthic Terrain Modeller (BTM), Multi-beam SONAR, Airborne LiDAR Bathymetry, Satellite Derived Bathymetry, ArcGISTM ModelBuilder, Textural analysis, Substrate classification.

Texture segmentation by genetic programming.

PubMed

Song, Andy; Ciesielski, Vic

2008-01-01

This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.

Impact of Surface Roughness and Soil Texture on Mineral Dust Emission Fluxes Modeling

NASA Technical Reports Server (NTRS)

Menut, Laurent; Perez, Carlos; Haustein, Karsten; Bessagnet, Bertrand; Prigent, Catherine; Alfaro, Stephane

2013-01-01

Dust production models (DPM) used to estimate vertical fluxes of mineral dust aerosols over arid regions need accurate data on soil and surface properties. The Laboratoire Inter-Universitaire des Systemes Atmospheriques (LISA) data set was developed for Northern Africa, the Middle East, and East Asia. This regional data set was built through dedicated field campaigns and include, among others, the aerodynamic roughness length, the smooth roughness length of the erodible fraction of the surface, and the dry (undisturbed) soil size distribution. Recently, satellite-derived roughness length and high-resolution soil texture data sets at the global scale have emerged and provide the opportunity for the use of advanced schemes in global models. This paper analyzes the behavior of the ERS satellite-derived global roughness length and the State Soil Geographic data base-Food and Agriculture Organization of the United Nations (STATSGO-FAO) soil texture data set (based on wet techniques) using an advanced DPM in comparison to the LISA data set over Northern Africa and the Middle East. We explore the sensitivity of the drag partition scheme (a critical component of the DPM) and of the dust vertical fluxes (intensity and spatial patterns) to the roughness length and soil texture data sets. We also compare the use of the drag partition scheme to a widely used preferential source approach in global models. Idealized experiments with prescribed wind speeds show that the ERS and STATSGO-FAO data sets provide realistic spatial patterns of dust emission and friction velocity thresholds in the region. Finally, we evaluate a dust transport model for the period of March to July 2011 with observed aerosol optical depths from Aerosol Robotic Network sites. Results show that ERS and STATSGO-FAO provide realistic simulations in the region.

Visual terrain mapping for traversable path planning of mobile robots

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Amrani, Rachida; Tunstel, Edward W.

2004-10-01

In this paper, we have primarily discussed technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain visual clues. The Kalman Filtering technique is applied for aggregative fusion of sub-terrain assessment results. The last two terrain classifiers are shown to have remarkable capability for terrain traversability assessment of natural terrains. We have conducted a comparative performance evaluation of all three terrain classifiers and presented the results in this paper.

Noninvasive Classification of Hepatic Fibrosis Based on Texture Parameters From Double Contrast-Enhanced Magnetic Resonance Images

PubMed Central

Bahl, Gautam; Cruite, Irene; Wolfson, Tanya; Gamst, Anthony C.; Collins, Julie M.; Chavez, Alyssa D.; Barakat, Fatma; Hassanein, Tarek; Sirlin, Claude B.

2016-01-01

Purpose To demonstrate a proof of concept that quantitative texture feature analysis of double contrast-enhanced magnetic resonance imaging (MRI) can classify fibrosis noninvasively, using histology as a reference standard. Materials and Methods A Health Insurance Portability and Accountability Act (HIPAA)-compliant Institutional Review Board (IRB)-approved retrospective study of 68 patients with diffuse liver disease was performed at a tertiary liver center. All patients underwent double contrast-enhanced MRI, with histopathology-based staging of fibrosis obtained within 12 months of imaging. The MaZda software program was used to compute 279 texture parameters for each image. A statistical regularization technique, generalized linear model (GLM)-path, was used to develop a model based on texture features for dichotomous classification of fibrosis category (F ≤2 vs. F ≥3) of the 68 patients, with histology as the reference standard. The model's performance was assessed and cross-validated. There was no additional validation performed on an independent cohort. Results Cross-validated sensitivity, specificity, and total accuracy of the texture feature model in classifying fibrosis were 91.9%, 83.9%, and 88.2%, respectively. Conclusion This study shows proof of concept that accurate, noninvasive classification of liver fibrosis is possible by applying quantitative texture analysis to double contrast-enhanced MRI. Further studies are needed in independent cohorts of subjects. PMID:22851409

Classification and recognition of texture collagen obtaining by multiphoton microscope with neural network analysis

NASA Astrophysics Data System (ADS)

Wu, Shulian; Peng, Yuanyuan; Hu, Liangjun; Zhang, Xiaoman; Li, Hui

2016-01-01

Second harmonic generation microscopy (SHGM) was used to monitor the process of chronological aging skin in vivo. The collagen structures of mice model with different ages were obtained using SHGM. Then, texture feature with contrast, correlation and entropy were extracted and analysed using the grey level co-occurrence matrix. At last, the neural network tool of Matlab was applied to train the texture of collagen in different statues during the aging process. And the simulation of mice collagen texture was carried out. The results indicated that the classification accuracy reach 85%. Results demonstrated that the proposed approach effectively detected the target object in the collagen texture image during the chronological aging process and the analysis tool based on neural network applied the skin of classification and feature extraction method is feasible.

Detection of Focal Cortical Dysplasia Lesions in MRI Using Textural Features

NASA Astrophysics Data System (ADS)

Loyek, Christian; Woermann, Friedrich G.; Nattkemper, Tim W.

Focal cortical dysplasia (FCD) is a frequent cause of medically refractory partial epilepsy. The visual identification of FCD lesions on magnetic resonance images (MRI) is a challenging task in standard radiological analysis. Quantitative image analysis which tries to assist in the diagnosis of FCD lesions is an active field of research. In this work we investigate the potential of different texture features, in order to explore to what extent they are suitable for detecting lesional tissue. As a result we can show first promising results based on segmentation and texture classification.

Quantitative three-dimensional microtextural analyses of tooth wear as a tool for dietary discrimination in fishes

PubMed Central

Purnell, Mark; Seehausen, Ole; Galis, Frietson

2012-01-01

Resource polymorphisms and competition for resources are significant factors in speciation. Many examples come from fishes, and cichlids are of particular importance because of their role as model organisms at the interface of ecology, development, genetics and evolution. However, analysis of trophic resource use in fishes can be difficult and time-consuming, and for fossil fish species it is particularly problematic. Here, we present evidence from cichlids that analysis of tooth microwear based on high-resolution (sub-micrometre scale) three-dimensional data and new ISO standards for quantification of surface textures provides a powerful tool for dietary discrimination and investigation of trophic resource exploitation. Our results suggest that three-dimensional approaches to analysis offer significant advantages over two-dimensional operator-scored methods of microwear analysis, including applicability to rough tooth surfaces that lack distinct scratches and pits. Tooth microwear textures develop over a longer period of time than is represented by stomach contents, and analyses based on textures are less prone to biases introduced by opportunistic feeding. They are more sensitive to subtle dietary differences than isotopic analysis. Quantitative textural analysis of tooth microwear has a useful role to play, complementing existing approaches, in trophic analysis of fishes—both extant and extinct. PMID:22491979

A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

PubMed

Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

2016-01-13

A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

Spatial prediction of soil texture in region Centre (France) from summary data

NASA Astrophysics Data System (ADS)

Dobarco, Mercedes Roman; Saby, Nicolas; Paroissien, Jean-Baptiste; Orton, Tom G.

2015-04-01

Soil texture is a key controlling factor of important soil functions like water and nutrient holding capacity, retention of pollutants, drainage, soil biodiversity, and C cycling. High resolution soil texture maps enhance our understanding of the spatial distribution of soil properties and provide valuable information for decision making and crop management, environmental protection, and hydrological planning. We predicted the soil texture of agricultural topsoils in the Region Centre (France) combining regression and area-to-point kriging. Soil texture data was collected from the French soil-test database (BDAT), which is populated with soil analysis performed by farmers' demand. To protect the anonymity of the farms the data was treated by commune. In a first step, summary statistics of environmental covariates by commune were used to develop prediction models with Cubist, boosted regression trees, and random forests. In a second step the residuals of each individual observation were summarized by commune and kriged following the method developed by Orton et al. (2012). This approach allowed to include non-linear relationships among covariates and soil texture while accounting for the uncertainty on areal means in the area-to-point kriging step. Independent validation of the models was done using data from the systematic soil monitoring network of French soils. Future work will compare the performance of these models with a non-stationary variance geostatistical model using the most important covariates and summary statistics of texture data. The results will inform on whether the later and statistically more-challenging approach improves significantly texture predictions or whether the more simple area-to-point regression kriging can offer satisfactory results. The application of area-to-point regression kriging at national level using BDAT data has the potential to improve soil texture predictions for agricultural topsoils, especially when combined with existing maps (i.e., model ensemble).

A neural model of visual figure-ground segregation from kinetic occlusion.

PubMed

Barnes, Timothy; Mingolla, Ennio

2013-01-01

Freezing is an effective defense strategy for some prey, because their predators rely on visual motion to distinguish objects from their surroundings. An object moving over a background progressively covers (deletes) and uncovers (accretes) background texture while simultaneously producing discontinuities in the optic flow field. These events unambiguously specify kinetic occlusion and can produce a crisp edge, depth perception, and figure-ground segmentation between identically textured surfaces--percepts which all disappear without motion. Given two abutting regions of uniform random texture with different motion velocities, one region appears to be situated farther away and behind the other (i.e., the ground) if its texture is accreted or deleted at the boundary between the regions, irrespective of region and boundary velocities. Consequently, a region with moving texture appears farther away than a stationary region if the boundary is stationary, but it appears closer (i.e., the figure) if the boundary is moving coherently with the moving texture. A computational model of visual areas V1 and V2 shows how interactions between orientation- and direction-selective cells first create a motion-defined boundary and then signal kinetic occlusion at that boundary. Activation of model occlusion detectors tuned to a particular velocity results in the model assigning the adjacent surface with a matching velocity to the far depth. A weak speed-depth bias brings faster-moving texture regions forward in depth in the absence of occlusion (shearing motion). These processes together reproduce human psychophysical reports of depth ordering for key cases of kinetic occlusion displays. Copyright © 2012 Elsevier Ltd. All rights reserved.

Automatic Texture Reconstruction of 3d City Model from Oblique Images

NASA Astrophysics Data System (ADS)

Kang, Junhua; Deng, Fei; Li, Xinwei; Wan, Fang

2016-06-01

In recent years, the photorealistic 3D city models are increasingly important in various geospatial applications related to virtual city tourism, 3D GIS, urban planning, real-estate management. Besides the acquisition of high-precision 3D geometric data, texture reconstruction is also a crucial step for generating high-quality and visually realistic 3D models. However, most of the texture reconstruction approaches are probably leading to texture fragmentation and memory inefficiency. In this paper, we introduce an automatic framework of texture reconstruction to generate textures from oblique images for photorealistic visualization. Our approach include three major steps as follows: mesh parameterization, texture atlas generation and texture blending. Firstly, mesh parameterization procedure referring to mesh segmentation and mesh unfolding is performed to reduce geometric distortion in the process of mapping 2D texture to 3D model. Secondly, in the texture atlas generation step, the texture of each segmented region in texture domain is reconstructed from all visible images with exterior orientation and interior orientation parameters. Thirdly, to avoid color discontinuities at boundaries between texture regions, the final texture map is generated by blending texture maps from several corresponding images. We evaluated our texture reconstruction framework on a dataset of a city. The resulting mesh model can get textured by created texture without resampling. Experiment results show that our method can effectively mitigate the occurrence of texture fragmentation. It is demonstrated that the proposed framework is effective and useful for automatic texture reconstruction of 3D city model.

Textural features for image classification

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Dinstein, I.; Shanmugam, K.

1973-01-01

Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

Texture analysis applied to second harmonic generation image data for ovarian cancer classification

NASA Astrophysics Data System (ADS)

Wen, Bruce L.; Brewer, Molly A.; Nadiarnykh, Oleg; Hocker, James; Singh, Vikas; Mackie, Thomas R.; Campagnola, Paul J.

2014-09-01

Remodeling of the extracellular matrix has been implicated in ovarian cancer. To quantitate the remodeling, we implement a form of texture analysis to delineate the collagen fibrillar morphology observed in second harmonic generation microscopy images of human normal and high grade malignant ovarian tissues. In the learning stage, a dictionary of "textons"-frequently occurring texture features that are identified by measuring the image response to a filter bank of various shapes, sizes, and orientations-is created. By calculating a representative model based on the texton distribution for each tissue type using a training set of respective second harmonic generation images, we then perform classification between images of normal and high grade malignant ovarian tissues. By optimizing the number of textons and nearest neighbors, we achieved classification accuracy up to 97% based on the area under receiver operating characteristic curves (true positives versus false positives). The local analysis algorithm is a more general method to probe rapidly changing fibrillar morphologies than global analyses such as FFT. It is also more versatile than other texture approaches as the filter bank can be highly tailored to specific applications (e.g., different disease states) by creating customized libraries based on common image features.

Texture and Tempered Condition Combined Effects on Fatigue Behavior in an Al-Cu-Li Alloy

NASA Astrophysics Data System (ADS)

Wang, An; Liu, Zhiyi; Liu, Meng; Wu, Wenting; Bai, Song; Yang, Rongxian

2017-05-01

Texture and tempered condition combined effects on fatigue behavior in an Al-Cu-Li alloy have been investigated using tensile testing, cyclic loading testing, scanning electron microscope (SEM), transmission electron microscopy (TEM) and texture analysis. Results showed that in near-threshold region, T4-tempered samples possessed the lowest fatigue crack propagation (FCP) rate. In Paris regime, T4-tempered sample had similar FCP rate with T6-tempered sample. T83-tempered sample exhibited the greatest FCP rate among the three tempered conditions. 3% pre-stretching in T83-tempered sample resulted in a reducing intensity of Goss texture and facilitated T1 precipitation. SEM results showed that less crack deflection was observed in T83-tempered sample, as compared to other two tempered samples. It was the combined effects of a lower intensity of Goss texture and T1 precipitates retarding the reversible dislocation slipping in the plastic zone ahead the crack tip.

TH-E-BRF-04: Characterizing the Response of Texture-Based CT Image Features for Quantification of Radiation-Induced Normal Lung Damage

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krafft, S; Court, L; Briere, T

2014-06-15

Purpose: Radiation induced lung damage (RILD) is an important dose-limiting toxicity for patients treated with radiation therapy. Scoring systems for RILD are subjective and limit our ability to find robust predictors of toxicity. We investigate the dose and time-related response for texture-based lung CT image features that serve as potential quantitative measures of RILD. Methods: Pre- and post-RT diagnostic imaging studies were collected for retrospective analysis of 21 patients treated with photon or proton radiotherapy for NSCLC. Total lung and selected isodose contours (0–5, 5–15, 15–25Gy, etc.) were deformably registered from the treatment planning scan to the pre-RT and availablemore » follow-up CT studies for each patient. A CT image analysis framework was utilized to extract 3698 unique texture-based features (including co-occurrence and run length matrices) for each region of interest defined by the isodose contours and the total lung volume. Linear mixed models were fit to determine the relationship between feature change (relative to pre-RT), planned dose and time post-RT. Results: Seventy-three follow-up CT scans from 21 patients (median: 3 scans/patient) were analyzed to describe CT image feature change. At the p=0.05 level, dose affected feature change in 2706 (73.1%) of the available features. Similarly, time affected feature change in 408 (11.0%) of the available features. Both dose and time were significant predictors of feature change in a total of 231 (6.2%) of the extracted image features. Conclusion: Characterizing the dose and time-related response of a large number of texture-based CT image features is the first step toward identifying objective measures of lung toxicity necessary for assessment and prediction of RILD. There is evidence that numerous features are sensitive to both the radiation dose and time after RT. Beyond characterizing feature response, further investigation is warranted to determine the utility of these features as surrogates of clinically significant lung injury.« less

Associations Between PET Textural Features and GLUT1 Expression, and the Prognostic Significance of Textural Features in Lung Adenocarcinoma.

PubMed

Koh, Young Wha; Park, Seong Yong; Hyun, Seung Hyup; Lee, Su Jin

2018-02-01

We evaluated the association between positron emission tomography (PET) textural features and glucose transporter 1 (GLUT1) expression level and further investigated the prognostic significance of textural features in lung adenocarcinoma. We evaluated 105 adenocarcinoma patients. We extracted texture-based PET parameters of primary tumors. Conventional PET parameters were also measured. The relationships between PET parameters and GLUT1 expression levels were evaluated. The association between PET parameters and overall survival (OS) was assessed using Cox's proportional hazard regression models. In terms of PET textural features, tumors expressing high levels of GLUT1 exhibited significantly lower coarseness, contrast, complexity, and strength, but significantly higher busyness. On univariate analysis, the metabolic tumor volume, total lesion glycolysis, contrast, busyness, complexity, and strength were significant predictors of OS. Multivariate analysis showed that lower complexity (HR=2.017, 95%CI=1.032-3.942, p=0.040) was independently associated with poorer survival. PET textural features may aid risk stratification in lung adenocarcinoma patients. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Content-based image retrieval for interstitial lung diseases using classification confidence

NASA Astrophysics Data System (ADS)

Dash, Jatindra Kumar; Mukhopadhyay, Sudipta; Prabhakar, Nidhi; Garg, Mandeep; Khandelwal, Niranjan

2013-02-01

Content Based Image Retrieval (CBIR) system could exploit the wealth of High-Resolution Computed Tomography (HRCT) data stored in the archive by finding similar images to assist radiologists for self learning and differential diagnosis of Interstitial Lung Diseases (ILDs). HRCT findings of ILDs are classified into several categories (e.g. consolidation, emphysema, ground glass, nodular etc.) based on their texture like appearances. Therefore, analysis of ILDs is considered as a texture analysis problem. Many approaches have been proposed for CBIR of lung images using texture as primitive visual content. This paper presents a new approach to CBIR for ILDs. The proposed approach makes use of a trained neural network (NN) to find the output class label of query image. The degree of confidence of the NN classifier is analyzed using Naive Bayes classifier that dynamically takes a decision on the size of the search space to be used for retrieval. The proposed approach is compared with three simple distance based and one classifier based texture retrieval approaches. Experimental results show that the proposed technique achieved highest average percentage precision of 92.60% with lowest standard deviation of 20.82%.

Sensitivity and specificity of 3-D texture analysis of lung parenchyma is better than 2-D for discrimination of lung pathology in stage 0 COPD

NASA Astrophysics Data System (ADS)

Xu, Ye; Sonka, Milan; McLennan, Geoffrey; Guo, Junfeng; Hoffman, Eric

2005-04-01

Lung parenchyma evaluation via multidetector-row CT (MDCT), has significantly altered clinical practice in the early detection of lung disease. Our goal is to enhance our texture-based tissue classification ability to differentiate early pathologic processes by extending our 2-D Adaptive Multiple Feature Method (AMFM) to 3-D AMFM. We performed MDCT on 34 human volunteers in five categories: emphysema in severe Chronic Obstructive Pulmonary Disease (COPD) as EC, emphysema in mild COPD (MC), normal appearing lung in COPD (NC), non-smokers with normal lung function (NN), smokers with normal function (NS). We volumetrically excluded the airway and vessel regions, calculated 24 volumetric texture features for each Volume of Interest (VOI); and used Bayesian rules for discrimination. Leave-one-out and half-half methods were used for testing. Sensitivity, specificity and accuracy were calculated. The accuracy of the leave-one-out method for the four-class classification in the form of 3-D/2-D is: EC: 84.9%/70.7%, MC: 89.8%/82.7%; NC: 87.5.0%/49.6%; NN: 100.0%/60.0%. The accuracy of the leave-one-out method for the two-class classification in the form of 3-D/2-D is: NN: 99.3%/71.6%; NS: 99.7%/74.5%. We conclude that 3-D AMFM analysis of the lung parenchyma improves discrimination compared to 2-D analysis of the same images.

Identifying metastatic breast tumors using textural kinetic features of a contrast based habitat in DCE-MRI

NASA Astrophysics Data System (ADS)

Chaudhury, Baishali; Zhou, Mu; Goldgof, Dmitry B.; Hall, Lawrence O.; Gatenby, Robert A.; Gillies, Robert J.; Drukteinis, Jennifer S.

2015-03-01

The ability to identify aggressive tumors from indolent tumors using quantitative analysis on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) would dramatically change the breast cancer treatment paradigm. With this prognostic information, patients with aggressive tumors that have the ability to spread to distant sites outside of the breast could be selected for more aggressive treatment and surveillance regimens. Conversely, patients with tumors that do not have the propensity to metastasize could be treated less aggressively, avoiding some of the morbidity associated with surgery, radiation and chemotherapy. We propose a computer aided detection framework to determine which breast cancers will metastasize to the loco-regional lymph nodes as well as which tumors will eventually go on to develop distant metastses using quantitative image analysis and radiomics. We defined a new contrast based tumor habitat and analyzed textural kinetic features from this habitat for classification purposes. The proposed tumor habitat, which we call combined-habitat, is derived from the intersection of two individual tumor sub-regions: one that exhibits rapid initial contrast uptake and the other that exhibits rapid delayed contrast washout. Hence the combined-habitat represents the tumor sub-region within which the pixels undergo both rapid initial uptake and rapid delayed washout. We analyzed a dataset of twenty-seven representative two dimensional (2D) images from volumetric DCE-MRI of breast tumors, for classification of tumors with no lymph nodes from tumors with positive number of axillary lymph nodes. For this classification an accuracy of 88.9% was achieved. Twenty of the twenty-seven patients were analyzed for classification of distant metastatic tumors from indolent cancers (tumors with no lymph nodes), for which the accuracy was 84.3%.

Collagen morphology and texture analysis: from statistics to classification

PubMed Central

Mostaço-Guidolin, Leila B.; Ko, Alex C.-T.; Wang, Fei; Xiang, Bo; Hewko, Mark; Tian, Ganghong; Major, Arkady; Shiomi, Masashi; Sowa, Michael G.

2013-01-01

In this study we present an image analysis methodology capable of quantifying morphological changes in tissue collagen fibril organization caused by pathological conditions. Texture analysis based on first-order statistics (FOS) and second-order statistics such as gray level co-occurrence matrix (GLCM) was explored to extract second-harmonic generation (SHG) image features that are associated with the structural and biochemical changes of tissue collagen networks. Based on these extracted quantitative parameters, multi-group classification of SHG images was performed. With combined FOS and GLCM texture values, we achieved reliable classification of SHG collagen images acquired from atherosclerosis arteries with >90% accuracy, sensitivity and specificity. The proposed methodology can be applied to a wide range of conditions involving collagen re-modeling, such as in skin disorders, different types of fibrosis and muscular-skeletal diseases affecting ligaments and cartilage. PMID:23846580

Textural characteristics and sedimentary environment of sediment at eroded and deposited regions in the severely eroded coastline of Batu Pahat, Malaysia.

PubMed

Wan Mohtar, Wan Hanna Melini; Nawang, Siti Aminah Bassa; Abdul Maulud, Khairul Nizam; Benson, Yannie Anak; Azhary, Wan Ahmad Hafiz Wan Mohamed

2017-11-15

This study investigates the textural characteristics of sediments collected at eroded and deposited areas of highly severed eroded coastline of Batu Pahat, Malaysia. Samples were taken from systematically selected 23 locations along the 67km stretch of coastline and are extended to the fluvial sediments of the main river of Batu Pahat. Grain size distribution analysis was conducted to identify its textural characteristics and associated sedimentary transport behaviours. Sediments obtained along the coastline were fine-grained material with averaged mean size of 7.25 ϕ, poorly sorted, positively skewed and has wide distributions. Samples from eroded and deposition regions displayed no distinctive characteristics and exhibited similar profiles. The high energy condition transported the sediments as suspension, mostly as pelagic and the sediments were deposited as shallow marine and agitated deposits. The fluvial sediments of up to 3km into the river have particularly similar profile of textural characteristics with the neighbouring marine sediments from the river mouth. Profiles were similar with marine sediments about 3km opposite the main current and can go up to 10km along the current of Malacca Straits. Copyright © 2017 Elsevier B.V. All rights reserved.

Computer-aided mass detection in mammography: False positive reduction via gray-scale invariant ranklet texture features

DOE Office of Scientific and Technical Information (OSTI.GOV)

Masotti, Matteo; Lanconelli, Nico; Campanini, Renato

In this work, gray-scale invariant ranklet texture features are proposed for false positive reduction (FPR) in computer-aided detection (CAD) of breast masses. Two main considerations are at the basis of this proposal. First, false positive (FP) marks surviving our previous CAD system seem to be characterized by specific texture properties that can be used to discriminate them from masses. Second, our previous CAD system achieves invariance to linear/nonlinear monotonic gray-scale transformations by encoding regions of interest into ranklet images through the ranklet transform, an image transformation similar to the wavelet transform, yet dealing with pixels' ranks rather than with theirmore » gray-scale values. Therefore, the new FPR approach proposed herein defines a set of texture features which are calculated directly from the ranklet images corresponding to the regions of interest surviving our previous CAD system, hence, ranklet texture features; then, a support vector machine (SVM) classifier is used for discrimination. As a result of this approach, texture-based information is used to discriminate FP marks surviving our previous CAD system; at the same time, invariance to linear/nonlinear monotonic gray-scale transformations of the new CAD system is guaranteed, as ranklet texture features are calculated from ranklet images that have this property themselves by construction. To emphasize the gray-scale invariance of both the previous and new CAD systems, training and testing are carried out without any in-between parameters' adjustment on mammograms having different gray-scale dynamics; in particular, training is carried out on analog digitized mammograms taken from a publicly available digital database, whereas testing is performed on full-field digital mammograms taken from an in-house database. Free-response receiver operating characteristic (FROC) curve analysis of the two CAD systems demonstrates that the new approach achieves a higher reduction of FP marks when compared to the previous one. Specifically, at 60%, 65%, and 70% per-mammogram sensitivity, the new CAD system achieves 0.50, 0.68, and 0.92 FP marks per mammogram, whereas at 70%, 75%, and 80% per-case sensitivity it achieves 0.37, 0.48, and 0.71 FP marks per mammogram, respectively. Conversely, at the same sensitivities, the previous CAD system reached 0.71, 0.87, and 1.15 FP marks per mammogram, and 0.57, 0.73, and 0.92 FPs per mammogram. Also, statistical significance of the difference between the two per-mammogram and per-case FROC curves is demonstrated by the p-value<0.001 returned by jackknife FROC analysis performed on the two CAD systems.« less

Reconstructing White Walls: Multi-View Multi-Shot 3d Reconstruction of Textureless Surfaces

NASA Astrophysics Data System (ADS)

Ley, Andreas; Hänsch, Ronny; Hellwich, Olaf

2016-06-01

The reconstruction of the 3D geometry of a scene based on image sequences has been a very active field of research for decades. Nevertheless, there are still existing challenges in particular for homogeneous parts of objects. This paper proposes a solution to enhance the 3D reconstruction of weakly-textured surfaces by using standard cameras as well as a standard multi-view stereo pipeline. The underlying idea of the proposed method is based on improving the signal-to-noise ratio in weakly-textured regions while adaptively amplifying the local contrast to make better use of the limited numerical range in 8-bit images. Based on this premise, multiple shots per viewpoint are used to suppress statistically uncorrelated noise and enhance low-contrast texture. By only changing the image acquisition and adding a preprocessing step, a tremendous increase of up to 300% in completeness of the 3D reconstruction is achieved.

Extraction of texture features with a multiresolution neural network

NASA Astrophysics Data System (ADS)

Lepage, Richard; Laurendeau, Denis; Gagnon, Roger A.

1992-09-01

Texture is an important surface characteristic. Many industrial materials such as wood, textile, or paper are best characterized by their texture. Detection of defaults occurring on such materials or classification for quality control anD matching can be carried out through careful texture analysis. A system for the classification of pieces of wood used in the furniture industry is proposed. This paper is concerned with a neural network implementation of the features extraction and classification components of the proposed system. Texture appears differently depending at which spatial scale it is observed. A complete description of a texture thus implies an analysis at several spatial scales. We propose a compact pyramidal representation of the input image for multiresolution analysis. The feature extraction system is implemented on a multilayer artificial neural network. Each level of the pyramid, which is a representation of the input image at a given spatial resolution scale, is mapped into a layer of the neural network. A full resolution texture image is input at the base of the pyramid and a representation of the texture image at multiple resolutions is generated by the feedforward pyramid structure of the neural network. The receptive field of each neuron at a given pyramid level is preprogrammed as a discrete Gaussian low-pass filter. Meaningful characteristics of the textured image must be extracted if a good resolving power of the classifier must be achieved. Local dominant orientation is the principal feature which is extracted from the textured image. Local edge orientation is computed with a Sobel mask at four orientation angles (multiple of (pi) /4). The resulting intrinsic image, that is, the local dominant orientation image, is fed to the texture classification neural network. The classification network is a three-layer feedforward back-propagation neural network.

Determination of mango fruit from binary image using randomized Hough transform

NASA Astrophysics Data System (ADS)

Rizon, Mohamed; Najihah Yusri, Nurul Ain; Abdul Kadir, Mohd Fadzil; bin Mamat, Abd. Rasid; Abd Aziz, Azim Zaliha; Nanaa, Kutiba

2015-12-01

A method of detecting mango fruit from RGB input image is proposed in this research. From the input image, the image is processed to obtain the binary image using the texture analysis and morphological operations (dilation and erosion). Later, the Randomized Hough Transform (RHT) method is used to find the best ellipse fits to each binary region. By using the texture analysis, the system can detect the mango fruit that is partially overlapped with each other and mango fruit that is partially occluded by the leaves. The combination of texture analysis and morphological operator can isolate the partially overlapped fruit and fruit that are partially occluded by leaves. The parameters derived from RHT method was used to calculate the center of the ellipse. The center of the ellipse acts as the gripping point for the fruit picking robot. As the results, the rate of detection was up to 95% for fruit that is partially overlapped and partially covered by leaves.

Stereo-Based Region-Growing using String Matching

NASA Technical Reports Server (NTRS)

Mandelbaum, Robert; Mintz, Max

1995-01-01

We present a novel stereo algorithm based on a coarse texture segmentation preprocessing phase. Matching is performed using a string comparison. Matching sub-strings correspond to matching sequences of textures. Inter-scanline clustering of matching sub-strings yields regions of matching texture. The shape of these regions yield information concerning object's height, width and azimuthal position relative to the camera pair. Hence, rather than the standard dense depth map, the output of this algorithm is a segmentation of objects in the scene. Such a format is useful for the integration of stereo with other sensor modalities on a mobile robotic platform. It is also useful for localization; the height and width of a detected object may be used for landmark recognition, while depth and relative azimuthal location determine pose. The algorithm does not rely on the monotonicity of order of image primitives. Occlusions, exposures, and foreshortening effects are not problematic. The algorithm can deal with certain types of transparencies. It is computationally efficient, and very amenable to parallel implementation. Further, the epipolar constraints may be relaxed to some small but significant degree. A version of the algorithm has been implemented and tested on various types of images. It performs best on random dot stereograms, on images with easily filtered backgrounds (as in synthetic images), and on real scenes with uncontrived backgrounds.

An application of Chan-Vese method used to determine the ROI area in CT lung screening

NASA Astrophysics Data System (ADS)

Prokop, Paweł; Surtel, Wojciech

2016-09-01

The article presents two approaches of determining the ROI area in CT lung screening. First approach is based on a classic method of framing the image in order to determine the ROI by using a MaZda tool. Second approach is based on segmentation of CT images of the lungs and reducing the redundant information from the image. Of the two approaches of an Active Contour, it was decided to choose the Chan-Vese method. In order to determine the effectiveness of the approach, it was performed an analysis of received ROI texture and extraction of textural features. In order to determine the effectiveness of the method, it was performed an analysis of the received ROI textures and extraction of the texture features, by using a Mazda tool. The results were compared and presented in the form of the radar graphs. The second approach proved to be effective and appropriate and consequently it is used for further analysis of CT images, in the computer-aided diagnosis of sarcoidosis.

A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon.

PubMed

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E; Moran, Emilio

2008-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.

A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon

PubMed Central

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E.; Moran, Emilio

2009-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin. PMID:19789716

Efficient video-equipped fire detection approach for automatic fire alarm systems

NASA Astrophysics Data System (ADS)

Kang, Myeongsu; Tung, Truong Xuan; Kim, Jong-Myon

2013-01-01

This paper proposes an efficient four-stage approach that automatically detects fire using video capabilities. In the first stage, an approximate median method is used to detect video frame regions involving motion. In the second stage, a fuzzy c-means-based clustering algorithm is employed to extract candidate regions of fire from all of the movement-containing regions. In the third stage, a gray level co-occurrence matrix is used to extract texture parameters by tracking red-colored objects in the candidate regions. These texture features are, subsequently, used as inputs of a back-propagation neural network to distinguish between fire and nonfire. Experimental results indicate that the proposed four-stage approach outperforms other fire detection algorithms in terms of consistently increasing the accuracy of fire detection in both indoor and outdoor test videos.

Relationship between trabecular texture features of CT images and an amount of bone cement volume injection in percutaneous vertebroplasty

NASA Astrophysics Data System (ADS)

Tack, Gye Rae; Choi, Hyung Guen; Shin, Kyu-Chul; Lee, Sung J.

2001-06-01

Percutaneous vertebroplasty is a surgical procedure that was introduced for the treatment of compression fracture of the vertebrae. This procedure includes puncturing vertebrae and filling with polymethylmethacrylate (PMMA). Recent studies have shown that the procedure could provide structural reinforcement for the osteoporotic vertebrae while being minimally invasive and safe with immediate pain relief. However, treatment failures due to disproportionate PMMA volume injection have been reported as one of complications in vertebroplasty. It is believed that control of PMMA volume is one of the most critical factors that can reduce the incidence of complications. In this study, appropriate amount of PMMA volume was assessed based on the imaging data of a given patient under the following hypotheses: (1) a relationship can be drawn between the volume of PMMA injection and textural features of the trabecular bone in preoperative CT images and (2) the volume of PMMA injection can be estimated based on 3D reconstruction of postoperative CT images. Gray-level run length analysis was used to determine the textural features of the trabecular bone. The width of trabecular (T-texture) and the width of intertrabecular spaces (I-texture) were calculated. The correlation between PMMA volume and textural features of patient's CT images was also examined to evaluate the appropriate PMMA amount. Results indicated that there was a strong correlation between the actual PMMA injection volume and the area of the intertrabecular space and that of trabecular bone calculated from the CT image (correlation coefficient, requals0.96 and requals-0.95, respectively). T- texture (requals-0.93) did correlate better with the actual PMMA volume more than the I-texture (requals0.57). Therefore, it was demonstrated that appropriate PMMA injection volume could be predicted based on the textural analysis for better clinical management of the osteoporotic spine.

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

PubMed Central

Pérez-Beteta, Julián; Luque, Belén; Arregui, Elena; Calvo, Manuel; Borrás, José M; López, Carlos; Martino, Juan; Velasquez, Carlos; Asenjo, Beatriz; Benavides, Manuel; Herruzo, Ismael; Martínez-González, Alicia; Pérez-Romasanta, Luis; Arana, Estanislao; Pérez-García, Víctor M

2016-01-01

Objective: The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. Methods: 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan–Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient. Results: Kaplan–Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. Conclusion: Heterogeneity measures computed on the post-contrast pre-operative T1 weighted MR images of patients with GBM are predictors of survival. Advances in knowledge: Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour. PMID:27319577

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival.

PubMed

Molina, David; Pérez-Beteta, Julián; Luque, Belén; Arregui, Elena; Calvo, Manuel; Borrás, José M; López, Carlos; Martino, Juan; Velasquez, Carlos; Asenjo, Beatriz; Benavides, Manuel; Herruzo, Ismael; Martínez-González, Alicia; Pérez-Romasanta, Luis; Arana, Estanislao; Pérez-García, Víctor M

2016-07-04

The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T 1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan-Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient. Kaplan-Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. Heterogeneity measures computed on the post-contrast pre-operative T 1 weighted MR images of patients with GBM are predictors of survival. Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour.

Differentiating the grades of thymic epithelial tumor malignancy using textural features of intratumoral heterogeneity via (18)F-FDG PET/CT.

PubMed

Lee, Hyo Sang; Oh, Jungsu S; Park, Young Soo; Jang, Se Jin; Choi, Ik Soo; Ryu, Jin-Sook

2016-05-01

We aimed to explore the ability of textural heterogeneity indices determined by (18)F-FDG PET/CT for grading the malignancy of thymic epithelial tumors (TETs). We retrospectively enrolled 47 patients with pathologically proven TETs who underwent pre-treatment (18)F-FDG PET/CT. TETs were classified by pathological results into three subgroups with increasing grades of malignancy: low-risk thymoma (LRT; WHO classification A, AB and B1), high-risk thymoma (B2 and B3), and thymic carcinoma (TC). Using (18)F-FDG PET/CT, we obtained conventional imaging indices including SUVmax and 20 intratumoral heterogeneity indices: i.e., four local-scale indices derived from the neighborhood gray-tone difference matrix (NGTDM), eight regional-scale indices from the gray-level run-length matrix (GLRLM), and eight regional-scale indices from the gray-level size zone matrix (GLSZM). Area under the receiver operating characteristic curve (AUC) was used to demonstrate the abilities of the imaging indices for differentiating subgroups. Multivariable logistic regression analysis was performed to show the independent significance of the textural indices. Combined criteria using optimal cutoff values of the SUVmax and a best-performing heterogeneity index were applied to investigate whether they improved differentiation between the subgroups. Most of the GLRLM and GLSZM indices and the SUVmax showed good or fair discrimination (AUC >0.7) with best performance for some of the GLRLM indices and the SUVmax, whereas the NGTDM indices showed relatively inferior performance. The discriminative ability of some of the GLSZM indices was independent from that of SUVmax in multivariate analysis. Combined use of the SUVmax and a GLSZM index improved positive predictive values for LRT and TC. Texture analysis of (18)F-FDG PET/CT scans has the potential to differentiate between TET tumor grades; regional-scale indices from GLRLM and GLSZM perform better than local-scale indices from the NGTDM. The SUVmax and heterogeneity indices may have complementary value in differentiating TET subgroups.

Physicochemical and sensory properties of fresh potato-based pasta (gnocchi).

PubMed

Alessandrini, Laura; Balestra, Federica; Romani, Santina; Rocculi, Pietro; Rosa, Marco Dalla

2010-01-01

This study dealt with the characterization and quality assessment of 3 kinds of potato-based pasta (gnocchi) made with steam-cooked, potato puree (water added to potato flakes), and reconstituted potatoes as main ingredients. The aim of the research was to evaluate the quality of the products in terms of physicochemical, textural, and sensory characteristics. Water content, water activity, color (L* and h°), and texture (texture profile analysis [TPA] and shearing test) were evaluated on both raw and cooked samples. In addition, on the recovered cooking water the loss of solid substances was determined and on the cooked gnocchi a sensory assessment was performed. Eight sensory attributes (yellowness, hardness, gumminess, adhesiveness, potato taste, sweet taste, flour taste, and sapidity) were investigated. Statistically significant differences among products were obtained, especially concerning textural properties. In fact, sample made with reconstituted potatoes and emulsifiers resulted the hardest (8.53 ± 1.22 N), the gummiest (2.90 ± 0.05 N), and the "chewiest" (2.90 ± 0.58 N) after cooking. Gnocchi made with potato puree or reconstituted potatoes significantly differed from the one produced with steam-cooked potatoes in terms of sensory properties (yellowness, hardness, flour taste, and sapidity). Pearson's correlation analysis between some textural instrumental and sensory parameters showed significant correlation coefficients (0.532 < r < 0.810). Score plot of principal component analysis (PCA) confirmed obtained results from physicochemical and sensory analyses, in terms of high discriminant capacity of colorimetric and textural characteristics. © 2010 Institute of Food Technologists®

Computer-aided Assessment of Regional Abdominal Fat with Food Residue Removal in CT

PubMed Central

Makrogiannis, Sokratis; Caturegli, Giorgio; Davatzikos, Christos; Ferrucci, Luigi

2014-01-01

Rationale and Objectives Separate quantification of abdominal subcutaneous and visceral fat regions is essential to understand the role of regional adiposity as risk factor in epidemiological studies. Fat quantification is often based on computed tomography (CT) because fat density is distinct from other tissue densities in the abdomen. However, the presence of intestinal food residues with densities similar to fat may reduce fat quantification accuracy. We introduce an abdominal fat quantification method in CT with interest in food residue removal. Materials and Methods Total fat was identified in the feature space of Hounsfield units and divided into subcutaneous and visceral components using model-based segmentation. Regions of food residues were identified and removed from visceral fat using a machine learning method integrating intensity, texture, and spatial information. Cost-weighting and bagging techniques were investigated to address class imbalance. Results We validated our automated food residue removal technique against semimanual quantifications. Our feature selection experiments indicated that joint intensity and texture features produce the highest classification accuracy at 95%. We explored generalization capability using k-fold cross-validation and receiver operating characteristic (ROC) analysis with variable k. Losses in accuracy and area under ROC curve between maximum and minimum k were limited to 0.1% and 0.3%. We validated tissue segmentation against reference semimanual delineations. The Dice similarity scores were as high as 93.1 for subcutaneous fat and 85.6 for visceral fat. Conclusions Computer-aided regional abdominal fat quantification is a reliable computational tool for large-scale epidemiological studies. Our proposed intestinal food residue reduction scheme is an original contribution of this work. Validation experiments indicate very good accuracy and generalization capability. PMID:24119354

Computer-aided assessment of regional abdominal fat with food residue removal in CT.

PubMed

Makrogiannis, Sokratis; Caturegli, Giorgio; Davatzikos, Christos; Ferrucci, Luigi

2013-11-01

Separate quantification of abdominal subcutaneous and visceral fat regions is essential to understand the role of regional adiposity as risk factor in epidemiological studies. Fat quantification is often based on computed tomography (CT) because fat density is distinct from other tissue densities in the abdomen. However, the presence of intestinal food residues with densities similar to fat may reduce fat quantification accuracy. We introduce an abdominal fat quantification method in CT with interest in food residue removal. Total fat was identified in the feature space of Hounsfield units and divided into subcutaneous and visceral components using model-based segmentation. Regions of food residues were identified and removed from visceral fat using a machine learning method integrating intensity, texture, and spatial information. Cost-weighting and bagging techniques were investigated to address class imbalance. We validated our automated food residue removal technique against semimanual quantifications. Our feature selection experiments indicated that joint intensity and texture features produce the highest classification accuracy at 95%. We explored generalization capability using k-fold cross-validation and receiver operating characteristic (ROC) analysis with variable k. Losses in accuracy and area under ROC curve between maximum and minimum k were limited to 0.1% and 0.3%. We validated tissue segmentation against reference semimanual delineations. The Dice similarity scores were as high as 93.1 for subcutaneous fat and 85.6 for visceral fat. Computer-aided regional abdominal fat quantification is a reliable computational tool for large-scale epidemiological studies. Our proposed intestinal food residue reduction scheme is an original contribution of this work. Validation experiments indicate very good accuracy and generalization capability. Published by Elsevier Inc.

Aboveground biomass mapping of African forest mosaics using canopy texture analysis: toward a regional approach.

PubMed

Bastin, Jean-François; Barbier, Nicolas; Couteron, Pierre; Adams, Benoît; Shapiro, Aurélie; Bogaert, Jan; De Cannière, Charles

In the context of the reduction of greenhouse gas emissions caused by deforestation and forest degradation (the REDD+ program), optical very high resolution (VHR) satellite images provide an opportunity to characterize forest canopy structure and to quantify aboveground biomass (AGB) at less expense than methods based on airborne remote sensing data. Among the methods for processing these VHR images, Fourier textural ordination (FOTO) presents a good method to detect forest canopy structural heterogeneity and therefore to predict AGB variations. Notably, the method does not saturate at intermediate AGB values as do pixelwise processing of available space borne optical and radar signals. However, a regional-scale application requires overcoming two difficulties: (1) instrumental effects due to variations in sun–scene–sensor geometry or sensor-specific responses that preclude the use of wide arrays of images acquired under heterogeneous conditions and (2) forest structural diversity including monodominant or open canopy forests, which are of particular importance in Central Africa. In this study, we demonstrate the feasibility of a rigorous regional study of canopy texture by harmonizing FOTO indices of images acquired from two different sensors (Geoeye-1 and QuickBird-2) and different sun–scene–sensor geometries and by calibrating a piecewise biomass inversion model using 26 inventory plots (1 ha) sampled across very heterogeneous forest types. A good agreement was found between observed and predicted AGB (residual standard error [RSE] = 15%; R2 = 0.85; P < 0.001) across a wide range of AGB levels from 26 Mg/ha to 460 Mg/ha, and was confirmed by cross validation. A high-resolution biomass map (100-m pixels) was produced for a 400-km2 area, and predictions obtained from both imagery sources were consistent with each other (r = 0.86; slope = 1.03; intercept = 12.01 Mg/ha). These results highlight the horizontal structure of forest canopy as a powerful descriptor of the entire forest stand structure and heterogeneity. In particular, we show that quantitative metrics resulting from such textural analysis offer new opportunities to characterize the spatial and temporal variation of the structure of dense forests and may complement the toolbox used by tropical forest ecologists, managers or REDD+ national monitoring, reporting and verification bodies.

Measurement, modeling and perception of painted surfaces: A Multi-scale Analysis of the Touch-up Problem

NASA Astrophysics Data System (ADS)

Kalghatgi, Suparna Kishore

Real-world surfaces typically have geometric features at a range of spatial scales. At the microscale, opaque surfaces are often characterized by bidirectional reflectance distribution functions (BRDF), which describes how a surface scatters incident light. At the mesoscale, surfaces often exhibit visible texture -- stochastic or patterned arrangements of geometric features that provide visual information about surface properties such as roughness, smoothness, softness, etc. These textures also affect how light is scattered by the surface, but the effects are at a different spatial scale than those captured by the BRDF. Through this research, we investigate how microscale and mesoscale surface properties interact to contribute to overall surface appearance. This behavior is also the cause of the well-known "touch-up problem" in the paint industry, where two regions coated with exactly the same paint, look different in color, gloss and/or texture because of differences in application methods. At first, samples were created by applying latex paint to standard wallboard surfaces. Two application methods- spraying and rolling were used. The BRDF and texture properties of the samples were measured, which revealed differences at both the microscale and mesoscale. This data was then used as input for a physically-based image synthesis algorithm, to generate realistic images of the surfaces under different viewing conditions. In order to understand the factors that govern touch-up visibility, psychophysical tests were conducted using calibrated, digital photographs of the samples as stimuli. Images were presented in pairs and a two alternative forced choice design was used for the experiments. These judgments were then used as data for a Thurstonian scaling analysis to produce psychophysical scales of visibility, which helped determine the effect of paint formulation, application methods, and viewing and illumination conditions on the touch-up problem. The results can be used as base data towards development of a psychophysical model that relates physical differences in paint formulation and application methods to visual differences in surface appearance.

Methodology of remote sensing data interpretation and geological applications. [Brazil

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Veneziani, P.; Dosanjos, C. E.

1982-01-01

Elements of photointerpretation discussed include the analysis of photographic texture and structure as well as film tonality. The method used is based on conventional techniques developed for interpreting aerial black and white photographs. By defining the properties which characterize the form and individuality of dual images, homologous zones can be identified. Guy's logic method (1966) was adapted and used on functions of resolution, scale, and spectral characteristics of remotely sensed products. Applications of LANDSAT imagery are discussed for regional geological mapping, mineral exploration, hydrogeology, and geotechnical engineering in Brazil.

Scalable fabrication of strongly textured organic semiconductor micropatterns by capillary force lithography.

PubMed

Jo, Pil Sung; Vailionis, Arturas; Park, Young Min; Salleo, Alberto

2012-06-26

Strongly textured organic semiconductor micropatterns made of the small molecule dioctylbenzothienobenzothiophene (C(8)-BTBT) are fabricated by using a method based on capillary force lithography (CFL). This technique provides the C(8)-BTBT solution with nucleation sites for directional growth, and can be used as a scalable way to produce high quality crystalline arrays in desired regions of a substrate for OFET applications. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Modern methods of experimental construction of texture complete direct pole figures by using X-ray data

NASA Astrophysics Data System (ADS)

Isaenkova, M.; Perlovich, Yu; Fesenko, V.

2016-04-01

Currently used methods for constructing texture complete direct pole figure (CDPF) based on the results of X-ray diffractometric measurements were considered with respect to the products of Zr-based alloys and, in particular, used in a nuclear reactor cladding tubes, for which the accuracy of determination of integral texture parameters is of the especial importance. The main attention was devoted to technical issues which are solved by means of computer processing of large arrays of obtained experimental data. Among considered questions there are amendments of the defocusing, techniques for constructing of complete direct pole figures and determination of integral textural parameters. The methods of reconstruction of complete direct pole figures by partial direct pole figures recorded up to tilt angles of sample ψ=70-80°: the method of extrapolation of data to an uninvestigated region of the stereographic projection, and the method of "sewing" of partial pole figures measured for three mutually perpendicular plane sections of the product. The limits of applicability of these methods, depending on the shape of the test product and the degree of inhomogeneity of the layer-by-layer texture, were revealed. On the basis of a large number of experimental data, the accuracy of the integral parameters used for calculation of the physical and mechanical properties of metals with a hexagonal crystal structure was found to be equal to 0.02, when taking into account the texture heterogeneity of regular products from Zr-based alloys.

Decoupling of superposed textures in an electrically biased piezoceramic with a 100 preferred orientation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fancher, Chris M.; Blendell, John E.; Bowman, Keith J.

2017-02-07

A method leveraging Rietveld full-pattern texture analysis to decouple induced domain texture from a preferred grain orientation is presented in this paper. The proposed method is demonstrated by determining the induced domain texture in a polar polymorph of 100 oriented 0.91Bi 1/2Na 1/2TiO 3-0.07BaTiO 3-0.02K 0.5Na 0.5NbO 3. Domain textures determined using the present method are compared with results obtained via single peak fitting. Texture determined using single peak fitting estimated more domain alignment than that determined using the Rietveld based method. These results suggest that the combination of grain texture and phase transitions can lead to single peak fittingmore » under or over estimating domain texture. Finally, while demonstrated for a bulk piezoelectric, the proposed method can be applied to quantify domain textures in multi-component systems and thin films.« less

Characterization of microstructure and texture across dissimilar super duplex/austenitic stainless steel weldment joint by super duplex filler metal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eghlimi, Abbas, E-mail: a.eghlimi@ma.iut.ac.ir; Shamanian, Morteza; Eskandarian, Masoomeh

In the present paper, microstructural changes across an as-welded dissimilar austenitic/duplex stainless steel couple welded by a super duplex stainless steel filler metal using gas tungsten arc welding process is characterized with optical microscopy and electron back-scattered diffraction techniques. Accordingly, variations of microstructure, texture, and grain boundary character distribution of base metals, heat affected zones, and weld metal were investigated. The results showed that the weld metal, which was composed of Widmanstätten austenite side-plates and allotriomorphic grain boundary austenite morphologies, had the weakest texture and was dominated by low angle boundaries. The welding process increased the ferrite content but decreasedmore » the texture intensity at the heat affected zone of the super duplex stainless steel base metal. In addition, through partial ferritization, it changed the morphology of elongated grains of the rolled microstructure to twinned partially transformed austenite plateaus scattered between ferrite textured colonies. However, the texture of the austenitic stainless steel heat affected zone was strengthened via encouraging recrystallization and formation of annealing twins. At both interfaces, an increase in the special character coincident site lattice boundaries of the primary phase as well as a strong texture with <100> orientation, mainly of Goss component, was observed. - Graphical abstract: Display Omitted - Highlights: • Weld metal showed local orientation at microscale but random texture at macroscale. • Intensification of <100> orientated grains was observed adjacent to the fusion lines. • The austenite texture was weaker than that of the ferrite in all duplex regions. • Welding caused twinned partially transformed austenites to form at SDSS HAZ. • At both interfaces, the ratio of special CSL boundaries of the primary phase increased.« less

Estimating local scaling properties for the classification of interstitial lung disease patterns

NASA Astrophysics Data System (ADS)

Huber, Markus B.; Nagarajan, Mahesh B.; Leinsinger, Gerda; Ray, Lawrence A.; Wismueller, Axel

2011-03-01

Local scaling properties of texture regions were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honeycombing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and the estimation of local scaling properties with Scaling Index Method (SIM). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions including the Bonferroni correction. The best classification results were obtained by the set of SIM features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers with the highest accuracy (94.1%, 93.7%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced texture features using local scaling properties can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

Texture feature extraction based on a uniformity estimation method for local brightness and structure in chest CT images.

PubMed

Peng, Shao-Hu; Kim, Deok-Hwan; Lee, Seok-Lyong; Lim, Myung-Kwan

2010-01-01

Texture feature is one of most important feature analysis methods in the computer-aided diagnosis (CAD) systems for disease diagnosis. In this paper, we propose a Uniformity Estimation Method (UEM) for local brightness and structure to detect the pathological change in the chest CT images. Based on the characteristics of the chest CT images, we extract texture features by proposing an extension of rotation invariant LBP (ELBP(riu4)) and the gradient orientation difference so as to represent a uniform pattern of the brightness and structure in the image. The utilization of the ELBP(riu4) and the gradient orientation difference allows us to extract rotation invariant texture features in multiple directions. Beyond this, we propose to employ the integral image technique to speed up the texture feature computation of the spatial gray level dependent method (SGLDM). Copyright © 2010 Elsevier Ltd. All rights reserved.

Fusion method of SAR and optical images for urban object extraction

NASA Astrophysics Data System (ADS)

Jia, Yonghong; Blum, Rick S.; Li, Fangfang

2007-11-01

A new image fusion method of SAR, Panchromatic (Pan) and multispectral (MS) data is proposed. First of all, SAR texture is extracted by ratioing the despeckled SAR image to its low pass approximation, and is used to modulate high pass details extracted from the available Pan image by means of the á trous wavelet decomposition. Then, high pass details modulated with the texture is applied to obtain the fusion product by HPFM (High pass Filter-based Modulation) fusion method. A set of image data including co-registered Landsat TM, ENVISAT SAR and SPOT Pan is used for the experiment. The results demonstrate accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where SAR texture information enhances the fusion product, and the proposed approach is effective for image interpret and classification.

How visual attention is modified by disparities and textures changes?

NASA Astrophysics Data System (ADS)

Khaustova, Dar'ya; Fournier, Jérome; Wyckens, Emmanuel; Le Meur, Olivier

2013-03-01

The 3D image/video quality of experience is a multidimensional concept that depends on 2D image quality, depth quantity and visual comfort. The relationship between these parameters is not yet clearly defined. From this perspective, we aim to understand how texture complexity, depth quantity and visual comfort influence the way people observe 3D content in comparison with 2D. Six scenes with different structural parameters were generated using Blender software. For these six scenes, the following parameters were modified: texture complexity and the amount of depth changing the camera baseline and the convergence distance at the shooting side. Our study was conducted using an eye-tracker and a 3DTV display. During the eye-tracking experiment, each observer freely examined images with different depth levels and texture complexities. To avoid memory bias, we ensured that each observer had only seen scene content once. Collected fixation data were used to build saliency maps and to analyze differences between 2D and 3D conditions. Our results show that the introduction of disparity shortened saccade length; however fixation durations remained unaffected. An analysis of the saliency maps did not reveal any differences between 2D and 3D conditions for the viewing duration of 20 s. When the whole period was divided into smaller intervals, we found that for the first 4 s the introduced disparity was conducive to the section of saliency regions. However, this contribution is quite minimal if the correlation between saliency maps is analyzed. Nevertheless, we did not find that discomfort (comfort) had any influence on visual attention. We believe that existing metrics and methods are depth insensitive and do not reveal such differences. Based on the analysis of heat maps and paired t-tests of inter-observer visual congruency values we deduced that the selected areas of interest depend on texture complexities.

2011 IEEE Visualization Contest winner: Visualizing unsteady vortical behavior of a centrifugal pump.

PubMed

Otto, Mathias; Kuhn, Alexander; Engelke, Wito; Theisel, Holger

2012-01-01

In the 2011 IEEE Visualization Contest, the dataset represented a high-resolution simulation of a centrifugal pump operating below optimal speed. The goal was to find suitable visualization techniques to identify regions of rotating stall that impede the pump's effectiveness. The winning entry split analysis of the pump into three parts based on the pump's functional behavior. It then applied local and integration-based methods to communicate the unsteady flow behavior in different regions of the dataset. This research formed the basis for a comparison of common vortex extractors and more recent methods. In particular, integration-based methods (separation measures, accumulated scalar fields, particle path lines, and advection textures) are well suited to capture the complex time-dependent flow behavior. This video (http://youtu.be/oD7QuabY0oU) shows simulations of unsteady flow in a centrifugal pump.

A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Tseng, Tzu-Liang B.; Zheng, Bin; Zhang, Jianying; Qian, Wei

2015-03-01

A novel breast cancer risk analysis approach is proposed for enhancing performance of computerized breast cancer risk analysis using bilateral mammograms. Based on the intensity of breast area, five different sub-regions were acquired from one mammogram, and bilateral features were extracted from every sub-region. Our dataset includes 180 bilateral mammograms from 180 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including sub-region segmentation, bilateral feature extraction, feature selection, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under the curve (AUC) is 0.763 ± 0.021 when applying the multiple sub-region features to our testing dataset. The positive predictive value and the negative predictive value were 0.60 and 0.73, respectively. The study demonstrates that (1) features extracted from multiple sub-regions can improve the performance of our scheme compared to using features from whole breast area only; (2) a classifier using asymmetry bilateral features can effectively predict breast cancer risk; (3) incorporating texture and morphological features with density features can boost the classification accuracy.

Effect of Irregularity in Shape and Boundary of a Macro-Texture Region in Titanium (Postprint)

DTIC Science & Technology

2015-10-15

AFRL-RX-WP-JA-2016-0328 EFFECT OF IRREGULARITY IN SHAPE AND BOUNDARY OF A MACRO-TEXTURE REGION IN TITANIUM (POSTPRINT) James L...2 October 2014 – 15 September 2015 4. TITLE AND SUBTITLE EFFECT OF IRREGULARITY IN SHAPE AND BOUNDARY OF A MACRO-TEXTURE REGION IN TITANIUM ...aerospace grade titanium alloy material are measured to be about the same level as corner trapped shear wave signals. In addition to the abnormally high

Deciphering the genetic control of fruit texture in apple by multiple family-based analysis and genome-wide association

PubMed Central

Di Guardo, Mario; Bink, Marco C.A.M.; Guerra, Walter; Letschka, Thomas; Lozano, Lidia; Busatto, Nicola; Poles, Lara; Tadiello, Alice; Bianco, Luca; Visser, Richard G.F.; van de Weg, Eric

2017-01-01

Abstract Fruit texture is a complex feature composed of mechanical and acoustic properties relying on the modifications occurring in the cell wall throughout fruit development and ripening. Apple is characterized by a large variation in fruit texture behavior that directly impacts both the consumer’s appreciation and post-harvest performance. To decipher the genetic control of fruit texture comprehensively, two complementing quantitative trait locus (QTL) mapping approaches were employed. The first was represented by a pedigree-based analysis (PBA) carried out on six full-sib pedigreed families, while the second was a genome-wide association study (GWAS) performed on a collection of 233 apple accessions. Both plant materials were genotyped with a 20K single nucleotide polymorphism (SNP) array and phenotyped with a sophisticated high-resolution texture analyzer. The overall QTL results indicated the fundamental role of chromosome 10 in controlling the mechanical properties, while chromosomes 2 and 14 were more associated with the acoustic response. The latter QTL, moreover, showed a consistent relationship between the QTL-estimated genotypes and the acoustic performance assessed among seedlings. The in silico annotation of these intervals revealed interesting candidate genes potentially involved in fruit texture regulation, as suggested by the gene expression profile. The joint integration of these approaches sheds light on the specific control of fruit texture, enabling important genetic information to assist in the selection of valuable fruit quality apple varieties. PMID:28338805

Deciphering the genetic control of fruit texture in apple by multiple family-based analysis and genome-wide association.

PubMed

Di Guardo, Mario; Bink, Marco C A M; Guerra, Walter; Letschka, Thomas; Lozano, Lidia; Busatto, Nicola; Poles, Lara; Tadiello, Alice; Bianco, Luca; Visser, Richard G F; van de Weg, Eric; Costa, Fabrizio

2017-03-01

Fruit texture is a complex feature composed of mechanical and acoustic properties relying on the modifications occurring in the cell wall throughout fruit development and ripening. Apple is characterized by a large variation in fruit texture behavior that directly impacts both the consumer's appreciation and post-harvest performance. To decipher the genetic control of fruit texture comprehensively, two complementing quantitative trait locus (QTL) mapping approaches were employed. The first was represented by a pedigree-based analysis (PBA) carried out on six full-sib pedigreed families, while the second was a genome-wide association study (GWAS) performed on a collection of 233 apple accessions. Both plant materials were genotyped with a 20K single nucleotide polymorphism (SNP) array and phenotyped with a sophisticated high-resolution texture analyzer. The overall QTL results indicated the fundamental role of chromosome 10 in controlling the mechanical properties, while chromosomes 2 and 14 were more associated with the acoustic response. The latter QTL, moreover, showed a consistent relationship between the QTL-estimated genotypes and the acoustic performance assessed among seedlings. The in silico annotation of these intervals revealed interesting candidate genes potentially involved in fruit texture regulation, as suggested by the gene expression profile. The joint integration of these approaches sheds light on the specific control of fruit texture, enabling important genetic information to assist in the selection of valuable fruit quality apple varieties. © The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Remote sensing of selective logging in Amazonia Assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis.

Treesearch

Gregory P. Asner; Michael Keller; Rodrigo Pereira; Johan C. Zweede

2002-01-01

We combined a detailed field study of forest canopy damage with calibrated Landsat 7 Enhanced Thematic Mapper Plus (ETM+) reflectance data and texture analysis to assess the sensitivity of basic broadband optical remote sensing to selective logging in Amazonia. Our field study encompassed measurements of ground damage and canopy gap fractions along a chronosequence of...

Dynamic texture recognition using local binary patterns with an application to facial expressions.

PubMed

Zhao, Guoying; Pietikäinen, Matti

2007-06-01

Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation.

Bone marrow cavity segmentation using graph-cuts with wavelet-based texture feature.

PubMed

Shigeta, Hironori; Mashita, Tomohiro; Kikuta, Junichi; Seno, Shigeto; Takemura, Haruo; Ishii, Masaru; Matsuda, Hideo

2017-10-01

Emerging bioimaging technologies enable us to capture various dynamic cellular activities [Formula: see text]. As large amounts of data are obtained these days and it is becoming unrealistic to manually process massive number of images, automatic analysis methods are required. One of the issues for automatic image segmentation is that image-taking conditions are variable. Thus, commonly, many manual inputs are required according to each image. In this paper, we propose a bone marrow cavity (BMC) segmentation method for bone images as BMC is considered to be related to the mechanism of bone remodeling, osteoporosis, and so on. To reduce manual inputs to segment BMC, we classified the texture pattern using wavelet transformation and support vector machine. We also integrated the result of texture pattern classification into the graph-cuts-based image segmentation method because texture analysis does not consider spatial continuity. Our method is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The proposed method with graph-cuts and texture pattern classification performs well without manual inputs by a user.

Cloud and surface textural features in polar regions

NASA Technical Reports Server (NTRS)

Welch, Ronald M.; Kuo, Kwo-Sen; Sengupta, Sailes K.

1990-01-01

The study examines the textural signatures of clouds, ice-covered mountains, solid and broken sea ice and floes, and open water. The textural features are computed from sum and difference histogram and gray-level difference vector statistics defined at various pixel displacement distances derived from Landsat multispectral scanner data. Polar cloudiness, snow-covered mountainous regions, solid sea ice, glaciers, and open water have distinguishable texture features. This suggests that textural measures can be successfully applied to the detection of clouds over snow-covered mountains, an ability of considerable importance for the modeling of snow-melt runoff. However, broken stratocumulus cloud decks and thin cirrus over broken sea ice remain difficult to distinguish texturally. It is concluded that even with high spatial resolution imagery, it may not be possible to distinguish broken stratocumulus and thin clouds from sea ice in the marginal ice zone using the visible channel textural features alone.

Texture Feature Extraction and Classification for Iris Diagnosis

NASA Astrophysics Data System (ADS)

Ma, Lin; Li, Naimin

Appling computer aided techniques in iris image processing, and combining occidental iridology with the traditional Chinese medicine is a challenging research area in digital image processing and artificial intelligence. This paper proposes an iridology model that consists the iris image pre-processing, texture feature analysis and disease classification. To the pre-processing, a 2-step iris localization approach is proposed; a 2-D Gabor filter based texture analysis and a texture fractal dimension estimation method are proposed for pathological feature extraction; and at last support vector machines are constructed to recognize 2 typical diseases such as the alimentary canal disease and the nerve system disease. Experimental results show that the proposed iridology diagnosis model is quite effective and promising for medical diagnosis and health surveillance for both hospital and public use.

Texture analysis of tissues in Gleason grading of prostate cancer

NASA Astrophysics Data System (ADS)

Alexandratou, Eleni; Yova, Dido; Gorpas, Dimitris; Maragos, Petros; Agrogiannis, George; Kavantzas, Nikolaos

2008-02-01

Prostate cancer is a common malignancy among maturing men and the second leading cause of cancer death in USA. Histopathological grading of prostate cancer is based on tissue structural abnormalities. Gleason grading system is the gold standard and is based on the organization features of prostatic glands. Although Gleason score has contributed on cancer prognosis and on treatment planning, its accuracy is about 58%, with this percentage to be lower in GG2, GG3 and GG5 grading. On the other hand it is strongly affected by "inter- and intra observer variations", making the whole process very subjective. Therefore, there is need for the development of grading tools based on imaging and computer vision techniques for a more accurate prostate cancer prognosis. The aim of this paper is the development of a novel method for objective grading of biopsy specimen in order to support histopathological prognosis of the tumor. This new method is based on texture analysis techniques, and particularly on Gray Level Co-occurrence Matrix (GLCM) that estimates image properties related to second order statistics. Histopathological images of prostate cancer, from Gleason grade2 to Gleason grade 5, were acquired and subjected to image texture analysis. Thirteen texture characteristics were calculated from this matrix as they were proposed by Haralick. Using stepwise variable selection, a subset of four characteristics were selected and used for the description and classification of each image field. The selected characteristics profile was used for grading the specimen with the multiparameter statistical method of multiple logistic discrimination analysis. The subset of these characteristics provided 87% correct grading of the specimens. The addition of any of the remaining characteristics did not improve significantly the diagnostic ability of the method. This study demonstrated that texture analysis techniques could provide valuable grading decision support to the pathologists, concerning prostate cancer prognosis.

"Textural analysis of multiparametric MRI detects transition zone prostate cancer".

PubMed

Sidhu, Harbir S; Benigno, Salvatore; Ganeshan, Balaji; Dikaios, Nikos; Johnston, Edward W; Allen, Clare; Kirkham, Alex; Groves, Ashley M; Ahmed, Hashim U; Emberton, Mark; Taylor, Stuart A; Halligan, Steve; Punwani, Shonit

2017-06-01

To evaluate multiparametric-MRI (mpMRI) derived histogram textural-analysis parameters for detection of transition zone (TZ) prostatic tumour. Sixty-seven consecutive men with suspected prostate cancer underwent 1.5T mpMRI prior to template-mapping-biopsy (TPM). Twenty-six men had 'significant' TZ tumour. Two radiologists in consensus matched TPM to the single axial slice best depicting tumour, or largest TZ diameter for those with benign histology, to define single-slice whole TZ-regions-of-interest (ROIs). Textural-parameter differences between single-slice whole TZ-ROI containing significant tumour versus benign/insignificant tumour were analysed using Mann Whitney U test. Diagnostic accuracy was assessed by receiver operating characteristic area under curve (ROC-AUC) analysis cross-validated with leave-one-out (LOO) analysis. ADC kurtosis was significantly lower (p < 0.001) in TZ containing significant tumour with ROC-AUC 0.80 (LOO-AUC 0.78); the difference became non-significant following exclusion of significant tumour from single-slice whole TZ-ROI (p = 0.23). T1-entropy was significantly lower (p = 0.004) in TZ containing significant tumour with ROC-AUC 0.70 (LOO-AUC 0.66) and was unaffected by excluding significant tumour from TZ-ROI (p = 0.004). Combining these parameters yielded ROC-AUC 0.86 (LOO-AUC 0.83). Textural features of the whole prostate TZ can discriminate significant prostatic cancer through reduced kurtosis of the ADC-histogram where significant tumour is included in TZ-ROI and reduced T1 entropy independent of tumour inclusion. • MR textural features of prostate transition zone may discriminate significant prostatic cancer. • Transition zone (TZ) containing significant tumour demonstrates a less peaked ADC histogram. • TZ containing significant tumour reveals higher post-contrast T1-weighted homogeneity. • The utility of MR texture analysis in prostate cancer merits further investigation.

Structural scene analysis and content-based image retrieval applied to bone age assessment

NASA Astrophysics Data System (ADS)

Fischer, Benedikt; Brosig, André; Deserno, Thomas M.; Ott, Bastian; Günther, Rolf W.

2009-02-01

Radiological bone age assessment is based on global or local image regions of interest (ROI), such as epiphyseal regions or the area of carpal bones. Usually, these regions are compared to a standardized reference and a score determining the skeletal maturity is calculated. For computer-assisted diagnosis, automatic ROI extraction is done so far by heuristic approaches. In this work, we apply a high-level approach of scene analysis for knowledge-based ROI segmentation. Based on a set of 100 reference images from the IRMA database, a so called structural prototype (SP) is trained. In this graph-based structure, the 14 phalanges and 5 metacarpal bones are represented by nodes, with associated location, shape, as well as texture parameters modeled by Gaussians. Accordingly, the Gaussians describing the relative positions, relative orientation, and other relative parameters between two nodes are associated to the edges. Thereafter, segmentation of a hand radiograph is done in several steps: (i) a multi-scale region merging scheme is applied to extract visually prominent regions; (ii) a graph/sub-graph matching to the SP robustly identifies a subset of the 19 bones; (iii) the SP is registered to the current image for complete scene-reconstruction (iv) the epiphyseal regions are extracted from the reconstructed scene. The evaluation is based on 137 images of Caucasian males from the USC hand atlas. Overall, an error rate of 32% is achieved, for the 6 middle distal and medial/distal epiphyses, 23% of all extractions need adjustments. On average 9.58 of the 14 epiphyseal regions were extracted successfully per image. This is promising for further use in content-based image retrieval (CBIR) and CBIR-based automatic bone age assessment.

Breast-implant texturing associated with delamination of capsular layers: A histological analysis of the double capsule phenomenon.

PubMed

Efanov, J I; Giot, J P; Fernandez, J; Danino, M A

2017-06-01

Macro-texturing of breast implants was developed with the double goal of improving implant stabilization within the breast cavity and decreasing the rate of capsular contractures. However, recent evidence suggests that double capsular formation, a potentially worrisome phenomenon associated with late seromas and biofilms, occurs with preponderance in macro-textured implants. Our objective was to analyze histologically different regions of double capsules to determine if they are more prone to mechanical movements. A prospective analysis including patients undergoing second-stage expander to definitive breast-implant reconstruction post-mastectomy was conducted after intraoperative identification of the double capsule phenomenon. Two samples were collected from each capsules around the implant, located centrally and laterally. The specimens were sent for histological analysis by the institution's pathologist. In total, 10 patients were identified intraoperatively with partial double capsule phenomenon. Among samples retrieved from the lateral aspect of the breast implant, all were associated with delamination and fractures in the collagen matrix of the double capsules. This phenomenon was not observed in any sample from the dome of the breast. Breast-implant macro-texturing plays an important role on delamination of capsules on lateral portions of the breast, which may have an etiologic role in double capsule formation. Manufacturing implants with macro-texturing on one side and smooth surface on the other could diminish mechanical shear forces responsible for these findings. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Cortical mechanisms for the segregation and representation of acoustic textures.

PubMed

Overath, Tobias; Kumar, Sukhbinder; Stewart, Lauren; von Kriegstein, Katharina; Cusack, Rhodri; Rees, Adrian; Griffiths, Timothy D

2010-02-10

Auditory object analysis requires two fundamental perceptual processes: the definition of the boundaries between objects, and the abstraction and maintenance of an object's characteristic features. Although it is intuitive to assume that the detection of the discontinuities at an object's boundaries precedes the subsequent precise representation of the object, the specific underlying cortical mechanisms for segregating and representing auditory objects within the auditory scene are unknown. We investigated the cortical bases of these two processes for one type of auditory object, an "acoustic texture," composed of multiple frequency-modulated ramps. In these stimuli, we independently manipulated the statistical rules governing (1) the frequency-time space within individual textures (comprising ramps with a given spectrotemporal coherence) and (2) the boundaries between textures (adjacent textures with different spectrotemporal coherences). Using functional magnetic resonance imaging, we show mechanisms defining boundaries between textures with different coherences in primary and association auditory cortices, whereas texture coherence is represented only in association cortex. Furthermore, participants' superior detection of boundaries across which texture coherence increased (as opposed to decreased) was reflected in a greater neural response in auditory association cortex at these boundaries. The results suggest a hierarchical mechanism for processing acoustic textures that is relevant to auditory object analysis: boundaries between objects are first detected as a change in statistical rules over frequency-time space, before a representation that corresponds to the characteristics of the perceived object is formed.

Ultrasound image texture processing for evaluating fatty liver in peripartal dairy cows

NASA Astrophysics Data System (ADS)

Amin, Viren R.; Bobe, Gerd; Young, Jerry; Ametaj, Burim; Beitz, Donald

2001-07-01

The objective of this work is to characterize the liver ultrasound texture as it changes in diffuse disease of fatty liver. This technology could allow non-invasive diagnosis of fatty liver, a major metabolic disorder in early lactation dairy cows. More than 100 liver biopsies were taken from fourteen dairy cows, as a part of the USDA-funded study for effects of glucagon on prevention and treatment of fatty liver. Up to nine liver biopsies were taken from each cow during peripartal period of seven weeks and total lipid content was determined chemically. Just before each liver biopsy was taken, ultrasonic B-mode images were digitally captured using a 3.5 or 5 MHz transducer. Effort was made to capture images that were non-blurred, void of large blood vessels and multiple echoes, and of consistent texture. From each image, a region-of-interest of size 100-by-100 pixels was processed. Texture parameters were calculated using algorithms such as first and second order statistics, 2D Fourier transformation, co-occurrence matrix, and gradient analysis. Many cows had normal liver (3% to 6% total lipid) and a few had developed fatty liver with total lipid up to 15%. The selected texture parameters showed consistent change with changing lipid content and could potentially be used to diagnose early fatty liver non-invasively. The approach of texture analysis algorithms and initial results on their potential in evaluating total lipid percentage is presented here.

Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level.

PubMed

Navarro, Pedro J; Fernández-Isla, Carlos; Alcover, Pedro María; Suardíaz, Juan

2016-07-27

This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL), based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS) calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon's entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed.

Brownian motion curve-based textural classification and its application in cancer diagnosis.

PubMed

Mookiah, Muthu Rama Krishnan; Shah, Pratik; Chakraborty, Chandan; Ray, Ajoy K

2011-06-01

To develop an automated diagnostic methodology based on textural features of the oral mucosal epithelium to discriminate normal and oral submucous fibrosis (OSF). A total of 83 normal and 29 OSF images from histopathologic sections of the oral mucosa are considered. The proposed diagnostic mechanism consists of two parts: feature extraction using Brownian motion curve (BMC) and design ofa suitable classifier. The discrimination ability of the features has been substantiated by statistical tests. An error back-propagation neural network (BPNN) is used to classify OSF vs. normal. In development of an automated oral cancer diagnostic module, BMC has played an important role in characterizing textural features of the oral images. Fisher's linear discriminant analysis yields 100% sensitivity and 85% specificity, whereas BPNN leads to 92.31% sensitivity and 100% specificity, respectively. In addition to intensity and morphology-based features, textural features are also very important, especially in histopathologic diagnosis of oral cancer. In view of this, a set of textural features are extracted using the BMC for the diagnosis of OSF. Finally, a textural classifier is designed using BPNN, which leads to a diagnostic performance with 96.43% accuracy. (Anal Quant

Automated classification of articular cartilage surfaces based on surface texture.

PubMed

Stachowiak, G P; Stachowiak, G W; Podsiadlo, P

2006-11-01

In this study the automated classification system previously developed by the authors was used to classify articular cartilage surfaces with different degrees of wear. This automated system classifies surfaces based on their texture. Plug samples of sheep cartilage (pins) were run on stainless steel discs under various conditions using a pin-on-disc tribometer. Testing conditions were specifically designed to produce different severities of cartilage damage due to wear. Environmental scanning electron microscope (SEM) (ESEM) images of cartilage surfaces, that formed a database for pattern recognition analysis, were acquired. The ESEM images of cartilage were divided into five groups (classes), each class representing different wear conditions or wear severity. Each class was first examined and assessed visually. Next, the automated classification system (pattern recognition) was applied to all classes. The results of the automated surface texture classification were compared to those based on visual assessment of surface morphology. It was shown that the texture-based automated classification system was an efficient and accurate method of distinguishing between various cartilage surfaces generated under different wear conditions. It appears that the texture-based classification method has potential to become a useful tool in medical diagnostics.

Differentiating benign from malignant mediastinal lymph nodes visible at EBUS using grey-scale textural analysis.

PubMed

Edey, Anthony J; Pollentine, Adrian; Doody, Claire; Medford, Andrew R L

2015-04-01

Recent data suggest that grey-scale textural analysis on endobronchial ultrasound (EBUS) imaging can differentiate benign from malignant lymphadenopathy. The objective of studies was to evaluate grey-scale textural analysis and examine its clinical utility. Images from 135 consecutive clinically indicated EBUS procedures were evaluated retrospectively using MATLAB software (MathWorks, Natick, MA, USA). Manual node mapping was performed to obtain a region of interest and grey-scale textural features (range of pixel values and entropy) were analysed. The initial analysis involved 94 subjects and receiver operating characteristic (ROC) curves were generated. The ROC thresholds were then applied on a second cohort (41 subjects) to validate the earlier findings. A total of 371 images were evaluated. There was no difference in proportions of malignant disease (56% vs 53%, P = 0.66) in the prediction (group 1) and validation (group 2) sets. There was no difference in range of pixel values in group 1 but entropy was significantly higher in the malignant group (5.95 vs 5.77, P = 0.03). Higher entropy was seen in adenocarcinoma versus lymphoma (6.00 vs 5.50, P < 0.05). An ROC curve for entropy gave an area under the curve of 0.58 with 51% sensitivity and 71% specificity for entropy greater than 5.94 for malignancy. In group 2, the entropy threshold phenotyped only 47% of benign cases and 20% of malignant cases correctly. These findings suggest that use of EBUS grey-scale textural analysis for differentiation of malignant from benign lymphadenopathy may not be accurate. Further studies are required. © 2015 Asian Pacific Society of Respirology.

Local multifractal detrended fluctuation analysis for non-stationary image's texture segmentation

NASA Astrophysics Data System (ADS)

Wang, Fang; Li, Zong-shou; Li, Jin-wei

2014-12-01

Feature extraction plays a great important role in image processing and pattern recognition. As a power tool, multifractal theory is recently employed for this job. However, traditional multifractal methods are proposed to analyze the objects with stationary measure and cannot for non-stationary measure. The works of this paper is twofold. First, the definition of stationary image and 2D image feature detection methods are proposed. Second, a novel feature extraction scheme for non-stationary image is proposed by local multifractal detrended fluctuation analysis (Local MF-DFA), which is based on 2D MF-DFA. A set of new multifractal descriptors, called local generalized Hurst exponent (Lhq) is defined to characterize the local scaling properties of textures. To test the proposed method, both the novel texture descriptor and other two multifractal indicators, namely, local Hölder coefficients based on capacity measure and multifractal dimension Dq based on multifractal differential box-counting (MDBC) method, are compared in segmentation experiments. The first experiment indicates that the segmentation results obtained by the proposed Lhq are better than the MDBC-based Dq slightly and superior to the local Hölder coefficients significantly. The results in the second experiment demonstrate that the Lhq can distinguish the texture images more effectively and provide more robust segmentations than the MDBC-based Dq significantly.

Lung Texture in Serial Thoracic Computed Tomography Scans: Correlation of Radiomics-based Features With Radiation Therapy Dose and Radiation Pneumonitis Development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cunliffe, Alexandra; Armato, Samuel G.; Castillo, Richard

2015-04-01

Purpose: To assess the relationship between radiation dose and change in a set of mathematical intensity- and texture-based features and to determine the ability of texture analysis to identify patients who develop radiation pneumonitis (RP). Methods and Materials: A total of 106 patients who received radiation therapy (RT) for esophageal cancer were retrospectively identified under institutional review board approval. For each patient, diagnostic computed tomography (CT) scans were acquired before (0-168 days) and after (5-120 days) RT, and a treatment planning CT scan with an associated dose map was obtained. 32- × 32-pixel regions of interest (ROIs) were randomly identifiedmore » in the lungs of each pre-RT scan. ROIs were subsequently mapped to the post-RT scan and the planning scan dose map by using deformable image registration. The changes in 20 feature values (ΔFV) between pre- and post-RT scan ROIs were calculated. Regression modeling and analysis of variance were used to test the relationships between ΔFV, mean ROI dose, and development of grade ≥2 RP. Area under the receiver operating characteristic curve (AUC) was calculated to determine each feature's ability to distinguish between patients with and those without RP. A classifier was constructed to determine whether 2- or 3-feature combinations could improve RP distinction. Results: For all 20 features, a significant ΔFV was observed with increasing radiation dose. Twelve features changed significantly for patients with RP. Individual texture features could discriminate between patients with and those without RP with moderate performance (AUCs from 0.49 to 0.78). Using multiple features in a classifier, AUC increased significantly (0.59-0.84). Conclusions: A relationship between dose and change in a set of image-based features was observed. For 12 features, ΔFV was significantly related to RP development. This study demonstrated the ability of radiomics to provide a quantitative, individualized measurement of patient lung tissue reaction to RT and assess RP development.« less

Robust surface roughness indices and morphological interpretation

NASA Astrophysics Data System (ADS)

Trevisani, Sebastiano; Rocca, Michele

2016-04-01

Geostatistical-based image/surface texture indices based on variogram (Atkison and Lewis, 2000; Herzfeld and Higginson, 1996; Trevisani et al., 2012) and on its robust variant MAD (median absolute differences, Trevisani and Rocca, 2015) offer powerful tools for the analysis and interpretation of surface morphology (potentially not limited to solid earth). In particular, the proposed robust index (Trevisani and Rocca, 2015) with its implementation based on local kernels permits the derivation of a wide set of robust and customizable geomorphometric indices capable to outline specific aspects of surface texture. The stability of MAD in presence of signal noise and abrupt changes in spatial variability is well suited for the analysis of high-resolution digital terrain models. Moreover, the implementation of MAD by means of a pixel-centered perspective based on local kernels, with some analogies to the local binary pattern approach (Lucieer and Stein, 2005; Ojala et al., 2002), permits to create custom roughness indices capable to outline different aspects of surface roughness (Grohmann et al., 2011; Smith, 2015). In the proposed poster, some potentialities of the new indices in the context of geomorphometry and landscape analysis will be presented. At same time, challenges and future developments related to the proposed indices will be outlined. Atkinson, P.M., Lewis, P., 2000. Geostatistical classification for remote sensing: an introduction. Computers & Geosciences 26, 361-371. Grohmann, C.H., Smith, M.J., Riccomini, C., 2011. Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland. IEEE Transactions on Geoscience and Remote Sensing 49, 1220-1213. Herzfeld, U.C., Higginson, C.A., 1996. Automated geostatistical seafloor classification - Principles, parameters, feature vectors, and discrimination criteria. Computers and Geosciences, 22 (1), pp. 35-52. Lucieer, A., Stein, A., 2005. Texture-based landform segmentation of LiDAR imagery. International Journal of Applied Earth Observation and Geoinformation 6, 261-270. Ojala, T., Pietikäinen, M. & Mäenpää, T. 2002. "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987. Smith, M.W. 2014. "Roughness in the Earth Sciences", Earth-Science Reviews, vol. 136, pp. 202-225. Trevisani, S., Cavalli, M. & Marchi, L. 2012. "Surface texture analysis of a high-resolution DTM: Interpreting an alpine basin", Geomorphology, vol. 161-162, pp. 26-39. Trevisani, S., Rocca, M. 2015. MAD: robust image texture analysis for applications in high resolution geomorphometry. Comput. Geosci. 81, 78-92. doi:10.1016/j.cageo.2015.04.003.

Further Development of a Computational Vision Model for the Generation and Analysis of Photo-Realistic Scenes for Military and Civilian Virtual Reality Applications

DTIC Science & Technology

1996-02-01

rectangle in the center of the image straddling the boundary between the foreground grass and background treeline . The synthetic content of the target region...the square in the middle of the grass, the square in the middle of the tree leaves, and the square on the treeline are all synthetic textures. The...target rectangle on the treeline boundary. What was suprising was that the synthetic grass texture was not well matched in the rectangle in the grass

Application of the angle measure technique as image texture analysis method for the identification of uranium ore concentrate samples: New perspective in nuclear forensics.

PubMed

Fongaro, Lorenzo; Ho, Doris Mer Lin; Kvaal, Knut; Mayer, Klaus; Rondinella, Vincenzo V

2016-05-15

The identification of interdicted nuclear or radioactive materials requires the application of dedicated techniques. In this work, a new approach for characterizing powder of uranium ore concentrates (UOCs) is presented. It is based on image texture analysis and multivariate data modelling. 26 different UOCs samples were evaluated applying the Angle Measure Technique (AMT) algorithm to extract textural features on samples images acquired at 250× and 1000× magnification by Scanning Electron Microscope (SEM). At both magnifications, this method proved effective to classify the different types of UOC powder based on the surface characteristics that depend on particle size, homogeneity, and graininess and are related to the composition and processes used in the production facilities. Using the outcome data from the application of the AMT algorithm, the total explained variance was higher than 90% with Principal Component Analysis (PCA), while partial least square discriminant analysis (PLS-DA) applied only on the 14 black colour UOCs powder samples, allowed their classification only on the basis of their surface texture features (sensitivity>0.6; specificity>0.6). This preliminary study shows that this method was able to distinguish samples with similar composition, but obtained from different facilities. The mean angle spectral data obtained by the image texture analysis using the AMT algorithm can be considered as a specific fingerprint or signature of UOCs and could be used for nuclear forensic investigation. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

SU-E-I-90: Characterizing Small Animal Lung Properties Using Speckle Observed with An In-Line X-Ray Phase Contrast Benchtop System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garson, A; Gunsten, S; Guan, H

Purpose: We demonstrate a novel X-ray phase-contrast (XPC) method for lung imaging representing a paradigm shift in the way small animal functional imaging is performed. In our method, information regarding airway microstructure that is encoded within speckle texture of a single XPC radiograph is decoded to produce 2D parametric images that will spatially resolve changes in lung properties such as microstructure sizes and air volumes. Such information cannot be derived from conventional lung radiography or any other 2D imaging modality. By computing these images at different points within a breathing cycle, dynamic functional imaging will be readily achieved without themore » need for tomography. Methods: XPC mouse lung radiographs acquired in situ with an in-line X-ray phase contrast benchtop system. The lung air volume is varied and controlled with a small animal ventilator. XPC radiographs will be acquired for various lung air volume levels representing different phases of the respiratory cycle. Similar data will be acquired of microsphere-based lung phantoms containing hollow glass spheres with known distributions of diameters. Image texture analysis is applied to the data to investigate relationships between texture characteristics and airspace/microsphere physical properties. Results: Correlations between Fourier-based texture descriptors (FBTDs) and regional lung air volume indicate that the texture features in 2D radiographs reveal information on 3D properties of the lungs. For example, we find for a 350 × 350 πm2 lung ROI a linear relationship between injected air volume and FBTD value with slope and intercept of 8.9×10{sup 5} and 7.5, respectively. Conclusion: We demonstrate specific image texture measures related to lung speckle features are correlated with physical characteristics of refracting elements (i.e. lung air spaces). Furthermore, we present results indicating the feasibility of implementing the technique with a simple imaging system design, short exposures, and low dose which provides potential for widespread use in laboratory settings for in vivo studies. This research was supported in part by NSF Award CBET1263988.« less

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma.

PubMed

Hu, Leland S; Ning, Shuluo; Eschbacher, Jennifer M; Baxter, Leslie C; Gaw, Nathan; Ranjbar, Sara; Plasencia, Jonathan; Dueck, Amylou C; Peng, Sen; Smith, Kris A; Nakaji, Peter; Karis, John P; Quarles, C Chad; Wu, Teresa; Loftus, Joseph C; Jenkins, Robert B; Sicotte, Hugues; Kollmeyer, Thomas M; O'Neill, Brian P; Elmquist, William; Hoxworth, Joseph M; Frakes, David; Sarkaria, Jann; Swanson, Kristin R; Tran, Nhan L; Li, Jing; Mitchell, J Ross

2017-01-01

Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology. © The Author(s) 2016. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

FFDM image quality assessment using computerized image texture analysis

NASA Astrophysics Data System (ADS)

Berger, Rachelle; Carton, Ann-Katherine; Maidment, Andrew D. A.; Kontos, Despina

2010-04-01

Quantitative measures of image quality (IQ) are routinely obtained during the evaluation of imaging systems. These measures, however, do not necessarily correlate with the IQ of the actual clinical images, which can also be affected by factors such as patient positioning. No quantitative method currently exists to evaluate clinical IQ. Therefore, we investigated the potential of using computerized image texture analysis to quantitatively assess IQ. Our hypothesis is that image texture features can be used to assess IQ as a measure of the image signal-to-noise ratio (SNR). To test feasibility, the "Rachel" anthropomorphic breast phantom (Model 169, Gammex RMI) was imaged with a Senographe 2000D FFDM system (GE Healthcare) using 220 unique exposure settings (target/filter, kVs, and mAs combinations). The mAs were varied from 10%-300% of that required for an average glandular dose (AGD) of 1.8 mGy. A 2.5cm2 retroareolar region of interest (ROI) was segmented from each image. The SNR was computed from the ROIs segmented from images linear with dose (i.e., raw images) after flat-field and off-set correction. Image texture features of skewness, coarseness, contrast, energy, homogeneity, and fractal dimension were computed from the Premium ViewTM postprocessed image ROIs. Multiple linear regression demonstrated a strong association between the computed image texture features and SNR (R2=0.92, p<=0.001). When including kV, target and filter as additional predictor variables, a stronger association with SNR was observed (R2=0.95, p<=0.001). The strong associations indicate that computerized image texture analysis can be used to measure image SNR and potentially aid in automating IQ assessment as a component of the clinical workflow. Further work is underway to validate our findings in larger clinical datasets.

In-situ laser ultrasonic measurement of the hcp to bcc transformation in commercially pure titanium

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shinbine, A., E-mail: alyssa.shinbine@gmail.com; Garcin, T.; Sinclair, C.

2016-07-15

Using a novel in-situ laser ultrasonic technique, the evolution of longitudinal velocity was used to measure the α − β transformation during cyclic heating and cooling in commercially pure titanium. In order to quantify the transformation kinetics, it is shown that changes in texture can not be ignored. This is particularly important in the case of titanium where significant grain growth occurs in the β-phase leading to the ultrasonic wave sampling a decreasing number of grains on each thermal treatment cycle. Electron backscatter diffraction measurements made postmortem in the region where the ultrasonic pulse traveled were used to obtain anmore » estimate of such local texture and grain size changes. An analysis technique for including the anisotropy of wave velocity depending on local texture is presented and shown to give self consistent results for the transformation kinetics. - Highlights: • Laser ultrasound and EBSD interpret the hcp/bcc phase transformation in cp-Ti. • Grain growth and texture produced variation in velocity during similar treatments. • Texture was deconvoluted from phase addition to obtain transformation kinetics.« less

Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy.

PubMed

Wu, Jia; Gong, Guanghua; Cui, Yi; Li, Ruijiang

2016-11-01

To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65). The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107-1115. © 2016 International Society for Magnetic Resonance in Medicine.

Cloud field classification based on textural features

NASA Technical Reports Server (NTRS)

Sengupta, Sailes Kumar

1989-01-01

An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes of features. Preliminary results based on the GLDV textural features alone look promising.

Classification of brain tumors using texture based analysis of T1-post contrast MR scans in a preclinical model

NASA Astrophysics Data System (ADS)

Tang, Tien T.; Zawaski, Janice A.; Francis, Kathleen N.; Qutub, Amina A.; Gaber, M. Waleed

2018-02-01

Accurate diagnosis of tumor type is vital for effective treatment planning. Diagnosis relies heavily on tumor biopsies and other clinical factors. However, biopsies do not fully capture the tumor's heterogeneity due to sampling bias and are only performed if the tumor is accessible. An alternative approach is to use features derived from routine diagnostic imaging such as magnetic resonance (MR) imaging. In this study we aim to establish the use of quantitative image features to classify brain tumors and extend the use of MR images beyond tumor detection and localization. To control for interscanner, acquisition and reconstruction protocol variations, the established workflow was performed in a preclinical model. Using glioma (U87 and GL261) and medulloblastoma (Daoy) models, T1-weighted post contrast scans were acquired at different time points post-implant. The tumor regions at the center, middle, and peripheral were analyzed using in-house software to extract 32 different image features consisting of first and second order features. The extracted features were used to construct a decision tree, which could predict tumor type with 10-fold cross-validation. Results from the final classification model demonstrated that middle tumor region had the highest overall accuracy at 79%, while the AUC accuracy was over 90% for GL261 and U87 tumors. Our analysis further identified image features that were unique to certain tumor region, although GL261 tumors were more homogenous with no significant differences between the central and peripheral tumor regions. In conclusion our study shows that texture features derived from MR scans can be used to classify tumor type with high success rates. Furthermore, the algorithm we have developed can be implemented with any imaging datasets and may be applicable to multiple tumor types to determine diagnosis.

Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data

NASA Astrophysics Data System (ADS)

Gegiuc, Alexandru; Similä, Markku; Karvonen, Juha; Lensu, Mikko; Mäkynen, Marko; Vainio, Jouni

2018-01-01

For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chart-based degree of ice ridging and the automated results varied monthly, being 83, 63 and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean.

Land use classification using texture information in ERTS-A MSS imagery

NASA Technical Reports Server (NTRS)

Haralick, R. M. (Principal Investigator); Shanmugam, K. S.; Bosley, R.

1973-01-01

The author has identified the following significant results. Preliminary digital analysis of ERTS-1 MSS imagery reveals that the textural features of the imagery are very useful for land use classification. A procedure for extracting the textural features of ERTS-1 imagery is presented and the results of a land use classification scheme based on the textural features are also presented. The land use classification algorithm using textural features was tested on a 5100 square mile area covered by part of an ERTS-1 MSS band 5 image over the California coastline. The image covering this area was blocked into 648 subimages of size 8.9 square miles each. Based on a color composite of the image set, a total of 7 land use categories were identified. These land use categories are: coastal forest, woodlands, annual grasslands, urban areas, large irrigated fields, small irrigated fields, and water. The automatic classifier was trained to identify the land use categories using only the textural characteristics of the subimages; 75 percent of the subimages were assigned correct identifications. Since texture and spectral features provide completely different kinds of information, a significant increase in identification accuracy will take place when both features are used together.

Muscle Protein Profiles Used for Prediction of Texture of Farmed Salmon (Salmo salar L.).

PubMed

Ørnholt-Johansson, Gine; Frosch, Stina; Gudjónsdóttir, María; Wulff, Tune; Jessen, Flemming

2017-04-26

A soft texture is undesired in Atlantic salmon as it leads to downgrading and reduced yield, yet it is a factor for which the cause is not fully understood. This lack of understanding highlights the need for identifying the cause of the soft texture and developing solutions by which the processing industry can improve the yield. Changes in muscle protein profiles can occur both pre- and postharvest and constitute an overall characterization of the muscle properties including texture. The aim of this study was to investigate this relationship between specific muscle proteins and the texture of the salmon fillet. Samples for 2D-gel-based proteomics were taken from the fillet above the lateral line at the same position as where the texture had been measured. The resulting protein profiles were analyzed using multivariate data analysis. Sixteen proteins were found to correlate to the measured texture, showing that it is possible to predict peak force based on a small subset of proteins. Additionally, eight of the 16 proteins were identified by tandem mass spectrometry including serum albumin, dipeptidyl peptidase 3, heat shock protein 70, annexins, and a protein presumed to be a titin fragment. It is contemplated that the identification of these proteins and their significance for the measured texture will contribute to further understanding of the Atlantic salmon muscle texture.

Quantification of ultrasonic texture intra-heterogeneity via volumetric stochastic modeling for tissue characterization.

PubMed

Al-Kadi, Omar S; Chung, Daniel Y F; Carlisle, Robert C; Coussios, Constantin C; Noble, J Alison

2015-04-01

Intensity variations in image texture can provide powerful quantitative information about physical properties of biological tissue. However, tissue patterns can vary according to the utilized imaging system and are intrinsically correlated to the scale of analysis. In the case of ultrasound, the Nakagami distribution is a general model of the ultrasonic backscattering envelope under various scattering conditions and densities where it can be employed for characterizing image texture, but the subtle intra-heterogeneities within a given mass are difficult to capture via this model as it works at a single spatial scale. This paper proposes a locally adaptive 3D multi-resolution Nakagami-based fractal feature descriptor that extends Nakagami-based texture analysis to accommodate subtle speckle spatial frequency tissue intensity variability in volumetric scans. Local textural fractal descriptors - which are invariant to affine intensity changes - are extracted from volumetric patches at different spatial resolutions from voxel lattice-based generated shape and scale Nakagami parameters. Using ultrasound radio-frequency datasets we found that after applying an adaptive fractal decomposition label transfer approach on top of the generated Nakagami voxels, tissue characterization results were superior to the state of art. Experimental results on real 3D ultrasonic pre-clinical and clinical datasets suggest that describing tumor intra-heterogeneity via this descriptor may facilitate improved prediction of therapy response and disease characterization. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Analyzing the texture changes in the quantitative phase maps of adipocytes

NASA Astrophysics Data System (ADS)

Roitshtain, Darina; Sharabani-Yosef, Orna; Gefen, Amit; Shaked, Natan T.

2016-03-01

We present a new analysis tool for studying texture changes in the quantitative phase maps of live cells acquired by wide-field interferometry. The sensitivity of wide-field interferometry systems to small changes in refractive index enables visualizing cells and inner cell organelles without the using fluorescent dyes or other cell-invasive approaches, which may affect the measurement and require external labeling. Our label-free texture-analysis tool is based directly on the optical path delay profile of the sample and does not necessitate decoupling refractive index and thickness in the cell quantitative phase profile; thus, relevant parameters can be calculated using a single-frame acquisition. Our experimental system includes low-coherence wide-field interferometer, combined with simultaneous florescence microscopy system for validation. We used this system and analysis tool for studying lipid droplets formation in adipocytes. The latter demonstration is relevant for various cellular functions such as lipid metabolism, protein storage and degradation to viral replication. These processes are functionally linked to several physiological and pathological conditions, including obesity and metabolic diseases. Quantification of these biological phenomena based on the texture changes in the cell phase map has a potential as a new cellular diagnosis tool.

3D texture analysis for classification of second harmonic generation images of human ovarian cancer

NASA Astrophysics Data System (ADS)

Wen, Bruce; Campbell, Kirby R.; Tilbury, Karissa; Nadiarnykh, Oleg; Brewer, Molly A.; Patankar, Manish; Singh, Vikas; Eliceiri, Kevin. W.; Campagnola, Paul J.

2016-10-01

Remodeling of the collagen architecture in the extracellular matrix (ECM) has been implicated in ovarian cancer. To quantify these alterations we implemented a form of 3D texture analysis to delineate the fibrillar morphology observed in 3D Second Harmonic Generation (SHG) microscopy image data of normal (1) and high risk (2) ovarian stroma, benign ovarian tumors (3), low grade (4) and high grade (5) serous tumors, and endometrioid tumors (6). We developed a tailored set of 3D filters which extract textural features in the 3D image sets to build (or learn) statistical models of each tissue class. By applying k-nearest neighbor classification using these learned models, we achieved 83-91% accuracies for the six classes. The 3D method outperformed the analogous 2D classification on the same tissues, where we suggest this is due the increased information content. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. Moreover, the texture analysis algorithm is quite general, as it does not rely on single morphological metrics such as fiber alignment, length, and width but their combined convolution with a customizable basis set.

Can texture analysis of tooth microwear detect within guild niche partitioning in extinct species?

NASA Astrophysics Data System (ADS)

Purnell, Mark; Nedza, Christopher; Rychlik, Leszek

2017-04-01

Recent work shows that tooth microwear analysis can be applied further back in time and deeper into the phylogenetic history of vertebrate clades than previously thought (e.g. niche partitioning in early Jurassic insectivorous mammals; Gill et al., 2014, Nature). Furthermore, quantitative approaches to analysis based on parameterization of surface roughness are increasing the robustness and repeatability of this widely used dietary proxy. Discriminating between taxa within dietary guilds has the potential to significantly increase our ability to determine resource use and partitioning in fossil vertebrates, but how sensitive is the technique? To address this question we analysed tooth microwear texture in sympatric populations of shrew species (Neomys fodiens, Neomys anomalus, Sorex araneus, Sorex minutus) from BiaŁ owieza Forest, Poland. These populations are known to exhibit varying degrees of niche partitioning (Churchfield & Rychlik, 2006, J. Zool.) with greatest overlap between the Neomys species. Sorex araneus also exhibits some niche overlap with N. anomalus, while S. minutus is the most specialised. Multivariate analysis based only on tooth microwear textures recovers the same pattern of niche partitioning. Our results also suggest that tooth textures track seasonal differences in diet. Projecting data from fossils into the multivariate dietary space defined using microwear from extant taxa demonstrates that the technique is capable of subtle dietary discrimination in extinct insectivores.

Segmentation of liver region with tumorous tissues

NASA Astrophysics Data System (ADS)

Zhang, Xuejun; Lee, Gobert; Tajima, Tetsuji; Kitagawa, Teruhiko; Kanematsu, Masayuki; Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Yokoyama, Ryujiro; Kondo, Hiroshi; Hoshi, Hiroaki; Nawano, Shigeru; Shinozaki, Kenji

2007-03-01

Segmentation of an abnormal liver region based on CT or MR images is a crucial step in surgical planning. However, precisely carrying out this step remains a challenge due to either connectivities of the liver to other organs or the shape, internal texture, and homogeneity of liver that maybe extensively affected in case of liver diseases. Here, we propose a non-density based method for extracting the liver region containing tumor tissues by edge detection processing. False extracted regions are eliminated by a shape analysis method and thresholding processing. If the multi-phased images are available then the overall outcome of segmentation can be improved by subtracting two phase images, and the connectivities can be further eliminated by referring to the intensity on another phase image. Within an edge liver map, tumor candidates are identified by their different gray values relative to the liver. After elimination of the small and nonspherical over-extracted regions, the final liver region integrates the tumor region with the liver tissue. In our experiment, 40 cases of MDCT images were used and the result showed that our fully automatic method for the segmentation of liver region is effective and robust despite the presence of hepatic tumors within the liver.

A research of selected textural features for detection of asbestos-cement roofing sheets using orthoimages

NASA Astrophysics Data System (ADS)

Książek, Judyta

2015-10-01

At present, there has been a great interest in the development of texture based image classification methods in many different areas. This study presents the results of research carried out to assess the usefulness of selected textural features for detection of asbestos-cement roofs in orthophotomap classification. Two different orthophotomaps of southern Poland (with ground resolution: 5 cm and 25 cm) were used. On both orthoimages representative samples for two classes: asbestos-cement roofing sheets and other roofing materials were selected. Estimation of texture analysis usefulness was conducted using machine learning methods based on decision trees (C5.0 algorithm). For this purpose, various sets of texture parameters were calculated in MaZda software. During the calculation of decision trees different numbers of texture parameters groups were considered. In order to obtain the best settings for decision trees models cross-validation was performed. Decision trees models with the lowest mean classification error were selected. The accuracy of the classification was held based on validation data sets, which were not used for the classification learning. For 5 cm ground resolution samples, the lowest mean classification error was 15.6%. The lowest mean classification error in the case of 25 cm ground resolution was 20.0%. The obtained results confirm potential usefulness of the texture parameter image processing for detection of asbestos-cement roofing sheets. In order to improve the accuracy another extended study should be considered in which additional textural features as well as spectral characteristics should be analyzed.

A potential conflict between preserving regional plant diversity and biotic resistance to an invasive grass, Microstegium vimineum

Treesearch

J. Stephen Brewer

2010-01-01

The relevance of diversity-biotic resistance studies to conservation of biodiversity could be improved by simultaneously examining the drivers of regional diversity and their effects on local species diversity and invasion. Using path analysis, I examined direct and indirect effects of various abiotic factors (i.e., flooding, treefall gaps, soil texture, proximity to...

The analysis of image feature robustness using cometcloud

PubMed Central

Qi, Xin; Kim, Hyunjoo; Xing, Fuyong; Parashar, Manish; Foran, David J.; Yang, Lin

2012-01-01

The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval. PMID:23248759

Three-dimensional ultrasound-based texture analysis of the effect of atorvastatin on carotid atherosclerosis

NASA Astrophysics Data System (ADS)

Awad, Joseph; Krasinski, Adam; Spence, David; Parraga, Grace; Fenster, Aaron

2010-03-01

Carotid atherosclerosis is the major cause of ischemic stroke, a leading cause of death and disability. This is driving the development of image analysis methods to quantitatively evaluate local arterial effects of potential treatments of carotid disease. Here we investigate the use of novel texture analysis tools to detect potential changes in the carotid arteries after statin therapy. Three-dimensional (3D) carotid ultrasound images were acquired from the left and right carotid arteries of 35 subjects (16 treated with 80 mg atorvastatin and 19 treated with placebo) at baseline and after 3 months of treatment. Two-hundred and seventy texture features were extracted from 3D ultrasound carotid artery images. These images previously had their vessel walls (VW) manually segmented. Highly ranked individual texture features were selected and compared to the VW volume (VWV) change using 3 measures: distance between classes, Wilcoxon rank sum test, and accuracy of the classifiers. Six classifiers were used. Using texture feature (L7R7) increases the average accuracy and area under the ROC curve to 74.4% and 0.72 respectively compared to 57.2% and 0.61 using VWV change. Thus, the results demonstrate that texture features are more sensitive in detecting drug effects on the carotid vessel wall than VWV change.

Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

PubMed Central

Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly

2013-01-01

High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. PMID:23261652

Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images.

PubMed

Barbosa, Daniel C; Roupar, Dalila B; Ramos, Jaime C; Tavares, Adriano C; Lima, Carlos S

2012-01-11

Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.

Automated kidney morphology measurements from ultrasound images using texture and edge analysis

NASA Astrophysics Data System (ADS)

Ravishankar, Hariharan; Annangi, Pavan; Washburn, Michael; Lanning, Justin

2016-04-01

In a typical ultrasound scan, a sonographer measures Kidney morphology to assess renal abnormalities. Kidney morphology can also help to discriminate between chronic and acute kidney failure. The caliper placements and volume measurements are often time consuming and an automated solution will help to improve accuracy, repeatability and throughput. In this work, we developed an automated Kidney morphology measurement solution from long axis Ultrasound scans. Automated kidney segmentation is challenging due to wide variability in kidney shape, size, weak contrast of the kidney boundaries and presence of strong edges like diaphragm, fat layers. To address the challenges and be able to accurately localize and detect kidney regions, we present a two-step algorithm that makes use of edge and texture information in combination with anatomical cues. First, we use an edge analysis technique to localize kidney region by matching the edge map with predefined templates. To accurately estimate the kidney morphology, we use textural information in a machine learning algorithm framework using Haar features and Gradient boosting classifier. We have tested the algorithm on 45 unseen cases and the performance against ground truth is measured by computing Dice overlap, % error in major and minor axis of kidney. The algorithm shows successful performance on 80% cases.

Slice simulation from a model of the parenchymous vascularization to evaluate texture features: work in progress.

PubMed

Rolland, Y; Bézy-Wendling, J; Duvauferrier, R; Coatrieux, J L

1999-03-01

To demonstrate the usefulness of a model of the parenchymous vascularization to evaluate texture analysis methods. Slices with thickness varying from 1 to 4 mm were reformatted from a 3D vascular model corresponding to either normal tissue perfusion or local hypervascularization. Parameters of statistical methods were measured on 16128x128 regions of interest, and mean values and standard deviation were calculated. For each parameter, the performances (discrimination power and stability) were evaluated. Among 11 calculated statistical parameters, three (homogeneity, entropy, mean of gradients) were found to have a good discriminating power to differentiate normal perfusion from hypervascularization, but only the gradient mean was found to have a good stability with respect to the thickness. Five parameters (run percentage, run length distribution, long run emphasis, contrast, and gray level distribution) were found to have intermediate results. In the remaining three, curtosis and correlation was found to have little discrimination power, skewness none. This 3D vascular model, which allows the generation of various examples of vascular textures, is a powerful tool to assess the performance of texture analysis methods. This improves our knowledge of the methods and should contribute to their a priori choice when designing clinical studies.

Texture and structure contribution to low-temperature plasticity enhancement of Mg-Al-Zn-Mn Alloy MA2-1hp after ECAP and annealing

NASA Astrophysics Data System (ADS)

Serebryany, V. N.; D'yakonov, G. S.; Kopylov, V. I.; Salishchev, G. A.; Dobatkin, S. V.

2013-05-01

Equal channel angular pressing (ECAP) in magnesium alloys due to severe plastic shear deformations provides both grain refinement and the slope of the initial basal texture at 40°-50° to the pressing direction. These changes in microstructure and texture contribute to the improvement of low-temperature plasticity of the alloys. Quantitative texture X-ray diffraction analysis and diffraction of backscattered electrons are used to study the main textural and structural factors responsible for enhanced low-temperature plasticity based on the example of magnesium alloy MA2-1hp of the Mg-Al-Zn-Mn system. The possible mechanisms of deformation that lead to this positive effect are discussed.

An Efficient Algorithm for Server Thermal Fault Diagnosis Based on Infrared Image

NASA Astrophysics Data System (ADS)

Liu, Hang; Xie, Ting; Ran, Jian; Gao, Shan

2017-10-01

It is essential for a data center to maintain server security and stability. Long-time overload operation or high room temperature may cause service disruption even a server crash, which would result in great economic loss for business. Currently, the methods to avoid server outages are monitoring and forecasting. Thermal camera can provide fine texture information for monitoring and intelligent thermal management in large data center. This paper presents an efficient method for server thermal fault monitoring and diagnosis based on infrared image. Initially thermal distribution of server is standardized and the interest regions of the image are segmented manually. Then the texture feature, Hu moments feature as well as modified entropy feature are extracted from the segmented regions. These characteristics are applied to analyze and classify thermal faults, and then make efficient energy-saving thermal management decisions such as job migration. For the larger feature space, the principal component analysis is employed to reduce the feature dimensions, and guarantee high processing speed without losing the fault feature information. Finally, different feature vectors are taken as input for SVM training, and do the thermal fault diagnosis after getting the optimized SVM classifier. This method supports suggestions for optimizing data center management, it can improve air conditioning efficiency and reduce the energy consumption of the data center. The experimental results show that the maximum detection accuracy is 81.5%.

Parenchymal texture measures weighted by breast anatomy: preliminary optimization in a case-control study

NASA Astrophysics Data System (ADS)

Gastounioti, Aimilia; Keller, Brad M.; Hsieh, Meng-Kang; Conant, Emily F.; Kontos, Despina

2016-03-01

Growing evidence suggests that quantitative descriptors of the parenchymal texture patterns hold a valuable role in assessing an individual woman's risk for breast cancer. In this work, we assess the hypothesis that breast cancer risk factors are not uniformly expressed in the breast parenchymal tissue and, therefore, breast-anatomy-weighted parenchymal texture descriptors, where different breasts ROIs have non uniform contributions, may enhance breast cancer risk assessment. To this end, we introduce an automated breast-anatomy-driven methodology which generates a breast atlas, which is then used to produce a weight map that reinforces the contributions of the central and upper-outer breast areas. We incorporate this methodology to our previously validated lattice-based strategy for parenchymal texture analysis. In the framework of a pilot case-control study, including digital mammograms from 424 women, our proposed breast-anatomy-weighted texture descriptors are optimized and evaluated against non weighted texture features, using regression analysis with leave-one-out cross validation. The classification performance is assessed in terms of the area under the curve (AUC) of the receiver operating characteristic. The collective discriminatory capacity of the weighted texture features was maximized (AUC=0.87) when the central breast area was considered more important than the upperouter area, with significant performance improvement (DeLong's test, p-value<0.05) against the non-weighted texture features (AUC=0.82). Our results suggest that breast-anatomy-driven methodologies have the potential to further upgrade the promising role of parenchymal texture analysis in breast cancer risk assessment and may serve as a reference in the design of future studies towards image-driven personalized recommendations regarding women's cancer risk evaluation.

Characterization of the major histopathological components of thyroid nodules using sonographic textural features for clinical diagnosis and management.

PubMed

Chen, Shao-Jer; Yu, Sung-Nien; Tzeng, Jeh-En; Chen, Yen-Ting; Chang, Ku-Yaw; Cheng, Kuo-Sheng; Hsiao, Fu-Tsung; Wei, Chang-Kuo

2009-02-01

In this study, the characteristic sonographic textural feature that represents the major histopathologic components of the thyroid nodules was objectively quantified to facilitate clinical diagnosis and management. A total of 157 regions-of-interest thyroid ultrasound image was recruited in the study. The sonographic system used was the GE LOGIQ 700), (General Electric Healthcare, Chalfant St. Giles, UK). The parameters affecting image acquisition were kept in the same condition for all lesions. Commonly used texture analysis methods were applied to characterize thyroid ultrasound images. Image features were classified according to the corresponding pathologic findings. To estimate their relevance and performance to classification, ReliefF was used as a feature selector. Among the various textural features, the sum average value derived from co-occurrence matrix can well reflect echogenicity and can effectively differentiate between follicles and fibrosis base thyroid nodules. Fibrosis shows lowest echogenicity and lowest difference sum average value. Enlarged follicles show highest echogenicity and difference sum average values. Papillary cancer or follicular tumors show the difference sum average values and echogenicity between. The rule of thumb for the echogenicity is that the more follicles are mixed in, the higher the echo of the follicular tumor and papillary cancer will be and vice versa for fibrosis mixed. Areas with intermediate and lower echo should address the possibility of follicular or papillary neoplasm mixed with either follicles or fibrosis. These areas provide more cellular information for ultrasound guided aspiration

The Study of Residential Areas Extraction Based on GF-3 Texture Image Segmentation

NASA Astrophysics Data System (ADS)

Shao, G.; Luo, H.; Tao, X.; Ling, Z.; Huang, Y.

2018-04-01

The study chooses the standard stripe and dual polarization SAR images of GF-3 as the basic data. Residential areas extraction processes and methods based upon GF-3 images texture segmentation are compared and analyzed. GF-3 images processes include radiometric calibration, complex data conversion, multi-look processing, images filtering, and then conducting suitability analysis for different images filtering methods, the filtering result show that the filtering method of Kuan is efficient for extracting residential areas, then, we calculated and analyzed the texture feature vectors using the GLCM (the Gary Level Co-occurrence Matrix), texture feature vectors include the moving window size, step size and angle, the result show that window size is 11*11, step is 1, and angle is 0°, which is effective and optimal for the residential areas extracting. And with the FNEA (Fractal Net Evolution Approach), we segmented the GLCM texture images, and extracted the residential areas by threshold setting. The result of residential areas extraction verified and assessed by confusion matrix. Overall accuracy is 0.897, kappa is 0.881, and then we extracted the residential areas by SVM classification based on GF-3 images, the overall accuracy is less 0.09 than the accuracy of extraction method based on GF-3 Texture Image Segmentation. We reached the conclusion that residential areas extraction based on GF-3 SAR texture image multi-scale segmentation is simple and highly accurate. although, it is difficult to obtain multi-spectrum remote sensing image in southern China, in cloudy and rainy weather throughout the year, this paper has certain reference significance.

Seabed photographs, sediment texture analyses, and sun-illuminated sea floor topography in the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts

USGS Publications Warehouse

Valentine, Page C.; Gallea, Leslie B.; Blackwood, Dann S.; Twomey, Erin R.

2010-01-01

The U.S. Geological Survey, in collaboration with National Oceanic and Atmospheric Administration's National Marine Sanctuary Program, conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary region from 1993 to 2004. The mapped area is approximately 3,700 km (1,100 nmi) in size and was subdivided into 18 quadrangles. An extensive series of sea-floor maps of the region based on multibeam sonar surveys has been published as paper maps and online in digital format (PDF, EPS, PS). In addition, 2,628 seabed-sediment samples were collected and analyzed and are in the usSEABED: Atlantic Coast Offshore Surficial Sediment Data Release. This report presents for viewing and downloading the more than 10,600 still seabed photographs that were acquired during the project. The digital images are provided in thumbnail, medium (1536 x 1024 pixels), and high (3071 x 2048) resolution. The images can be viewed by quadrangle on the U.S. Geological Survey Woods Hole Coastal and Marine Science Center's photograph database. Photograph metadata are embedded in each image in Exchangeable Image File Format and also provided in spreadsheet format. Published digital topographic maps and descriptive text for seabed features are included here for downloading and serve as context for the photographs. An interactive topographic map for each quadrangle shows locations of photograph stations, and each location is linked to the photograph database. This map also shows stations where seabed sediment was collected for texture analysis; the results of grain-size analysis and associated metadata are presented in spreadsheet format.

Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma.

PubMed

Kebir, Sied; Khurshid, Zain; Gaertner, Florian C; Essler, Markus; Hattingen, Elke; Fimmers, Rolf; Scheffler, Björn; Herrlinger, Ulrich; Bundschuh, Ralph A; Glas, Martin

2017-01-31

Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror tumor uptake heterogeneity; some of them may be associated with tumor progression. Fourteen patients with HGG and suspected of PSP underwent FET-PET imaging. A set of 19 conventional and textural FET-PET features were evaluated and subjected to unsupervised consensus clustering. The final diagnosis of true progression vs. PSP was based on follow-up MRI using RANO criteria. Three robust clusters have been identified based on 10 predominantly textural FET-PET features. None of the patients with PSP fell into cluster 2, which was associated with high values for textural FET-PET markers of uptake heterogeneity. Three out of 4 patients with PSP were assigned to cluster 3 that was largely associated with low values of textural FET-PET features. By comparison, tumor-to-normal brain ratio (TNRmax) at the optimal cutoff 2.1 was less predictive of PSP (negative predictive value 57% for detecting true progression, p=0.07 vs. 75% with cluster 3, p=0.04). Clustering based on textural O-(2-[18F]fluoroethyl)-L-tyrosine PET features may provide valuable information in assessing the elusive phenomenon of pseudoprogression.

TU-F-12A-05: Sensitivity of Textural Features to 3D Vs. 4D FDG-PET/CT Imaging in NSCLC Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, F; Nyflot, M; Bowen, S

2014-06-15

Purpose: Neighborhood Gray-level difference matrices (NGLDM) based texture parameters extracted from conventional (3D) 18F-FDG PET scans in patients with NSCLC have been previously shown to associate with response to chemoradiation and poorer patient outcome. However, the change in these parameters when utilizing respiratory-correlated (4D) FDG-PET scans has not yet been characterized for NSCLC. The Objectives: of this study was to assess the extent to which NGLDM-based texture parameters on 4D PET images vary with reference to values derived from 3D scans in NSCLC. Methods: Eight patients with newly diagnosed NSCLC treated with concomitant chemoradiotherapy were included in this study. 4Dmore » PET scans were reconstructed with OSEM-IR in 5 respiratory phase-binned images and corresponding CT data of each phase were employed for attenuation correction. NGLDM-based texture features, consisting of coarseness, contrast, busyness, complexity and strength, were evaluated for gross tumor volumes defined on 3D/4D PET scans by radiation oncologists. Variation of the obtained texture parameters over the respiratory cycle were examined with respect to values extracted from 3D scans. Results: Differences between texture parameters derived from 4D scans at different respiratory phases and those extracted from 3D scans ranged from −30% to 13% for coarseness, −12% to 40% for contrast, −5% to 50% for busyness, −7% to 38% for complexity, and −43% to 20% for strength. Furthermore, no evident correlations were observed between respiratory phase and 4D scan texture parameters. Conclusion: Results of the current study showed that NGLDM-based texture parameters varied considerably based on choice of 3D PET and 4D PET reconstruction of NSCLC patient images, indicating that standardized image acquisition and analysis protocols need to be established for clinical studies, especially multicenter clinical trials, intending to validate prognostic values of texture features for NSCLC.« less

Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm.

PubMed

Banchhor, Sumit K; Londhe, Narendra D; Araki, Tadashi; Saba, Luca; Radeva, Petia; Laird, John R; Suri, Jasjit S

2017-12-01

Planning of percutaneous interventional procedures involves a pre-screening and risk stratification of the coronary artery disease. Current screening tools use stand-alone plaque texture-based features and therefore lack the ability to stratify the risk. This IRB approved study presents a novel strategy for coronary artery disease risk stratification using an amalgamation of IVUS plaque texture-based and wall-based measurement features. Due to common genetic plaque makeup, carotid plaque burden was chosen as a gold standard for risk labels during training-phase of machine learning (ML) paradigm. Cross-validation protocol was adopted to compute the accuracy of the ML framework. A set of 59 plaque texture-based features was padded with six wall-based measurement features to show the improvement in stratification accuracy. The ML system was executed using principle component analysis-based framework for dimensionality reduction and uses support vector machine classifier for training and testing-phases. The ML system produced a stratification accuracy of 91.28%, demonstrating an improvement of 5.69% when wall-based measurement features were combined with plaque texture-based features. The fused system showed an improvement in mean sensitivity, specificity, positive predictive value, and area under the curve by: 6.39%, 4.59%, 3.31% and 5.48%, respectively when compared to the stand-alone system. While meeting the stability criteria of 5%, the ML system also showed a high average feature retaining power and mean reliability of 89.32% and 98.24%, respectively. The ML system showed an improvement in risk stratification accuracy when the wall-based measurement features were fused with the plaque texture-based features. Copyright © 2017 Elsevier Ltd. All rights reserved.

Method for adhering a coating to a substrate structure

DOEpatents

Taxacher, Glenn Curtis; Crespo, Andres Garcia; Roberts, III, Herbert Chidsey

2015-02-17

A method for adhering a coating to a substrate structure comprises selecting a substrate structure having an outer surface oriented substantially parallel to a direction of radial stress, modifying the outer surface to provide a textured region having steps to adhere a coating thereto, and applying a coating to extend over at least a portion of the textured region, wherein the steps are oriented substantially perpendicular to the direction of radial stress to resist deformation of the coating relative to the substrate structure. A rotating component comprises a substrate structure having an outer surface oriented substantially parallel to a direction of radial stress. The outer surface defines a textured region having steps to adhere a coating thereto, and a coating extends over at least a portion of the textured region. The steps are oriented substantially perpendicular to the direction of radial stress to resist creep.

3D Texture Analysis Reveals Imperceptible MRI Textural Alterations in the Thalamus and Putamen in Progressive Myoclonic Epilepsy Type 1, EPM1

PubMed Central

Suoranta, Sanna; Holli-Helenius, Kirsi; Koskenkorva, Päivi; Niskanen, Eini; Könönen, Mervi; Äikiä, Marja; Eskola, Hannu; Kälviäinen, Reetta; Vanninen, Ritva

2013-01-01

Progressive myoclonic epilepsy type 1 (EPM1) is an autosomal recessively inherited neurodegenerative disorder characterized by young onset age, myoclonus and tonic-clonic epileptic seizures. At the time of diagnosis, the visual assessment of the brain MRI is usually normal, with no major changes found later. Therefore, we utilized texture analysis (TA) to characterize and classify the underlying properties of the affected brain tissue by means of 3D texture features. Sixteen genetically verified patients with EPM1 and 16 healthy controls were included in the study. TA was performed upon 3D volumes of interest that were placed bilaterally in the thalamus, amygdala, hippocampus, caudate nucleus and putamen. Compared to the healthy controls, EPM1 patients had significant textural differences especially in the thalamus and right putamen. The most significantly differing texture features included parameters that measure the complexity and heterogeneity of the tissue, such as the co-occurrence matrix-based entropy and angular second moment, and also the run-length matrix-based parameters of gray-level non-uniformity, short run emphasis and long run emphasis. This study demonstrates the usability of 3D TA for extracting additional information from MR images. Textural alterations which suggest complex, coarse and heterogeneous appearance were found bilaterally in the thalamus, supporting the previous literature on thalamic pathology in EPM1. The observed putamenal involvement is a novel finding. Our results encourage further studies on the clinical applications, feasibility, reproducibility and reliability of 3D TA. PMID:23922849

Figure-ground segregation in a recurrent network architecture.

PubMed

Roelfsema, Pieter R; Lamme, Victor A F; Spekreijse, Henk; Bosch, Holger

2002-05-15

Here we propose a model of how the visual brain segregates textured scenes into figures and background. During texture segregation, locations where the properties of texture elements change abruptly are assigned to boundaries, whereas image regions that are relatively homogeneous are grouped together. Boundary detection and grouping of image regions require different connection schemes, which are accommodated in a single network architecture by implementing them in different layers. As a result, all units carry signals related to boundary detection as well as grouping of image regions, in accordance with cortical physiology. Boundaries yield an early enhancement of network responses, but at a later point, an entire figural region is grouped together, because units that respond to it are labeled with enhanced activity. The model predicts which image regions are preferentially perceived as figure or as background and reproduces the spatio-temporal profile of neuronal activity in the visual cortex during texture segregation in intact animals, as well as in animals with cortical lesions.

Influence of crystallographic texture in X70 pipeline steels on toughness anisotropy and delamination

NASA Astrophysics Data System (ADS)

Al-Jabr, Haytham M.

The effects of microstructure and crystallographic texture in four commercially-produced API X70 pipeline steels and their relation to planar anisotropy of toughness and delamination were evaluated. The experimental steels were processed through either a hot strip mill, a Steckel mill, or a compact strip mill. Different processing routes were selected to obtain plates with potential variations in the microstructure and anisotropic characteristics. Tensile and Charpy impact testing were used to evaluate the mechanical properties in three orientations: longitudinal (L), transverse (T) and diagonal (D) with respect to the rolling direction to evaluate mechanical property anisotropy. The yield and tensile strengths were higher in the T orientation and toughness was lower in the D orientation for all plates. Delamination was observed in some of the ductile fracture surfaces of the impact samples. To further study the splitting behavior and effects on impact toughness, a modified impact test (MCVN) specimen with side grooves was designed to intensify induced stresses parallel to the notch root and thus facilitate evaluation of delamination. Scanning electron microscopy combined with electron backscattered diffraction (EBSD) were used to evaluate the grain size, microstructural constituents, and crystallographic texture to determine the factors leading to delamination and the anisotropy in toughness. The ferrite grain size is mainly responsible for the differences in DBTTs between the L and T orientations. The higher DBTT in the D orientation observed in pipeline steels is attributed to crystallographic texture. The higher DBTT in the D direction is due to the higher volume fraction of grains having their {100} planes parallel or close to the primary fracture plane for the D orientation. An equation based on a new "brittleness parameter," based on an assessment of grain orientations based on EBSD data, was developed to predict the changes in DBTTs with respect to sample orientation based on grain size and texture. The calculated DBTTs correlated well with the experimental values. The {001} and {113} components are the main preferred orientations that cause brittleness in the D direction, since their {001} planes make an angle less than 20° with the primary fracture plane of the samples oriented in the D direction. It was also concluded that delamination occurs due to banded bainite regions that were oriented such that their {001} planes make a small angle with the rolling plane leading to degradation in crack arrestability. The texture of the banded regions consisted of {001}, {113} or {111} orientations. It was concluded that the {001} and {113} orientations promote splitting because their fracture strengths in the normal direction are low. The {111} orientation has a calculated fracture strength more than twice the {001} and {113} orientations and therefore banded regions with the {111} texture are more susceptible to cleavage fracture perpendicular to the normal direction.

Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling.

PubMed

Liu, Jianli; Lughofer, Edwin; Zeng, Xianyi

2015-01-01

Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties.

Transfer of the nationwide Czech soil survey data to a foreign soil classification - generating input parameters for a process-based soil erosion modelling approach

NASA Astrophysics Data System (ADS)

Beitlerová, Hana; Hieke, Falk; Žížala, Daniel; Kapička, Jiří; Keiser, Andreas; Schmidt, Jürgen; Schindewolf, Marcus

2017-04-01

Process-based erosion modelling is a developing and adequate tool to assess, simulate and understand the complex mechanisms of soil loss due to surface runoff. While the current state of available models includes powerful approaches, a major drawback is given by complex parametrization. A major input parameter for the physically based soil loss and deposition model EROSION 3D is represented by soil texture. However, as the model has been developed in Germany it is dependent on the German soil classification. To exploit data generated during a massive nationwide soil survey campaign taking place in the 1960s across the entire Czech Republic, a transfer from the Czech to the German or at least international (e.g. WRB) system is mandatory. During the survey the internal differentiation of grain sizes was realized in a two fractions approach, separating texture into solely above and below 0.01 mm rather than into clayey, silty and sandy textures. Consequently, the Czech system applies a classification of seven different textures based on the respective percentage of large and small particles, while in Germany 31 groups are essential. The followed approach of matching Czech soil survey data to the German system focusses on semi-logarithmic interpolation of the cumulative soil texture curve additionally on a regression equation based on a recent database of 128 soil pits. Furthermore, for each of the seven Czech texture classes a group of typically suitable classes of the German system was derived. A GIS-based spatial analysis to test approaches of interpolation the soil texture was carried out. First results show promising matches and pave the way to a Czech model application of EROSION 3D.

Balance the nodule shape and surroundings: a new multichannel image based convolutional neural network scheme on lung nodule diagnosis

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Zheng, Bin; Huang, Xia; Qian, Wei

2017-03-01

Deep learning is a trending promising method in medical image analysis area, but how to efficiently prepare the input image for the deep learning algorithms remains a challenge. In this paper, we introduced a novel artificial multichannel region of interest (ROI) generation procedure for convolutional neural networks (CNN). From LIDC database, we collected 54880 benign nodule samples and 59848 malignant nodule samples based on the radiologists' annotations. The proposed CNN consists of three pairs of convolutional layers and two fully connected layers. For each original ROI, two new ROIs were generated: one contains the segmented nodule which highlighted the nodule shape, and the other one contains the gradient of the original ROI which highlighted the textures. By combining the three channel images into a pseudo color ROI, the CNN was trained and tested on the new multichannel ROIs (multichannel ROI II). For the comparison, we generated another type of multichannel image by replacing the gradient image channel with a ROI contains whitened background region (multichannel ROI I). With the 5-fold cross validation evaluation method, the CNN using multichannel ROI II achieved the ROI based area under the curve (AUC) of 0.8823+/-0.0177, compared to the AUC of 0.8484+/-0.0204 generated by the original ROI. By calculating the average of ROI scores from one nodule, the lesion based AUC using multichannel ROI was 0.8793+/-0.0210. By comparing the convolved features maps from CNN using different types of ROIs, it can be noted that multichannel ROI II contains more accurate nodule shapes and surrounding textures.

Automatic detection of blood versus non-blood regions on intravascular ultrasound (IVUS) images using wavelet packet signatures

NASA Astrophysics Data System (ADS)

Katouzian, Amin; Baseri, Babak; Konofagou, Elisa E.; Laine, Andrew F.

2008-03-01

Intravascular ultrasound (IVUS) has been proven a reliable imaging modality that is widely employed in cardiac interventional procedures. It can provide morphologic as well as pathologic information on the occluded plaques in the coronary arteries. In this paper, we present a new technique using wavelet packet analysis that differentiates between blood and non-blood regions on the IVUS images. We utilized the multi-channel texture segmentation algorithm based on the discrete wavelet packet frames (DWPF). A k-mean clustering algorithm was deployed to partition the extracted textural features into blood and non-blood in an unsupervised fashion. Finally, the geometric and statistical information of the segmented regions was used to estimate the closest set of pixels to the lumen border and a spline curve was fitted to the set. The presented algorithm may be helpful in delineating the lumen border automatically and more reliably prior to the process of plaque characterization, especially with 40 MHz transducers, where appearance of the red blood cells renders the border detection more challenging, even manually. Experimental results are shown and they are quantitatively compared with manually traced borders by an expert. It is concluded that our two dimensional (2-D) algorithm, which is independent of the cardiac and catheter motions performs well in both in-vivo and in-vitro cases.

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters.

PubMed

Brynolfsson, Patrik; Nilsson, David; Torheim, Turid; Asklund, Thomas; Karlsson, Camilla Thellenberg; Trygg, Johan; Nyholm, Tufve; Garpebring, Anders

2017-06-22

In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

Textural features of dynamic contrast-enhanced MRI derived model-free and model-based parameter maps in glioma grading.

PubMed

Xie, Tian; Chen, Xiao; Fang, Jingqin; Kang, Houyi; Xue, Wei; Tong, Haipeng; Cao, Peng; Wang, Sumei; Yang, Yizeng; Zhang, Weiguo

2018-04-01

Presurgical glioma grading by dynamic contrast-enhanced MRI (DCE-MRI) has unresolved issues. The aim of this study was to investigate the ability of textural features derived from pharmacokinetic model-based or model-free parameter maps of DCE-MRI in discriminating between different grades of gliomas, and their correlation with pathological index. Retrospective. Forty-two adults with brain gliomas. 3.0T, including conventional anatomic sequences and DCE-MRI sequences (variable flip angle T1-weighted imaging and three-dimensional gradient echo volumetric imaging). Regions of interest on the cross-sectional images with maximal tumor lesion. Five commonly used textural features, including Energy, Entropy, Inertia, Correlation, and Inverse Difference Moment (IDM), were generated. All textural features of model-free parameters (initial area under curve [IAUC], maximal signal intensity [Max SI], maximal up-slope [Max Slope]) could effectively differentiate between grade II (n = 15), grade III (n = 13), and grade IV (n = 14) gliomas (P < 0.05). Two textural features, Entropy and IDM, of four DCE-MRI parameters, including Max SI, Max Slope (model-free parameters), vp (Extended Tofts), and vp (Patlak) could differentiate grade III and IV gliomas (P < 0.01) in four measurements. Both Entropy and IDM of Patlak-based K trans and vp could differentiate grade II (n = 15) from III (n = 13) gliomas (P < 0.01) in four measurements. No textural features of any DCE-MRI parameter maps could discriminate between subtypes of grade II and III gliomas (P < 0.05). Both Entropy and IDM of Extended Tofts- and Patlak-based vp showed highest area under curve in discriminating between grade III and IV gliomas. However, intraclass correlation coefficient (ICC) of these features revealed relatively lower inter-observer agreement. No significant correlation was found between microvascular density and textural features, compared with a moderate correlation found between cellular proliferation index and those features. Textural features of DCE-MRI parameter maps displayed a good ability in glioma grading. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1099-1111. © 2017 International Society for Magnetic Resonance in Medicine.

SU-C-201-04: Quantification of Perfusion Heterogeneity Based On Texture Analysis for Fully Automatic Detection of Ischemic Deficits From Myocardial Perfusion Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fang, Y; Huang, H; Su, T

Purpose: Texture-based quantification of image heterogeneity has been a popular topic for imaging studies in recent years. As previous studies mainly focus on oncological applications, we report our recent efforts of applying such techniques on cardiac perfusion imaging. A fully automated procedure has been developed to perform texture analysis for measuring the image heterogeneity. Clinical data were used to evaluate the preliminary performance of such methods. Methods: Myocardial perfusion images of Thallium-201 scans were collected from 293 patients with suspected coronary artery disease. Each subject underwent a Tl-201 scan and a percutaneous coronary intervention (PCI) within three months. The PCImore » Result was used as the gold standard of coronary ischemia of more than 70% stenosis. Each Tl-201 scan was spatially normalized to an image template for fully automatic segmentation of the LV. The segmented voxel intensities were then carried into the texture analysis with our open-source software Chang Gung Image Texture Analysis toolbox (CGITA). To evaluate the clinical performance of the image heterogeneity for detecting the coronary stenosis, receiver operating characteristic (ROC) analysis was used to compute the overall accuracy, sensitivity and specificity as well as the area under curve (AUC). Those indices were compared to those obtained from the commercially available semi-automatic software QPS. Results: With the fully automatic procedure to quantify heterogeneity from Tl-201 scans, we were able to achieve a good discrimination with good accuracy (74%), sensitivity (73%), specificity (77%) and AUC of 0.82. Such performance is similar to those obtained from the semi-automatic QPS software that gives a sensitivity of 71% and specificity of 77%. Conclusion: Based on fully automatic procedures of data processing, our preliminary data indicate that the image heterogeneity of myocardial perfusion imaging can provide useful information for automatic determination of the myocardial ischemia.« less

Characterization of subarctic vegetation using ground based remote sensing methods

NASA Astrophysics Data System (ADS)

Finnell, D.; Garnello, A.; Palace, M. W.; Sullivan, F.; Herrick, C.; Anderson, S. M.; Crill, P. M.; Varner, R. K.

2014-12-01

Stordalen mire is located at 68°21'N and 19°02'E in the Swedish subarctic. Climate monitoring has revealed a warming trend spanning the past 150 years affecting the mires ability to hold stable palsa/hummock mounds. The micro-topography of the landscape has begun to degrade into thaw ponds changing the vegetation cover from ombrothrophic to minerotrophic. Hummocks are ecologically important due to their ability to act as a carbon sinks. Thaw ponds and sphagnum rich transitional zones have been documented as sources of atmospheric CH4. An objective of this project is to determine if a high resolution three band camera (RGB) and a RGNIR camera could detect differences in vegetation over five different site types. Species composition was collected for 50 plots with ten repetitions for each site type: palsa/hummock, tall shrub, semi-wet, tall graminoid, and wet. Sites were differentiated based on dominating species and features consisting of open water presence, sphagnum spp. cover, graminoid spp. cover, or the presence of dry raised plateaus/mounds. A pole based camera mount was used to collect images at a height of ~2.44m from the ground. The images were cropped in post-processing to fit a one-square meter quadrat. Texture analysis was performed on all images, including entropy, lacunarity, and angular second momentum. Preliminary results suggested that site type influences the number of species present. The p-values for the ability to predict site type using a t-test range from <0.0001 to 0.0461. A stepwise discriminant analysis on site type vs. texture yielded a 10% misclassification rate. Through the use of a stepwise regression of texture variables, actual vs. predicted percent of vegetation coverage provided R squared values of 0.73, 0.71, 0.67, and 0.89 for C. bigelowii, R. chamaemorus, Sphagnum spp., and open water respectively. These data have provided some support to the notion that texture analyses can be used for classification of mire site types. Future work will involve scaling up from the 50 plots through the use of data collected from two unmanned aerial systems (UAS), as well as WorldView-2 satellite imagery collected during the years 2012-2014. Identification of methane flux regions will later be analyzed based on vegetation coverage to aid classification of increased emission zones within the mire.

Friction Stir Back Extrusion of Aluminium Alloys for Automotive Applications

NASA Astrophysics Data System (ADS)

Xu, Zeren

Since the invention of Friction Stir Welding in 1991 as a solid state joining technique, extensive scientific investigations have been carried out to understand fundamental aspects of material behaviors when processed by this technique, in order to optimize processing conditions as well as mechanical properties of the welds. Based on the basic principles of Friction Stir Welding, several derivatives have also been developed such as Friction Stir Processing, Friction Extrusion and Friction Stir Back Extrusion. Friction Stir Back Extrusion is a novel technique that is proposed recently and designed for fabricating tubes from lightweight alloys. Some preliminary results have been reported regarding microstructure and mechanical properties of Friction Stir Back Extrusion processed AZ31 magnesium alloy, however, systematic study and in-depth investigations are still needed to understand the materials behaviors and underlying mechanisms when subjected to Friction Stir Back Extrusion, especially for age-hardenable Al alloys. In the present study, Friction Stir Back Extrusion processed AA6063-T5 and AA7075-T6 alloys are analyzed with respect to grain structure evolution, micro-texture change, recrystallization mechanisms, precipitation sequence as well as mechanical properties. Optical Microscopy, Electron Backscatter Diffraction, Transmission Electron Microscopy, Vickers Hardness measurements and uniaxial tensile tests are carried out to characterize the microstructural change as well as micro and macro mechanical properties of the processed tubes. Special attention is paid to the micro-texture evolution across the entire tube and dynamic recrystallization mechanisms that are responsible for grain refinement. Significant grain refinement has been observed near the processing zone while the tube wall is characterized by inhomogeneous grain structure across the thickness for both alloys. Dissolution of existing precipitates is noticed under the thermal hysterias imposed by Friction Stir Back Extrusion process, resulting in decreased strength but improved elongation of the processed tubes; a post-process aging step can effectively restore the mechanical properties of the processed tubes by allowing for the reprecipitation of solute elements in the form of fine, dispersed precipitates. Texture analysis performed for AA6063 alloy suggests the dominance of simple shear type textures with clear transition from initial texture to stable B/ ?B components via intermediate types that are stable under moderate strain levels. In order to identify the texture components properly, rigid body rotations are applied to the existing coordinate system to align it to local shear reference frame. Surprisingly, for AA7075 tubes, and fibers are observed to be the dominant texture components in the transition region as well as thermomechanically affected zone while the processing zone is characterized by random texture. The underlying mechanisms responsible for the formation of random texture are discussed in Chapter 5 based on Electron Backscatter Diffraction analysis. Comparative discussions are also carried out for the recrystallization mechanisms that are responsible for grain structure evolution of both alloys. Continuous grain subdivision and reorientation is cited as the dominant mechanism for the recrystallization of AA6063 alloys, while dynamic recrystallization occurs mainly in the form of Geometric Dynamic Recrystallization and progressive subgrain rotations near grain boundaries in AA7075 alloys.

Efficient iris texture analysis method based on Gabor ordinal measures

NASA Astrophysics Data System (ADS)

Tajouri, Imen; Aydi, Walid; Ghorbel, Ahmed; Masmoudi, Nouri

2017-07-01

With the remarkably increasing interest directed to the security dimension, the iris recognition process is considered to stand as one of the most versatile technique critically useful for the biometric identification and authentication process. This is mainly due to every individual's unique iris texture. A modestly conceived efficient approach relevant to the feature extraction process is proposed. In the first place, iris zigzag "collarette" is extracted from the rest of the image by means of the circular Hough transform, as it includes the most significant regions lying in the iris texture. In the second place, the linear Hough transform is used for the eyelids' detection purpose while the median filter is applied for the eyelashes' removal. Then, a special technique combining the richness of Gabor features and the compactness of ordinal measures is implemented for the feature extraction process, so that a discriminative feature representation for every individual can be achieved. Subsequently, the modified Hamming distance is used for the matching process. Indeed, the advanced procedure turns out to be reliable, as compared to some of the state-of-the-art approaches, with a recognition rate of 99.98%, 98.12%, and 95.02% on CASIAV1.0, CASIAV3.0, and IIT Delhi V1 iris databases, respectively.

Assessment of visual landscape quality using IKONOS imagery.

PubMed

Ozkan, Ulas Yunus

2014-07-01

The assessment of visual landscape quality is of importance to the management of urban woodlands. Satellite remote sensing may be used for this purpose as a substitute for traditional survey techniques that are both labour-intensive and time-consuming. This study examines the association between the quality of the perceived visual landscape in urban woodlands and texture measures extracted from IKONOS satellite data, which features 4-m spatial resolution and four spectral bands. The study was conducted in the woodlands of Istanbul (the most important element of urban mosaic) lying along both shores of the Bosporus Strait. The visual quality assessment applied in this study is based on the perceptual approach and was performed via a survey of expressed preferences. For this purpose, representative photographs of real scenery were used to elicit observers' preferences. A slide show comprising 33 images was presented to a group of 153 volunteers (all undergraduate students), and they were asked to rate the visual quality of each on a 10-point scale (1 for very low visual quality, 10 for very high). Average visual quality scores were calculated for landscape. Texture measures were acquired using the two methods: pixel-based and object-based. Pixel-based texture measures were extracted from the first principle component (PC1) image. Object-based texture measures were extracted by using the original four bands. The association between image texture measures and perceived visual landscape quality was tested via Pearson's correlation coefficient. The analysis found a strong linear association between image texture measures and visual quality. The highest correlation coefficient was calculated between standard deviation of gray levels (SDGL) (one of the pixel-based texture measures) and visual quality (r = 0.82, P < 0.05). The results showed that perceived visual quality of urban woodland landscapes can be estimated by using texture measures extracted from satellite data in combination with appropriate modelling techniques.

Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm.

PubMed

Al Ajmi, Eiman; Forghani, Behzad; Reinhold, Caroline; Bayat, Maryam; Forghani, Reza

2018-06-01

There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm. Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set. Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis. Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours. • We present and validate a paradigm for texture analysis of DECT scans. • Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis. • DECT texture analysis has high accura\\cy for diagnosis of benign parotid tumours. • DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.

An application to pulmonary emphysema classification based on model of texton learning by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryojiro; Kanematsu, Masayuki; Fujita, Hiroshi

2012-03-01

We aim at using a new texton based texture classification method in the classification of pulmonary emphysema in computed tomography (CT) images of the lungs. Different from conventional computer-aided diagnosis (CAD) pulmonary emphysema classification methods, in this paper, firstly, the dictionary of texton is learned via applying sparse representation(SR) to image patches in the training dataset. Then the SR coefficients of the test images over the dictionary are used to construct the histograms for texture presentations. Finally, classification is performed by using a nearest neighbor classifier with a histogram dissimilarity measure as distance. The proposed approach is tested on 3840 annotated regions of interest consisting of normal tissue and mild, moderate and severe pulmonary emphysema of three subtypes. The performance of the proposed system, with an accuracy of about 88%, is comparably higher than state of the art method based on the basic rotation invariant local binary pattern histograms and the texture classification method based on texton learning by k-means, which performs almost the best among other approaches in the literature.

Cell segmentation in time-lapse fluorescence microscopy with temporally varying sub-cellular fusion protein patterns.

PubMed

Bunyak, Filiz; Palaniappan, Kannappan; Chagin, Vadim; Cardoso, M

2009-01-01

Fluorescently tagged proteins such as GFP-PCNA produce rich dynamically varying textural patterns of foci distributed in the nucleus. This enables the behavioral study of sub-cellular structures during different phases of the cell cycle. The varying punctuate patterns of fluorescence, drastic changes in SNR, shape and position during mitosis and abundance of touching cells, however, require more sophisticated algorithms for reliable automatic cell segmentation and lineage analysis. Since the cell nuclei are non-uniform in appearance, a distribution-based modeling of foreground classes is essential. The recently proposed graph partitioning active contours (GPAC) algorithm supports region descriptors and flexible distance metrics. We extend GPAC for fluorescence-based cell segmentation using regional density functions and dramatically improve its efficiency for segmentation from O(N(4)) to O(N(2)), for an image with N(2) pixels, making it practical and scalable for high throughput microscopy imaging studies.

Fabric defect detection based on faster R-CNN

NASA Astrophysics Data System (ADS)

Liu, Zhoufeng; Liu, Xianghui; Li, Chunlei; Li, Bicao; Wang, Baorui

2018-04-01

In order to effectively detect the defects for fabric image with complex texture, this paper proposed a novel detection algorithm based on an end-to-end convolutional neural network. First, the proposal regions are generated by RPN (regional proposal Network). Then, Fast Region-based Convolutional Network method (Fast R-CNN) is adopted to determine whether the proposal regions extracted by RPN is a defect or not. Finally, Soft-NMS (non-maximum suppression) and data augmentation strategies are utilized to improve the detection precision. Experimental results demonstrate that the proposed method can locate the fabric defect region with higher accuracy compared with the state-of- art, and has better adaptability to all kinds of the fabric image.

Multifractal modeling, segmentation, prediction, and statistical validation of posterior fossa tumors

NASA Astrophysics Data System (ADS)

Islam, Atiq; Iftekharuddin, Khan M.; Ogg, Robert J.; Laningham, Fred H.; Sivakumar, Bhuvaneswari

2008-03-01

In this paper, we characterize the tumor texture in pediatric brain magnetic resonance images (MRIs) and exploit these features for automatic segmentation of posterior fossa (PF) tumors. We focus on PF tumor because of the prevalence of such tumor in pediatric patients. Due to varying appearance in MRI, we propose to model the tumor texture with a multi-fractal process, such as a multi-fractional Brownian motion (mBm). In mBm, the time-varying Holder exponent provides flexibility in modeling irregular tumor texture. We develop a detailed mathematical framework for mBm in two-dimension and propose a novel algorithm to estimate the multi-fractal structure of tissue texture in brain MRI based on wavelet coefficients. This wavelet based multi-fractal feature along with MR image intensity and a regular fractal feature obtained using our existing piecewise-triangular-prism-surface-area (PTPSA) method, are fused in segmenting PF tumor and non-tumor regions in brain T1, T2, and FLAIR MR images respectively. We also demonstrate a non-patient-specific automated tumor prediction scheme based on these image features. We experimentally show the tumor discriminating power of our novel multi-fractal texture along with intensity and fractal features in automated tumor segmentation and statistical prediction. To evaluate the performance of our tumor prediction scheme, we obtain ROCs and demonstrate how sharply the curves reach the specificity of 1.0 sacrificing minimal sensitivity. Experimental results show the effectiveness of our proposed techniques in automatic detection of PF tumors in pediatric MRIs.

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

PubMed

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

Deformation processes and weakening mechanisms within the frictional viscous transition zone of major crustal-scale faults: insights from the Great Glen Fault Zone, Scotland

NASA Astrophysics Data System (ADS)

Stewart, M.; Holdsworth, R. E.; Strachan, R. A.

2000-05-01

The Great Glen Fault Zone (GGFZ), Scotland, is a typical example of a crustal-scale, reactivated strike-slip fault within the continental crust. Analysis of intensely strained fault rocks from the core of the GGFZ near Fort William provides a unique insight into the nature of deformation associated with the main phase of (sinistral) movements along the fault zone. In this region, an exhumed sequence of complex mid-crustal deformation textures that developed in the region of the frictional-viscous transition (ca. 8-15 km depth) is preserved. Fault rock fabrics vary from mylonitic in quartzites to cataclastic in micaceous shear zones and feldspathic psammites. Protolith mineralogy exerted a strong control on the initial textural development and distribution of the fault rocks. At lower strains, crystal-plastic deformation occurred in quartz-dominated lithologies to produce mylonites simultaneously with widespread fracturing and cataclasis in feldspar- and mica-dominated rocks. At higher strains, shearing appears to increasingly localise into interconnected networks of cataclastic shear zones, many of which are strongly foliated. Textures indicative of fluid-assisted diffusive mass transfer mechanisms are widespread in such regions and suggest that a hydrous fluid-assisted, grainsize-controlled switch in deformation behaviour followed the brittle comminution of grains. The fault zone textural evolution implies that a strain-induced, fluid-assisted shallowing and narrowing of the frictional-viscous transition occurred with increasing strain. It is proposed that this led to an overall weakening of the fault zone and that equivalent processes may occur along many other long-lived, crustal-scale dislocations.

Quantitative analysis of ultrasonic images of fibrotic liver using co-occurrence matrix based on multi-Rayleigh model

NASA Astrophysics Data System (ADS)

Isono, Hiroshi; Hirata, Shinnosuke; Hachiya, Hiroyuki

2015-07-01

In medical ultrasonic images of liver disease, a texture with a speckle pattern indicates a microscopic structure such as nodules surrounded by fibrous tissues in hepatitis or cirrhosis. We have been applying texture analysis based on a co-occurrence matrix to ultrasonic images of fibrotic liver for quantitative tissue characterization. A co-occurrence matrix consists of the probability distribution of brightness of pixel pairs specified with spatial parameters and gives new information on liver disease. Ultrasonic images of different types of fibrotic liver were simulated and the texture-feature contrast was calculated to quantify the co-occurrence matrices generated from the images. The results show that the contrast converges with a value that can be theoretically estimated using a multi-Rayleigh model of echo signal amplitude distribution. We also found that the contrast value increases as liver fibrosis progresses and fluctuates depending on the size of fibrotic structure.

General Approach for Rock Classification Based on Digital Image Analysis of Electrical Borehole Wall Images

NASA Astrophysics Data System (ADS)

Linek, M.; Jungmann, M.; Berlage, T.; Clauser, C.

2005-12-01

Within the Ocean Drilling Program (ODP), image logging tools have been routinely deployed such as the Formation MicroScanner (FMS) or the Resistivity-At-Bit (RAB) tools. Both logging methods are based on resistivity measurements at the borehole wall and therefore are sensitive to conductivity contrasts, which are mapped in color scale images. These images are commonly used to study the structure of the sedimentary rocks and the oceanic crust (petrologic fabric, fractures, veins, etc.). So far, mapping of lithology from electrical images is purely based on visual inspection and subjective interpretation. We apply digital image analysis on electrical borehole wall images in order to develop a method, which augments objective rock identification. We focus on supervised textural pattern recognition which studies the spatial gray level distribution with respect to certain rock types. FMS image intervals of rock classes known from core data are taken in order to train textural characteristics for each class. A so-called gray level co-occurrence matrix is computed by counting the occurrence of a pair of gray levels that are a certain distant apart. Once the matrix for an image interval is computed, we calculate the image contrast, homogeneity, energy, and entropy. We assign characteristic textural features to different rock types by reducing the image information into a small set of descriptive features. Once a discriminating set of texture features for each rock type is found, we are able to discriminate the entire FMS images regarding the trained rock type classification. A rock classification based on texture features enables quantitative lithology mapping and is characterized by a high repeatability, in contrast to a purely visual subjective image interpretation. We show examples for the rock classification between breccias, pillows, massive units, and horizontally bedded tuffs based on ODP image data.

Structure of Profiled Crystals Based on Solid Solutions of Bi2Te3 and Their X-Ray Diagnostics

NASA Astrophysics Data System (ADS)

Voronin, A. I.; Bublik, V. T.; Tabachkova, N. Yu.; Belov, Yu. M.

2011-05-01

In this work, we used x-ray structural diagnostic data to reveal the formation of structural regularities in profiled polycrystalline ingots based on Bi and Sb chalcogenide solid solutions. In Bi2Te3 lattice crystals, the solid phase grows such that the cleavage surfaces are perpendicular to the crystallization front. The crystallization singularity determines the nature of the growth texture. Because texture is an important factor determining the anisotropy of properties, which in turn determines the suitability of an ingot for production of modules and the possibility of figure of merit improvement, its diagnostics is an important issue for technology testing. Examples of texture analysis using the method of straight pole figure (SPF) construction for profiled crystals are provided. The structure of the surface layers in the profiled ingots was studied after electroerosion cutting. In addition, the method of estimation of the disturbed layer depth based on the nature of texture changes was used.

Computer-aided diagnosis of cavernous malformations in brain MR images.

PubMed

Wang, Huiquan; Ahmed, S Nizam; Mandal, Mrinal

2018-06-01

Cavernous malformation or cavernoma is one of the most common epileptogenic lesions. It is a type of brain vessel abnormality that can cause serious symptoms such as seizures, intracerebral hemorrhage, and various neurological disorders. Manual detection of cavernomas by physicians in a large set of brain MRI slices is a time-consuming and labor-intensive task and often delays diagnosis. In this paper, we propose a computer-aided diagnosis (CAD) system for cavernomas based on T2-weighted axial plane MRI image analysis. The proposed technique first extracts the brain area based on atlas registration and active contour model, and then performs template matching to obtain candidate cavernoma regions. Texture, the histogram of oriented gradients and local binary pattern features of each candidate region are calculated, and principal component analysis is applied to reduce the feature dimensionality. Support vector machines (SVMs) are finally used to classify each region into cavernoma or non-cavernoma so that most of the false positives (obtained by template matching) are eliminated. The performance of the proposed CAD system is evaluated and experimental results show that it provides superior performance in cavernoma detection compared to existing techniques. Copyright © 2018 Elsevier Ltd. All rights reserved.

Evaluation of the Williams-type model for barley yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1981-01-01

The Williams-type yield model is based on multiple regression analysis of historial time series data at CRD level pooled to regional level (groups of similar CRDs). Basic variables considered in the analysis include USDA yield, monthly mean temperature, monthly precipitation, soil texture and topographic information, and variables derived from these. Technologic trend is represented by piecewise linear and/or quadratic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-1979) demonstrate that biases are small and performance based on root mean square appears to be acceptable for the intended AgRISTARS large area applications. The model is objective, adequate, timely, simple, and not costly. It consideres scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma

PubMed Central

Kebir, Sied; Khurshid, Zain; Gaertner, Florian C.; Essler, Markus; Hattingen, Elke; Fimmers, Rolf; Scheffler, Björn; Herrlinger, Ulrich; Bundschuh, Ralph A.; Glas, Martin

2017-01-01

Rationale Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror tumor uptake heterogeneity; some of them may be associated with tumor progression. Methods Fourteen patients with HGG and suspected of PSP underwent FET-PET imaging. A set of 19 conventional and textural FET-PET features were evaluated and subjected to unsupervised consensus clustering. The final diagnosis of true progression vs. PSP was based on follow-up MRI using RANO criteria. Results Three robust clusters have been identified based on 10 predominantly textural FET-PET features. None of the patients with PSP fell into cluster 2, which was associated with high values for textural FET-PET markers of uptake heterogeneity. Three out of 4 patients with PSP were assigned to cluster 3 that was largely associated with low values of textural FET-PET features. By comparison, tumor-to-normal brain ratio (TNRmax) at the optimal cutoff 2.1 was less predictive of PSP (negative predictive value 57% for detecting true progression, p=0.07 vs. 75% with cluster 3, p=0.04). Principal Conclusions Clustering based on textural O-(2-[18F]fluoroethyl)-L-tyrosine PET features may provide valuable information in assessing the elusive phenomenon of pseudoprogression. PMID:28030820

Texture in thin film silicides and germanides: A review

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Schutter, B., E-mail: bob.deschutter@ugent.be; De Keyser, K.; Detavernier, C.

Silicides and germanides are compounds consisting of a metal and silicon or germanium. In the microelectronics industry, silicides are the material of choice for contacting silicon based devices (over the years, CoSi{sub 2}, C54-TiSi{sub 2}, and NiSi have been adopted), while germanides are considered as a top candidate for contacting future germanium based electronics. Since also strain engineering through the use of Si{sub 1−x}Ge{sub x} in the source/drain/gate regions of MOSFET devices is an important technique for improving device characteristics in modern Si-based microelectronics industry, a profound understanding of the formation of silicide/germanide contacts to silicon and germanium is ofmore » utmost importance. The crystallographic texture of these films, which is defined as the statistical distribution of the orientation of the grains in the film, has been the subject of scientific studies since the 1970s. Different types of texture like epitaxy, axiotaxy, fiber, or combinations thereof have been observed in such films. In recent years, it has become increasingly clear that film texture can have a profound influence on the formation and stability of silicide/germanide contacts, as it controls the type and orientation of grain boundaries (affecting diffusion and agglomeration) and the interface energy (affecting nucleation during the solid-state reaction). Furthermore, the texture also has an impact on the electrical characteristics of the contact, as the orientation and size of individual grains influences functional properties such as contact resistance and sheet resistance and will induce local variations in strain and Schottky barrier height. This review aims to give a comprehensive overview of the scientific work that has been published in the field of texture studies on thin film silicide/germanide contacts.« less

Texture in thin film silicides and germanides: A review

NASA Astrophysics Data System (ADS)

De Schutter, B.; De Keyser, K.; Lavoie, C.; Detavernier, C.

2016-09-01

Silicides and germanides are compounds consisting of a metal and silicon or germanium. In the microelectronics industry, silicides are the material of choice for contacting silicon based devices (over the years, CoSi2, C54-TiSi2, and NiSi have been adopted), while germanides are considered as a top candidate for contacting future germanium based electronics. Since also strain engineering through the use of Si1-xGex in the source/drain/gate regions of MOSFET devices is an important technique for improving device characteristics in modern Si-based microelectronics industry, a profound understanding of the formation of silicide/germanide contacts to silicon and germanium is of utmost importance. The crystallographic texture of these films, which is defined as the statistical distribution of the orientation of the grains in the film, has been the subject of scientific studies since the 1970s. Different types of texture like epitaxy, axiotaxy, fiber, or combinations thereof have been observed in such films. In recent years, it has become increasingly clear that film texture can have a profound influence on the formation and stability of silicide/germanide contacts, as it controls the type and orientation of grain boundaries (affecting diffusion and agglomeration) and the interface energy (affecting nucleation during the solid-state reaction). Furthermore, the texture also has an impact on the electrical characteristics of the contact, as the orientation and size of individual grains influences functional properties such as contact resistance and sheet resistance and will induce local variations in strain and Schottky barrier height. This review aims to give a comprehensive overview of the scientific work that has been published in the field of texture studies on thin film silicide/germanide contacts.

Textural characterization of histopathological images for oral sub-mucous fibrosis detection.

PubMed

Krishnan, M Muthu Rama; Shah, Pratik; Choudhary, Anirudh; Chakraborty, Chandan; Paul, Ranjan Rashmi; Ray, Ajoy K

2011-10-01

In the field of quantitative microscopy, textural information plays a significant role very often in tissue characterization and diagnosis, in addition to morphology and intensity. The aim of this work is to improve the classification accuracy based on textural features for the development of a computer assisted screening of oral sub-mucous fibrosis (OSF). In fact, a systematic approach is introduced in order to grade the histopathological tissue sections into normal, OSF without dysplasia and OSF with dysplasia, which would help the oral onco-pathologists to screen the subjects rapidly. In totality, 71 textural features are extracted from epithelial region of the tissue sections using various wavelet families, Gabor-wavelet, local binary pattern, fractal dimension and Brownian motion curve, followed by preprocessing and segmentation. Wavelet families contribute a common set of 9 features, out of which 8 are significant and other 61 out of 62 obtained from the rest of the extractors are also statistically significant (p<0.05) in discriminating the three stages. Based on mean distance criteria, the best wavelet family (i.e., biorthogonal3.1 (bior3.1)) is selected for classifier design. support vector machine (SVM) is trained by 146 samples based on 69 textural features and its classification accuracy is computed for each of the combinations of wavelet family and rest of the extractors. Finally, it has been investigated that bior3.1 wavelet coefficients leads to higher accuracy (88.38%) in combination with LBP and Gabor wavelet features through three-fold cross validation. Results are shown and discussed in detail. It is shown that combining more than one texture measure instead of using just one might improve the overall accuracy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Computer-aided diagnosis of melanoma using border and wavelet-based texture analysis.

PubMed

Garnavi, Rahil; Aldeen, Mohammad; Bailey, James

2012-11-01

This paper presents a novel computer-aided diagnosis system for melanoma. The novelty lies in the optimised selection and integration of features derived from textural, borderbased and geometrical properties of the melanoma lesion. The texture features are derived from using wavelet-decomposition, the border features are derived from constructing a boundaryseries model of the lesion border and analysing it in spatial and frequency domains, and the geometry features are derived from shape indexes. The optimised selection of features is achieved by using the Gain-Ratio method, which is shown to be computationally efficient for melanoma diagnosis application. Classification is done through the use of four classifiers; namely, Support Vector Machine, Random Forest, Logistic Model Tree and Hidden Naive Bayes. The proposed diagnostic system is applied on a set of 289 dermoscopy images (114 malignant, 175 benign) partitioned into train, validation and test image sets. The system achieves and accuracy of 91.26% and AUC value of 0.937, when 23 features are used. Other important findings include (i) the clear advantage gained in complementing texture with border and geometry features, compared to using texture information only, and (ii) higher contribution of texture features than border-based features in the optimised feature set.

A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer.

PubMed

Neofytou, Marios S; Tanos, Vasilis; Pattichis, Marios S; Pattichis, Constantinos S; Kyriacou, Efthyvoulos C; Koutsouris, Dimitris D

2007-11-29

In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i) the distance from the tissue (panoramic vs close up), (ii) difference in viewing angles and (iii) color correction. We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 x 576 pixels and 24 bits color for: (i) a variety of testing targets from a color palette with a known color distribution, (ii) different viewing angles, (iv) two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better approximations to the original images. Within the proposed protocol, for human ROIs, we have found that there is a large number of texture features that showed significant differences between normal and abnormal endometrium. This study provides a standardized protocol for avoiding any significant texture feature differences that may arise due to variability in the acquisition procedure or the lack of color correction. After applying the protocol, we have found that significant differences in texture features will only be due to the fact that the features were extracted from different types of tissue (normal vs abnormal).

Clustered-dot halftoning with direct binary search.

PubMed

Goyal, Puneet; Gupta, Madhur; Staelin, Carl; Fischer, Mani; Shacham, Omri; Allebach, Jan P

2013-02-01

In this paper, we present a new algorithm for aperiodic clustered-dot halftoning based on direct binary search (DBS). The DBS optimization framework has been modified for designing clustered-dot texture, by using filters with different sizes in the initialization and update steps of the algorithm. Following an intuitive explanation of how the clustered-dot texture results from this modified framework, we derive a closed-form cost metric which, when minimized, equivalently generates stochastic clustered-dot texture. An analysis of the cost metric and its influence on the texture quality is presented, which is followed by a modification to the cost metric to reduce computational cost and to make it more suitable for screen design.

Texture-adaptive hyperspectral video acquisition system with a spatial light modulator

NASA Astrophysics Data System (ADS)

Fang, Xiaojing; Feng, Jiao; Wang, Yongjin

2014-10-01

We present a new hybrid camera system based on spatial light modulator (SLM) to capture texture-adaptive high-resolution hyperspectral video. The hybrid camera system records a hyperspectral video with low spatial resolution using a gray camera and a high-spatial resolution video using a RGB camera. The hyperspectral video is subsampled by the SLM. The subsampled points can be adaptively selected according to the texture characteristic of the scene by combining with digital imaging analysis and computational processing. In this paper, we propose an adaptive sampling method utilizing texture segmentation and wavelet transform (WT). We also demonstrate the effectiveness of the sampled pattern on the SLM with the proposed method.

Hyperspectral remote sensing exploration of carbonatite - an example from Epembe, Kunene region, Namibia

NASA Astrophysics Data System (ADS)

Zimmermann, Robert; Brandmeier, Melanie; Andreani, Louis; Gloaguen, Richard

2015-04-01

Remote sensing data can provide valuable information about ore deposits and their alteration zones at surface level. High spectral and spatial resolution of the data is essential for detailed mapping of mineral abundances and related structures. Carbonatites are well known for hosting economic enrichments in REE, Ta, Nb and P (Jones et al. 2013). These make them a preferential target for exploration for those critical elements. In this study we show how combining geomorphic, textural and spectral data improves classification result. We selected a site with a well-known occurrence in northern Namibia: the Epembe dyke. For analysis LANDSAT 8, SRTM and airborne hyperspectral (HyMap) data were chosen. The overlapping data allows a multi-scale and multi-resolution approach. Results from data analysis were validated during fieldwork in 2014. Data was corrected for atmospherical and geometrical effects. Image classification, mineral mapping and tectonic geomorphology allow a refinement of the geological map by lithological mapping in a second step. Detailed mineral abundance maps were computed using spectral unmixing techniques. These techniques are well suited to map abundances of carbonate minerals, but not to discriminate the carbonatite itself from surrounding rocks with similar spectral signatures. Thus, geometric indices were calculated using tectonic geomorphology and textures. For this purpose the TecDEM-toolbox (SHAHZAD & GLOAGUEN 2011) was applied to the SRTM-data for geomorphic analysis. Textural indices (e.g. uniformity, entropy, angular second moment) were derived from HyMap and SRTM by a grey-level co-occurrence matrix (CLAUSI 2002). The carbonatite in the study area is ridge-forming and shows a narrow linear feature in the textural bands. Spectral and geometric information were combined using kohonen Self-Organizing Maps (SOM) for unsupervised clustering. The resulting class spectra were visually compared and interpreted. Classes with similar signatures were merged according to geological context. The major conclusions are: 1. Carbonate minerals can be mapped using spectral unmixing techniques. 2. Carbonatites are associated with specific geometric pattern 3. The combination of spectral and geometric information improves classification result and reduces misclassification. References Clausi, D. A. (2002): An analysis of co-occurrence texture statistics as a function of grey-level quantization. - Canadian Journal of Remote Sensing, 28 (1), 45-62 Jones, A. P., Genge, M. and Carmody, L (2013): Carbonate Melts and Carbonatites. - Reviews in Mineralogy & Geochemistry, 75, 289-322 Shahzad, F. & Gloaguen, R. (2011): TecDEM: A MATLAB based toolbox for tectonic geomorphology, Part 2: Surface dynamics and basin analysis. - Computers and Geosciences, 37 (2), 261-271

Vegetation classification of Coffea on Hawaii Island using WorldView-2 satellite imagery

NASA Astrophysics Data System (ADS)

Gaertner, Julie; Genovese, Vanessa Brooks; Potter, Christopher; Sewake, Kelvin; Manoukis, Nicholas C.

2017-10-01

Coffee is an important crop in tropical regions of the world; about 125 million people depend on coffee agriculture for their livelihoods. Understanding the spatial extent of coffee fields is useful for management and control of coffee pests such as Hypothenemus hampei and other pests that use coffee fruit as a host for immature stages such as the Mediterranean fruit fly, for economic planning, and for following changes in coffee agroecosystems over time. We present two methods for detecting Coffea arabica fields using remote sensing and geospatial technologies on WorldView-2 high-resolution spectral data of the Kona region of Hawaii Island. The first method, a pixel-based method using a maximum likelihood algorithm, attained 72% producer accuracy and 69% user accuracy (68% overall accuracy) based on analysis of 104 ground truth testing polygons. The second method, an object-based image analysis (OBIA) method, considered both spectral and textural information and improved accuracy, resulting in 76% producer accuracy and 94% user accuracy (81% overall accuracy) for the same testing areas. We conclude that the OBIA method is useful for detecting coffee fields grown in the open and use it to estimate the distribution of about 1050 hectares under coffee agriculture in the Kona region in 2012.

Paleoarchean trace fossils in altered volcanic glass.

PubMed

Staudigel, Hubert; Furnes, Harald; DeWit, Maarten

2015-06-02

Microbial corrosion textures in volcanic glass from Cenozoic seafloor basalts and the corresponding titanite replacement microtextures in metamorphosed Paleoarchean pillow lavas have been interpreted as evidence for a deep biosphere dating back in time through the earliest periods of preserved life on earth. This interpretation has been recently challenged for Paleoarchean titanite replacement textures based on textural and geochronological data from pillow lavas in the Hooggenoeg Complex of the Barberton Greenstone Belt in South Africa. We use this controversy to explore the strengths and weaknesses of arguments made in support or rejection of the biogenicity interpretation of bioalteration trace fossils in Cenozoic basalt glasses and their putative equivalents in Paleoarchean greenstones. Our analysis suggests that biogenicity cannot be taken for granted for all titanite-based textures in metamorphosed basalt glass, but a cautious and critical evaluation of evidence suggests that biogenicity remains the most likely interpretation for previously described titanite microtextures in Paleoarchean pillow lavas.

Paleoarchean trace fossils in altered volcanic glass

PubMed Central

Staudigel, Hubert; Furnes, Harald; DeWit, Maarten

2015-01-01

Microbial corrosion textures in volcanic glass from Cenozoic seafloor basalts and the corresponding titanite replacement microtextures in metamorphosed Paleoarchean pillow lavas have been interpreted as evidence for a deep biosphere dating back in time through the earliest periods of preserved life on earth. This interpretation has been recently challenged for Paleoarchean titanite replacement textures based on textural and geochronological data from pillow lavas in the Hooggenoeg Complex of the Barberton Greenstone Belt in South Africa. We use this controversy to explore the strengths and weaknesses of arguments made in support or rejection of the biogenicity interpretation of bioalteration trace fossils in Cenozoic basalt glasses and their putative equivalents in Paleoarchean greenstones. Our analysis suggests that biogenicity cannot be taken for granted for all titanite-based textures in metamorphosed basalt glass, but a cautious and critical evaluation of evidence suggests that biogenicity remains the most likely interpretation for previously described titanite microtextures in Paleoarchean pillow lavas. PMID:26038543

Advecting Procedural Textures for 2D Flow Animation

NASA Technical Reports Server (NTRS)

Kao, David; Pang, Alex; Moran, Pat (Technical Monitor)

2001-01-01

This paper proposes the use of specially generated 3D procedural textures for visualizing steady state 2D flow fields. We use the flow field to advect and animate the texture over time. However, using standard texture advection techniques and arbitrary textures will introduce some undesirable effects such as: (a) expanding texture from a critical source point, (b) streaking pattern from the boundary of the flowfield, (c) crowding of advected textures near an attracting spiral or sink, and (d) absent or lack of textures in some regions of the flow. This paper proposes a number of strategies to solve these problems. We demonstrate how the technique works using both synthetic data and computational fluid dynamics data.

Microstructural and Textural Differences Induced by Water and Furnace Cooling in Commercially Pure Zr Annealed in the α + β Region

NASA Astrophysics Data System (ADS)

Chai, Linjiang; Wang, Tingting; Ren, Yi; Song, Bo; Guo, Ning; Chen, Liangyu

2018-07-01

In this work, a commercially pure Zr sheet with a typical bimodal basal texture was annealed in an α + β region and then subjected to different coolings (in water and furnace). Microstructures and textures of both the as-received and the heat-treated specimens were investigated by electron channeling contrast imaging and electron backscatter diffraction techniques. Results show that a duplex microstructure consisting of untransformed bulk α grains and twinned martensitic plates is produced in the water-cooled specimen, which possesses a weakened texture compared to the initial one. For the specimen cooled in furnace, however, a uniform microstructure fully comprised of coarser equiaxed grains with a strengthened texture is obtained. Analyses reveal that the rapid cooling in water could suppress variant selection behaviors during β → α transformation and allow α plates with scattered orientations to be nucleated inside β phases, contributing to the weakened texture. In contrast, during slow cooling in furnace, β boundaries would act as preferred nucleation sites of α embryos, resulting in a strong variant selection that accounts for the intensified texture.

Microstructural and Textural Differences Induced by Water and Furnace Cooling in Commercially Pure Zr Annealed in the α + β Region

NASA Astrophysics Data System (ADS)

Chai, Linjiang; Wang, Tingting; Ren, Yi; Song, Bo; Guo, Ning; Chen, Liangyu

2018-03-01

In this work, a commercially pure Zr sheet with a typical bimodal basal texture was annealed in an α + β region and then subjected to different coolings (in water and furnace). Microstructures and textures of both the as-received and the heat-treated specimens were investigated by electron channeling contrast imaging and electron backscatter diffraction techniques. Results show that a duplex microstructure consisting of untransformed bulk α grains and twinned martensitic plates is produced in the water-cooled specimen, which possesses a weakened texture compared to the initial one. For the specimen cooled in furnace, however, a uniform microstructure fully comprised of coarser equiaxed grains with a strengthened texture is obtained. Analyses reveal that the rapid cooling in water could suppress variant selection behaviors during β → α transformation and allow α plates with scattered orientations to be nucleated inside β phases, contributing to the weakened texture. In contrast, during slow cooling in furnace, β boundaries would act as preferred nucleation sites of α embryos, resulting in a strong variant selection that accounts for the intensified texture.

Crystallographic texture and microstructural changes in fusion welds of recrystallized Zry-4 rolled plates

NASA Astrophysics Data System (ADS)

Moya Riffo, A.; Vicente Alvarez, M. A.; Santisteban, J. R.; Vizcaino, P.; Limandri, S.; Daymond, M. R.; Kerr, D.; Okasinski, J.; Almer, J.; Vogel, S. C.

2017-05-01

This work presents a detailed characterization of the microstructural and crystallographic texture changes observed in the transition region in a weld between two Zircaloy-4 cold rolled and recrystallized plates. The microstructural study was performed by optical microscopy under polarized light and scanning electron microscopy (SEM). Texture changes were characterized at different lengthscales: in the micrometric size, orientation imaging maps (OIM) were constructed by electron backscatter diffraction (EBSD), in the millimetre scale, high energy XRD experiments were done at the Advanced Photon Source (USA) and compared to neutron diffraction texture determinations performed in the HIPPO instrument at Los Alamos National Laboratory. In the heat affected zone (HAZ) we observed the development of Widmanstätten microstructures, typical of the α(hcp) to β(bcc) phase transformation. Associated with these changes a rotation of the c-poles is found in the HAZ and fusion zone. While the base material shows the typical texture of a cold rolled plate, with their c-poles pointing 35° apart from the normal direction of the plate in the normal-transversal line, in the HAZ, c-poles align along the transversal direction of the plate and then re-orient along different directions, all of these changes occurring within a lengthscale in the order of mm. The evolution of texture in this narrow region was captured by both OIM and XRD, and is consistent with previous measurements done by Neutron Diffraction in the HIPPO diffractometer at Los Alamos National Laboratory, USA. The microstructural and texture changes along the HAZ were interpreted as arising due to the effect of differences in the cooling rate and β grain size on the progress of the different α variants during transformation. Fast cooling rates and large β grains are associated to weak variant selection during the β->α transformation, while slow cooling rates and fine β grains result in strong variant selection.

Crystallographic texture and microstructural changes in fusion welds of recrystallized Zry-4 rolled plates

DOE PAGES

Riffo, A. Moya; Vicente Alvarez, M. A.; Santisteban, J. R.; ...

2017-02-08

This study presents a detailed characterization of the microstructural and crystallographic texture changes observed in the transition region in a weld between two Zircaloy-4 cold rolled and recrystallized plates. The microstructural study was performed by optical microscopy under polarized light and scanning electron microscopy (SEM). Texture changes were characterized at different lengthscales: in the micrometric size, orientation imaging maps (OIM) were constructed by electron backscatter diffraction (EBSD), in the millimetre scale, high energy XRD experiments were done at the Advanced Photon Source (USA) and compared to neutron diffraction texture determinations performed in the HIPPO instrument at Los Alamos National Laboratory.more » In the heat affected zone (HAZ) we observed the development of Widmanstätten microstructures, typical of the α( hcp) to β( bcc) phase transformation. Associated with these changes a rotation of the c-poles is found in the HAZ and fusion zone. While the base material shows the typical texture of a cold rolled plate, with their c-poles pointing 35° apart from the normal direction of the plate in the normal-transversal line, in the HAZ, c-poles align along the transversal direction of the plate and then re-orient along different directions, all of these changes occurring within a lengthscale in the order of mm. The evolution of texture in this narrow region was captured by both OIM and XRD, and is consistent with previous measurements done by Neutron Diffraction in the HIPPO diffractometer at Los Alamos National Laboratory, USA. The microstructural and texture changes along the HAZ were interpreted as arising due to the effect of differences in the cooling rate and β grain size on the progress of the different α variants during transformation. Fast cooling rates and large β grains are associated to weak variant selection during the β–>α transformation, while slow cooling rates and fine β grains result in strong variant selection.« less

Crystallographic texture and microstructural changes in fusion welds of recrystallized Zry-4 rolled plates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Riffo, A. Moya; Vicente Alvarez, M. A.; Santisteban, J. R.

This study presents a detailed characterization of the microstructural and crystallographic texture changes observed in the transition region in a weld between two Zircaloy-4 cold rolled and recrystallized plates. The microstructural study was performed by optical microscopy under polarized light and scanning electron microscopy (SEM). Texture changes were characterized at different lengthscales: in the micrometric size, orientation imaging maps (OIM) were constructed by electron backscatter diffraction (EBSD), in the millimetre scale, high energy XRD experiments were done at the Advanced Photon Source (USA) and compared to neutron diffraction texture determinations performed in the HIPPO instrument at Los Alamos National Laboratory.more » In the heat affected zone (HAZ) we observed the development of Widmanstätten microstructures, typical of the α( hcp) to β( bcc) phase transformation. Associated with these changes a rotation of the c-poles is found in the HAZ and fusion zone. While the base material shows the typical texture of a cold rolled plate, with their c-poles pointing 35° apart from the normal direction of the plate in the normal-transversal line, in the HAZ, c-poles align along the transversal direction of the plate and then re-orient along different directions, all of these changes occurring within a lengthscale in the order of mm. The evolution of texture in this narrow region was captured by both OIM and XRD, and is consistent with previous measurements done by Neutron Diffraction in the HIPPO diffractometer at Los Alamos National Laboratory, USA. The microstructural and texture changes along the HAZ were interpreted as arising due to the effect of differences in the cooling rate and β grain size on the progress of the different α variants during transformation. Fast cooling rates and large β grains are associated to weak variant selection during the β–>α transformation, while slow cooling rates and fine β grains result in strong variant selection.« less

Texture analysis of clinical radiographs using radon transform on a local scale for differentiation between post-menopausal women with and without hip fracture

NASA Astrophysics Data System (ADS)

Boehm, Holger F.; Körner, Markus; Baumert, Bernhard; Linsenmaier, Ulrich; Reiser, Maximilian

2011-03-01

Osteoporosis is a chronic condition characterized by demineralization and destruction of bone tissue. Fractures associated with the disease are becoming an increasingly relevant issue for public health institutions. Prediction of fracture risk is a major focus research and, over the years, has been approched by various methods. Still, bone mineral density (BMD) obtained by dual-energy X-ray absorptiometry (DXA) remains the clinical gold-standard for diagnosis and follow-up of osteoporosis. However, DXA is restricted to specialized diagnostic centers and there exists considerable overlap in BMD results between populations of individuals with and without fractures. Clinically far more available than DXA is conventional x-ray imaging depicting trabecular bone structure in great detail. In this paper, we demonstrate that bone structure depicted by clinical radiographs can be analysed quantitatively by parameters obtained from the Radon Transform (RT). RT is a global analysis-tool for detection of predefined, parameterized patterns, e.g. straight lines or struts, representing suitable approximations of trabecular bone texture. The proposed algorithm differentiates between patients with and without fractures of the hip by application of various texture-metrics based on the Radon-Transform to standard x-ray images of the proximal femur. We consider three different regions-of-interest in the proximal femur (femoral head, neck, and inter-trochanteric area), and conduct an analysis with respect to correct classification of the fracture status. Performance of the novel approach is compared to DXA. We draw the conclusion that performance of RT is comparable to DXA and may become a useful supplement to densitometry for the prediction of fracture risk.

Precision of quantitative computed tomography texture analysis using image filtering: A phantom study for scanner variability.

PubMed

Yasaka, Koichiro; Akai, Hiroyuki; Mackin, Dennis; Court, Laurence; Moros, Eduardo; Ohtomo, Kuni; Kiryu, Shigeru

2017-05-01

Quantitative computed tomography (CT) texture analyses for images with and without filtration are gaining attention to capture the heterogeneity of tumors. The aim of this study was to investigate how quantitative texture parameters using image filtering vary among different computed tomography (CT) scanners using a phantom developed for radiomics studies.A phantom, consisting of 10 different cartridges with various textures, was scanned under 6 different scanning protocols using four CT scanners from four different vendors. CT texture analyses were performed for both unfiltered images and filtered images (using a Laplacian of Gaussian spatial band-pass filter) featuring fine, medium, and coarse textures. Forty-five regions of interest were placed for each cartridge (x) in a specific scan image set (y), and the average of the texture values (T(x,y)) was calculated. The interquartile range (IQR) of T(x,y) among the 6 scans was calculated for a specific cartridge (IQR(x)), while the IQR of T(x,y) among the 10 cartridges was calculated for a specific scan (IQR(y)), and the median IQR(y) was then calculated for the 6 scans (as the control IQR, IQRc). The median of their quotient (IQR(x)/IQRc) among the 10 cartridges was defined as the variability index (VI).The VI was relatively small for the mean in unfiltered images (0.011) and for standard deviation (0.020-0.044) and entropy (0.040-0.044) in filtered images. Skewness and kurtosis in filtered images featuring medium and coarse textures were relatively variable across different CT scanners, with VIs of 0.638-0.692 and 0.430-0.437, respectively.Various quantitative CT texture parameters are robust and variable among different scanners, and the behavior of these parameters should be taken into consideration.

Comparative analysis of classification based algorithms for diabetes diagnosis using iris images.

PubMed

Samant, Piyush; Agarwal, Ravinder

2018-01-01

Photo-diagnosis is always an intriguing area for the researchers, with the advancement of image processing and computer machine vision techniques it have become more reliable and popular in recent years. The objective of this paper is to study the change in the features of iris, particularly irregularities in the pigmentation of certain areas of the iris with respect to diabetic health of an individual. Apart from the point that iris recognition concentrates on the overall structure of the iris, diagnostic techniques emphasises the local variations in the particular area of iris. Pre-image processing techniques have been applied to extract iris and thereafter, region of interest from the extracted iris have been cropped out. In order to observe the changes in the tissue pigmentation of region of interest, statistical, texture textural and wavelet features have been extracted. At the end, a comparison of accuracies of five different classifiers has been presented to classify two subject groups of diabetic and non-diabetic. Best classification accuracy has been calculated as 89.66% by the random forest classifier. Results have been shown the effectiveness and diagnostic significance of the proposed methodology. Presented piece of work offers a novel systemic perspective of non-invasive and automatic diabetic diagnosis.

Features of the normal choriocapillaris with OCT-angiography: Density estimation and textural properties.

PubMed

Montesano, Giovanni; Allegrini, Davide; Colombo, Leonardo; Rossetti, Luca M; Pece, Alfredo

2017-01-01

The main objective of our work is to perform an in depth analysis of the structural features of normal choriocapillaris imaged with OCT Angiography. Specifically, we provide an optimal radius for a circular Region of Interest (ROI) to obtain a stable estimate of the subfoveal choriocapillaris density and characterize its textural properties using Markov Random Fields. On each binarized image of the choriocapillaris OCT Angiography we performed simulated measurements of the subfoveal choriocapillaris densities with circular Regions of Interest (ROIs) of different radii and with small random displacements from the center of the Foveal Avascular Zone (FAZ). We then calculated the variability of the density measure with different ROI radii. We then characterized the textural features of choriocapillaris binary images by estimating the parameters of an Ising model. For each image we calculated the Optimal Radius (OR) as the minimum ROI radius required to obtain a standard deviation in the simulation below 0.01. The density measured with the individual OR was 0.52 ± 0.07 (mean ± STD). Similar density values (0.51 ± 0.07) were obtained using a fixed ROI radius of 450 μm. The Ising model yielded two parameter estimates (β = 0.34 ± 0.03; γ = 0.003 ± 0.012; mean ± STD), characterizing pixel clustering and white pixel density respectively. Using the estimated parameters to synthetize new random textures via simulation we obtained a good reproduction of the original choriocapillaris structural features and density. In conclusion, we developed an extensive characterization of the normal subfoveal choriocapillaris that might be used for flow analysis and applied to the investigation pathological alterations.

Computer-aided assessment of pulmonary disease in novel swine-origin H1N1 influenza on CT

NASA Astrophysics Data System (ADS)

Yao, Jianhua; Dwyer, Andrew J.; Summers, Ronald M.; Mollura, Daniel J.

2011-03-01

The 2009 pandemic is a global outbreak of novel H1N1 influenza. Radiologic images can be used to assess the presence and severity of pulmonary infection. We develop a computer-aided assessment system to analyze the CT images from Swine-Origin Influenza A virus (S-OIV) novel H1N1 cases. The technique is based on the analysis of lung texture patterns and classification using a support vector machine (SVM). Pixel-wise tissue classification is computed from the SVM value. The method was validated on four H1N1 cases and ten normal cases. We demonstrated that the technique can detect regions of pulmonary abnormality in novel H1N1 patients and differentiate these regions from visually normal lung (area under the ROC curve is 0.993). This technique can also be applied to differentiate regions infected by different pulmonary diseases.

a Region-Based Multi-Scale Approach for Object-Based Image Analysis

NASA Astrophysics Data System (ADS)

Kavzoglu, T.; Yildiz Erdemir, M.; Tonbul, H.

2016-06-01

Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

The effect of texture and grain size on improving the mechanical properties of Mg-Al-Zn alloys by friction stir processing.

PubMed

Peng, Jinhua; Zhang, Zhen; Liu, Zhao; Li, Yaozu; Guo, Peng; Zhou, Wei; Wu, Yucheng

2018-03-08

Friction stir processing (FSP) was used to achieve grain refinement on Mg-Al-Zn alloys, which also brought in significant texture modification. The different micro-texture characteristics were found to cause irregular micro-hardness distribution in FSPed region. The effects of texture and grain size were investigated by comparative analyses with strongly textured rolling sheet. Grain refinement improved both strength and elongation in condition of a basal texture while such led to an increment in yield stress and a drop in elongation and ultimate stress when the basal texture was modified by FSP.

Human (Homo sapiens) facial attractiveness in relation to skin texture and color.

PubMed

Fink, B; Grammer, K; Thornhill, R

2001-03-01

The notion that surface texture may provide important information about the geometry of visible surfaces has attracted considerable attention for a long time. The present study shows that skin texture plays a significant role in the judgment of female facial beauty. Following research in clinical dermatology, the authors developed a computer program that implemented an algorithm based on co-occurrence matrices for the analysis of facial skin texture. Homogeneity and contrast features as well as color parameters were extracted out of stimulus faces. Attractiveness ratings of the images made by male participants relate positively to parameters of skin homogeneity. The authors propose that skin texture is a cue to fertility and health. In contrast to some previous studies, the authors found that dark skin, not light skin, was rated as most attractive.

Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

NASA Astrophysics Data System (ADS)

Korfiatis, P.; Kalogeropoulou, C.; Daoussis, D.; Petsas, T.; Adonopoulos, A.; Costaridou, L.

2009-07-01

Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, B; Fujita, A; Buch, K

Purpose: To investigate the correlation between texture analysis-based model observer and human observer in the task of diagnosis of ischemic infarct in non-contrast head CT of adults. Methods: Non-contrast head CTs of five patients (2 M, 3 F; 58–83 y) with ischemic infarcts were retro-reconstructed using FBP and Adaptive Statistical Iterative Reconstruction (ASIR) of various levels (10–100%). Six neuro -radiologists reviewed each image and scored image quality for diagnosing acute infarcts by a 9-point Likert scale in a blinded test. These scores were averaged across the observers to produce the average human observer responses. The chief neuro-radiologist placed multiple ROIsmore » over the infarcts. These ROIs were entered into a texture analysis software package. Forty-two features per image, including 11 GLRL, 5 GLCM, 4 GLGM, 9 Laws, and 13 2-D features, were computed and averaged over the images per dataset. The Fisher-coefficient (ratio of between-class variance to in-class variance) was calculated for each feature to identify the most discriminating features from each matrix that separate the different confidence scores most efficiently. The 15 features with the highest Fisher -coefficient were entered into linear multivariate regression for iterative modeling. Results: Multivariate regression analysis resulted in the best prediction model of the confidence scores after three iterations (df=11, F=11.7, p-value<0.0001). The model predicted scores and human observers were highly correlated (R=0.88, R-sq=0.77). The root-mean-square and maximal residual were 0.21 and 0.44, respectively. The residual scatter plot appeared random, symmetric, and unbiased. Conclusion: For diagnosis of ischemic infarct in non-contrast head CT in adults, the predicted image quality scores from texture analysis-based model observer was highly correlated with that of human observers for various noise levels. Texture-based model observer can characterize image quality of low contrast, subtle texture changes in addition to human observers.« less

Texture Analysis and Machine Learning for Detecting Myocardial Infarction in Noncontrast Low-Dose Computed Tomography: Unveiling the Invisible.

PubMed

Mannil, Manoj; von Spiczak, Jochen; Manka, Robert; Alkadhi, Hatem

2018-06-01

The aim of this study was to test whether texture analysis and machine learning enable the detection of myocardial infarction (MI) on non-contrast-enhanced low radiation dose cardiac computed tomography (CCT) images. In this institutional review board-approved retrospective study, we included non-contrast-enhanced electrocardiography-gated low radiation dose CCT image data (effective dose, 0.5 mSv) acquired for the purpose of calcium scoring of 27 patients with acute MI (9 female patients; mean age, 60 ± 12 years), 30 patients with chronic MI (8 female patients; mean age, 68 ± 13 years), and in 30 subjects (9 female patients; mean age, 44 ± 6 years) without cardiac abnormality, hereafter termed controls. Texture analysis of the left ventricle was performed using free-hand regions of interest, and texture features were classified twice (Model I: controls versus acute MI versus chronic MI; Model II: controls versus acute and chronic MI). For both classifications, 6 commonly used machine learning classifiers were used: decision tree C4.5 (J48), k-nearest neighbors, locally weighted learning, RandomForest, sequential minimal optimization, and an artificial neural network employing deep learning. In addition, 2 blinded, independent readers visually assessed noncontrast CCT images for the presence or absence of MI. In Model I, best classification results were obtained using the k-nearest neighbors classifier (sensitivity, 69%; specificity, 85%; false-positive rate, 0.15). In Model II, the best classification results were found with the locally weighted learning classification (sensitivity, 86%; specificity, 81%; false-positive rate, 0.19) with an area under the curve from receiver operating characteristics analysis of 0.78. In comparison, both readers were not able to identify MI in any of the noncontrast, low radiation dose CCT images. This study indicates the ability of texture analysis and machine learning in detecting MI on noncontrast low radiation dose CCT images being not visible for the radiologists' eye.

Texture classification of vegetation cover in high altitude wetlands zone

NASA Astrophysics Data System (ADS)

Wentao, Zou; Bingfang, Wu; Hongbo, Ju; Hua, Liu

2014-03-01

The aim of this study was to investigate the utility of datasets composed of texture measures and other features for the classification of vegetation cover, specifically wetlands. QUEST decision tree classifier was applied to a SPOT-5 image sub-scene covering the typical wetlands area in Three River Sources region in Qinghai province, China. The dataset used for the classification comprised of: (1) spectral data and the components of principal component analysis; (2) texture measures derived from pixel basis; (3) DEM and other ancillary data covering the research area. Image textures is an important characteristic of remote sensing images; it can represent spatial variations with spectral brightness in digital numbers. When the spectral information is not enough to separate the different land covers, the texture information can be used to increase the classification accuracy. The texture measures used in this study were calculated from GLCM (Gray level Co-occurrence Matrix); eight frequently used measures were chosen to conduct the classification procedure. The results showed that variance, mean and entropy calculated by GLCM with a 9*9 size window were effective in distinguishing different vegetation types in wetlands zone. The overall accuracy of this method was 84.19% and the Kappa coefficient was 0.8261. The result indicated that the introduction of texture measures can improve the overall accuracy by 12.05% and the overall kappa coefficient by 0.1407 compared with the result using spectral and ancillary data.

Uterus segmentation in dynamic MRI using LBP texture descriptors

NASA Astrophysics Data System (ADS)

Namias, R.; Bellemare, M.-E.; Rahim, M.; Pirró, N.

2014-03-01

Pelvic floor disorders cover pathologies of which physiopathology is not well understood. However cases get prevalent with an ageing population. Within the context of a project aiming at modelization of the dynamics of pelvic organs, we have developed an efficient segmentation process. It aims at alleviating the radiologist with a tedious one by one image analysis. From a first contour delineating the uterus-vagina set, the organ border is tracked along a dynamic mri sequence. The process combines movement prediction, local intensity and texture analysis and active contour geometry control. Movement prediction allows a contour intitialization for next image in the sequence. Intensity analysis provides image-based local contour detection enhanced by local binary pattern (lbp) texture descriptors. Geometry control prohibits self intersections and smoothes the contour. Results show the efficiency of the method with images produced in clinical routine.

Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fried, David V.; Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas; Tucker, Susan L.

2014-11-15

Purpose: To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non-small cell lung cancer (NSCLC). Methods and Materials: We retrospectively reviewed 91 cases with stage III NSCLC treated with definitive chemoradiation therapy. All patients underwent pretreatment diagnostic contrast enhanced computed tomography (CE-CT) followed by 4-dimensional CT (4D-CT) for treatment simulation. We used the average-CT and expiratory (T50-CT) images from the 4D-CT along with the CE-CT for texture extraction. Histogram, gradient, co-occurrence, gray tone difference, and filtration-based techniques were used for texture feature extraction. Penalized Cox regression implementing cross-validation wasmore » used for covariate selection and modeling. Models incorporating texture features from the 33 image types and CPFs were compared to those with models incorporating CPFs alone for overall survival (OS), local-regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan-Meier curves were generated using leave-one-out cross-validation. Patients were stratified based on whether their predicted outcome was above or below the median. Reproducibility of texture features was evaluated using test-retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test-retest scans to our original models and determined the classification reproducibility. Results: Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone for OS (P=.046), LRC (P=.01), and FFDM (P=.005). The average CCCs were 0.89, 0.91, and 0.67 for texture features extracted from the average-CT, T50-CT, and CE-CT, respectively. Incorporating reproducibility within our models yielded 80.4% (±3.7% SD), 78.3% (±4.0% SD), and 78.8% (±3.9% SD) classification reproducibility in terms of OS, LRC, and FFDM, respectively. Conclusions: Pretreatment tumor texture may provide prognostic information beyond that obtained from CPFs. Models incorporating feature reproducibility achieved classification rates of ∼80%. External validation would be required to establish texture as a prognostic factor.« less

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

PubMed Central

Caetano dos Santos, Florentino Luciano; Skottman, Heli; Juuti-Uusitalo, Kati; Hyttinen, Jari

2016-01-01

Aims A fast, non-invasive and observer-independent method to analyze the homogeneity and maturity of human pluripotent stem cell (hPSC) derived retinal pigment epithelial (RPE) cells is warranted to assess the suitability of hPSC-RPE cells for implantation or in vitro use. The aim of this work was to develop and validate methods to create ensembles of state-of-the-art texture descriptors and to provide a robust classification tool to separate three different maturation stages of RPE cells by using phase contrast microscopy images. The same methods were also validated on a wide variety of biological image classification problems, such as histological or virus image classification. Methods For image classification we used different texture descriptors, descriptor ensembles and preprocessing techniques. Also, three new methods were tested. The first approach was an ensemble of preprocessing methods, to create an additional set of images. The second was the region-based approach, where saliency detection and wavelet decomposition divide each image in two different regions, from which features were extracted through different descriptors. The third method was an ensemble of Binarized Statistical Image Features, based on different sizes and thresholds. A Support Vector Machine (SVM) was trained for each descriptor histogram and the set of SVMs combined by sum rule. The accuracy of the computer vision tool was verified in classifying the hPSC-RPE cell maturation level. Dataset and Results The RPE dataset contains 1862 subwindows from 195 phase contrast images. The final descriptor ensemble outperformed the most recent stand-alone texture descriptors, obtaining, for the RPE dataset, an area under ROC curve (AUC) of 86.49% with the 10-fold cross validation and 91.98% with the leave-one-image-out protocol. The generality of the three proposed approaches was ascertained with 10 more biological image datasets, obtaining an average AUC greater than 97%. Conclusions Here we showed that the developed ensembles of texture descriptors are able to classify the RPE cell maturation stage. Moreover, we proved that preprocessing and region-based decomposition improves many descriptors’ accuracy in biological dataset classification. Finally, we built the first public dataset of stem cell-derived RPE cells, which is publicly available to the scientific community for classification studies. The proposed tool is available at https://www.dei.unipd.it/node/2357 and the RPE dataset at http://www.biomeditech.fi/data/RPE_dataset/. Both are available at https://figshare.com/s/d6fb591f1beb4f8efa6f. PMID:26895509

Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters.

PubMed

Wang, Jing-Jing; Wu, Hai-Feng; Sun, Tao; Li, Xia; Wang, Wei; Tao, Li-Xin; Huo, Da; Lv, Ping-Xin; He, Wen; Guo, Xiu-Hua

2013-01-01

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

The Effects of Bolus Volume and Texture on Pharyngeal Pressure Events Using High-resolution Manometry and Its Comparison with Videofluoroscopic Swallowing Study

PubMed Central

Ryu, Ju Seok; Park, Donghwi; Oh, Yoongul; Lee, Seok Tae; Kang, Jin Young

2016-01-01

Background/Aims The purpose of this study was to develop new parameters of high-resolution manometry (HRM) and to applicate these to quantify the effect of bolus volume and texture on pharyngeal swallowing. Methods Ten healthy subjects prospectively swallowed dry, thin fluid 2 mL, thin fluid 5 mL, thin fluid 10 mL, and drinking twice to compare effects of bolus volume. To compare effect of texture, subjects swallowed thin fluid 5 mL, yogurt 5 mL, and bread twice. A 32-sensor HRM catheter and BioVIEW ANALYSIS software were used for data collection and analysis. HRM data were synchronized with kinematic analysis of videofluoroscopic swallowing study (VFSS) using epiglottis tilting. Results Linear correlation analysis for volume showed significant correlation for area of velopharynx, duration of velopharynx, pre-upper esophageal sphincter (UES) maximal pressure, minimal UES pressure, UES activity time, and nadir UES duration. In the correlation with texture, all parameters were not significantly different. The contraction of the velopharynx was faster than laryngeal elevation. The durations of UES relaxation was shorter in the kinematic analysis than HRM. Conclusions The bolus volume was shown to have significant effect on pharyngeal pressure and timing, but the texture did not show any effect on pharyngeal swallowing. The parameters of HRM were more sensitive than those of kinematic analysis. As the parameters of HRM are based on precise anatomic structure and the kinematic analysis reflects the actions of multiple anatomic structures, HRM and VFSS should be used according to their purposes. PMID:26598598

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

NASA Astrophysics Data System (ADS)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

A texture analysis method for MR images of airway dilator muscles: a feasibility study

PubMed Central

Järnstedt, J; Sikiö, M; Viik, J; Dastidar, P; Peltomäki, T; Eskola, H

2014-01-01

Objectives: Airway dilator muscles play an important role in the analysis of breathing-related symptoms, such as obstructive sleep apnoea. Texture analysis (TA) provides a new non-invasive method for analysing airway dilator muscles. In this study, we propose a TA methodology for airway dilator muscles and prove the robustness of this method. Methods: 15 orthognathic surgery patients underwent 3-T MRI. Computerized TA was performed on 20 regions of interest (ROIs) in the patients' airway dilator muscles. 53 texture parameters were calculated for all ROIs. The robustness of the TA method was analysed by altering the locations, sizes and shapes of the ROIs. Results: Our study shows that there is significant difference in TA results as the size or shape of ROI changes. The change of location of the ROI inside the studied muscle does not affect the TA results. Conclusions: The TA method is valid for airway dilator muscles. We propose a methodology in which the number of co-occurrence parameters is reduced by using mean values from four different directions (0°, 45°, 90° and 135°) with pixel spacing of 1 pixel. PMID:24773626

Characterization of Breast Implant Surfaces, Shapes, and Biomechanics: A Comparison of High Cohesive Anatomically Shaped Textured Silicone, Breast Implants from Three Different Manufacturers.

PubMed

Atlan, Michael; Bigerelle, Maxence; Larreta-garde, Véronique; Hindié, Mathilde; Hedén, Per

2016-02-01

Several companies offer anatomically shaped breast implants but differences among manufacturers are often misunderstood. The shell texture is a crucial parameter for anatomically shaped implants to prevent rotation and to decrease the risk of capsular contracture, even though concerns have recently been raised concerning the complications associated with textured breast implants. The aim of this study was to characterize differences in terms of texture, cell adhesion, shape, and stiffness between some commonly used anatomically shaped implants from three different manufacturers. Five commercially available anatomically shaped breast implants from 3 different manufacturers (Allergan, Mentor, and Sebbin) were used. Scanning electron microscopy, X-ray microtomography, and scanning mechanical microscopy were used to characterize the shell texture. Human fibroblast adhesion onto the shells was evaluated. 3D models of the implants were obtained using CT-scan acquisitions to analyze their shape. Implant stiffness was evaluated using a tractiometer. Major differences were observed in the topography of the textures of the shells, but this was not conveyed by a statistically significant fibroblast adhesion difference. However, fibroblasts adhered better on anatomically shaped textured implants than on smooth implants (p < 0.01). Our work pointed out differences in the Biocell® texture in comparison with older studies. The 3D analysis showed significant shape differences between the anatomically shaped implants of the 3 companies, despite similar dimensions. Implant stiffness was comparable among the 3 brands. Each texture had its specific topography, and this work is the first description of Sebbin anatomic breast implant texturation. Moreover, major discrepancies were found in the analysis of the Biocell® texture when comparing our results with previous reports. These differences may have clinical implications and are discussed. This study also highlighted major shape differences among breast implants from different manufacturers, which is quite counterintuitive. The clinical impact of these differences however needs further investigation. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Texture analysis of intermediate-advanced hepatocellular carcinoma: prognosis and patients' selection of transcatheter arterial chemoembolization and sorafenib

PubMed Central

Fu, Sirui; Chen, Shuting; Liang, Changhong; Liu, Zaiyi; Zhu, Yanjie; Li, Yong; Lu, Ligong

2017-01-01

Transcatheter arterial chemoembolization (TACE) and sorafenib combination treatment for unselected hepatocellular carcinoma (HCC) is controversial. We explored the potential of texture analysis for appropriate patient selection. There were 261 HCCs included (TACE group: n = 197; TACE plus sorafenib (TACE+Sorafenib) group n = 64). We applied a Gabor filter and wavelet transform with 3 band-width responses (filter 0, 1.0, and 1.5) to portal-phase computed tomography (CT) images of the TACE group. Twenty-one textural parameters per filter were extracted from the region of interests delineated around tumor outline. After testing survival correlations, the TACE group was subdivided according to parameter thresholds in receiver operating characteristic curves and compared to TACE+Sorafenib group survival. The Gabor-1-90 (filter 0) was most significantly correlated with TTP. The TACE group was accordingly divided into the TACE-1 (Gabor-1-90 ≤ 3.6190) and TACE-2 (Gabor-1-90 > 3.6190) subgroups; TTP was similar in the TACE-1 subgroup and TACE+Sorafenib group, but shorter in the TACE-2 subgroup. Only wavelet-3-D (filter 1.0) correlated with overall survival (OS), and was used for subgrouping. The TACE-5 (wavelet-3-D ≤ 12.2620) subgroup and the TACE+Sorafenib group showed similar OS, while the TACE-6 (wavelet-3-D > 12.2620) subgroup had shorter OS. Gabor-1-90 and wavelet-3-D were consistent. In dependent of tumor number or size, CT textural parameters are correlated with TTP and OS. Patients with lower Gabor-1-90 (filter 0) and wavelet-3-D (filter 1.0) should be treated with TACE and sorafenib. Texture analysis holds promise for appropriate selection of HCCs for this combination therapy. PMID:27911268

Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Solomon, Justin, E-mail: justin.solomon@duke.edu; Samei, Ehsan

2014-09-15

Purpose: Quantum noise properties of CT images are generally assessed using simple geometric phantoms with uniform backgrounds. Such phantoms may be inadequate when assessing nonlinear reconstruction or postprocessing algorithms. The purpose of this study was to design anatomically informed textured phantoms and use the phantoms to assess quantum noise properties across two clinically available reconstruction algorithms, filtered back projection (FBP) and sinogram affirmed iterative reconstruction (SAFIRE). Methods: Two phantoms were designed to represent lung and soft-tissue textures. The lung phantom included intricate vessel-like structures along with embedded nodules (spherical, lobulated, and spiculated). The soft tissue phantom was designed based onmore » a three-dimensional clustered lumpy background with included low-contrast lesions (spherical and anthropomorphic). The phantoms were built using rapid prototyping (3D printing) technology and, along with a uniform phantom of similar size, were imaged on a Siemens SOMATOM Definition Flash CT scanner and reconstructed with FBP and SAFIRE. Fifty repeated acquisitions were acquired for each background type and noise was assessed by estimating pixel-value statistics, such as standard deviation (i.e., noise magnitude), autocorrelation, and noise power spectrum. Noise stationarity was also assessed by examining the spatial distribution of noise magnitude. The noise properties were compared across background types and between the two reconstruction algorithms. Results: In FBP and SAFIRE images, noise was globally nonstationary for all phantoms. In FBP images of all phantoms, and in SAFIRE images of the uniform phantom, noise appeared to be locally stationary (within a reasonably small region of interest). Noise was locally nonstationary in SAFIRE images of the textured phantoms with edge pixels showing higher noise magnitude compared to pixels in more homogenous regions. For pixels in uniform regions, noise magnitude was reduced by an average of 60% in SAFIRE images compared to FBP. However, for edge pixels, noise magnitude ranged from 20% higher to 40% lower in SAFIRE images compared to FBP. SAFIRE images of the lung phantom exhibited distinct regions with varying noise texture (i.e., noise autocorrelation/power spectra). Conclusions: Quantum noise properties observed in uniform phantoms may not be representative of those in actual patients for nonlinear reconstruction algorithms. Anatomical texture should be considered when evaluating the performance of CT systems that use such nonlinear algorithms.« less

HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling.

PubMed

Ross, C Wade; Prihodko, Lara; Anchang, Julius; Kumar, Sanath; Ji, Wenjie; Hanan, Niall P

2018-05-15

Hydrologic soil groups (HSGs) are a fundamental component of the USDA curve-number (CN) method for estimation of rainfall runoff; yet these data are not readily available in a format or spatial-resolution suitable for regional- and global-scale modeling applications. We developed a globally consistent, gridded dataset defining HSGs from soil texture, bedrock depth, and groundwater. The resulting data product-HYSOGs250m-represents runoff potential at 250 m spatial resolution. Our analysis indicates that the global distribution of soil is dominated by moderately high runoff potential, followed by moderately low, high, and low runoff potential. Low runoff potential, sandy soils are found primarily in parts of the Sahara and Arabian Deserts. High runoff potential soils occur predominantly within tropical and sub-tropical regions. No clear pattern could be discerned for moderately low runoff potential soils, as they occur in arid and humid environments and at both high and low elevations. Potential applications of this data include CN-based runoff modeling, flood risk assessment, and as a covariate for biogeographical analysis of vegetation distributions.

Textural content in 3T MR: an image-based marker for Alzheimer's disease

NASA Astrophysics Data System (ADS)

Bharath Kumar, S. V.; Mullick, Rakesh; Patil, Uday

2005-04-01

In this paper, we propose a study, which investigates the first-order and second-order distributions of T2 images from a magnetic resonance (MR) scan for an age-matched data set of 24 Alzheimer's disease and 17 normal patients. The study is motivated by the desire to analyze the brain iron uptake in the hippocampus of Alzheimer's patients, which is captured by low T2 values. Since, excess iron deposition occurs locally in certain regions of the brain, we are motivated to investigate the spatial distribution of T2, which is captured by higher-order statistics. Based on the first-order and second-order distributions (involving gray level co-occurrence matrix) of T2, we show that the second-order statistics provide features with sensitivity >90% (at 80% specificity), which in turn capture the textural content in T2 data. Hence, we argue that different texture characteristics of T2 in the hippocampus for Alzheimer's and normal patients could be used as an early indicator of Alzheimer's disease.

Pollen Image Recognition Based on DGDB-LBP Descriptor

NASA Astrophysics Data System (ADS)

Han, L. P.; Xie, Y. H.

2018-01-01

In this paper, we propose DGDB-LBP, a local binary pattern descriptor based on the pixel blocks in the dominant gradient direction. Differing from traditional LBP and its variants, DGDB-LBP encodes by comparing the main gradient magnitude of each block rather than the single pixel value or the average of pixel blocks, in doing so, it reduces the influence of noise on pollen images and eliminates redundant and non-informative features. In order to fully describe the texture features of pollen images and analyze it under multi-scales, we propose a new sampling strategy, which uses three types of operators to extract the radial, angular and multiple texture features under different scales. Considering that the pollen images have some degree of rotation under the microscope, we propose the adaptive encoding direction, which is determined by the texture distribution of local region. Experimental results on the Pollenmonitor dataset show that the average correct recognition rate of our method is superior to other pollen recognition methods in recent years.

Tissue classification for laparoscopic image understanding based on multispectral texture analysis

NASA Astrophysics Data System (ADS)

Zhang, Yan; Wirkert, Sebastian J.; Iszatt, Justin; Kenngott, Hannes; Wagner, Martin; Mayer, Benjamin; Stock, Christian; Clancy, Neil T.; Elson, Daniel S.; Maier-Hein, Lena

2016-03-01

Intra-operative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study we show (1) that multispectral imaging data is superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) that combining the tissue texture with the reflectance spectrum improves the classification performance. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

Potential impacts of robust surface roughness indexes on DTM-based segmentation

NASA Astrophysics Data System (ADS)

Trevisani, Sebastiano; Rocca, Michele

2017-04-01

In this study, we explore the impact of robust surface texture indexes based on MAD (median absolute differences), implemented by Trevisani and Rocca (2015), in the unsupervised morphological segmentation of an alpine basin. The area was already object of a geomorphometric analysis, consisting in the roughness-based segmentation of the landscape (Trevisani et al. 2012); the roughness indexes were calculated on a high resolution DTM derived by means of airborne Lidar using the variogram as estimator. The calculated roughness indexes have been then used for the fuzzy clustering (Odeh et al., 1992; Burrough et al., 2000) of the basin, revealing the high informative geomorphometric content of the roughness-based indexes. However, the fuzzy clustering revealed a high fuzziness and a high degree of mixing between textural classes; this was ascribed both to the morphological complexity of the basin and to the high sensitivity of variogram to non-stationarity and signal-noise. Accordingly, we explore how the new implemented roughness indexes based on MAD affect the morphological segmentation of the studied basin. References Burrough, P.A., Van Gaans, P.F.M., MacMillan, R.A., 2000. High-resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems 113, 37-52. Odeh, I.O.A., McBratney, A.B., Chittleborough, D.J., 1992. Soil pattern recognition with fuzzy-c-means: application to classification and soil-landform interrelationships. Soil Sciences Society of America Journal 56, 505-516. Trevisani, S., Cavalli, M. & Marchi, L. 2012, "Surface texture analysis of a high-resolution DTM: Interpreting an alpine basin", Geomorphology, vol. 161-162, pp. 26-39. Trevisani, S. & Rocca, M. 2015, "MAD: Robust image texture analysis for applications in high resolution geomorphometry", Computers and Geosciences, vol. 81, pp. 78-92.

Histogram-based adaptive gray level scaling for texture feature classification of colorectal polyps

NASA Astrophysics Data System (ADS)

Pomeroy, Marc; Lu, Hongbing; Pickhardt, Perry J.; Liang, Zhengrong

2018-02-01

Texture features have played an ever increasing role in computer aided detection (CADe) and diagnosis (CADx) methods since their inception. Texture features are often used as a method of false positive reduction for CADe packages, especially for detecting colorectal polyps and distinguishing them from falsely tagged residual stool and healthy colon wall folds. While texture features have shown great success there, the performance of texture features for CADx have lagged behind primarily because of the more similar features among different polyps types. In this paper, we present an adaptive gray level scaling and compare it to the conventional equal-spacing of gray level bins. We use a dataset taken from computed tomography colonography patients, with 392 polyp regions of interest (ROIs) identified and have a confirmed diagnosis through pathology. Using the histogram information from the entire ROI dataset, we generate the gray level bins such that each bin contains roughly the same number of voxels Each image ROI is the scaled down to two different numbers of gray levels, using both an equal spacing of Hounsfield units for each bin, and our adaptive method. We compute a set of texture features from the scaled images including 30 gray level co-occurrence matrix (GLCM) features and 11 gray level run length matrix (GLRLM) features. Using a random forest classifier to distinguish between hyperplastic polyps and all others (adenomas and adenocarcinomas), we find that the adaptive gray level scaling can improve performance based on the area under the receiver operating characteristic curve by up to 4.6%.

Fast determination of the current loss mechanisms in textured crystalline Si-based solar cells

NASA Astrophysics Data System (ADS)

Nakane, Akihiro; Fujimoto, Shohei; Fujiwara, Hiroyuki

2017-11-01

A quite general device analysis method that allows the direct evaluation of optical and recombination losses in crystalline silicon (c-Si)-based solar cells has been developed. By applying this technique, the current loss mechanisms of the state-of-the-art solar cells with ˜20% efficiencies have been revealed. In the established method, the optical and electrical losses are characterized from the analysis of an experimental external quantum efficiency (EQE) spectrum with very low computational cost. In particular, we have performed the EQE analyses of textured c-Si solar cells by employing the experimental reflectance spectra obtained directly from the actual devices while using flat optical models without any fitting parameters. We find that the developed method provides almost perfect fitting to EQE spectra reported for various textured c-Si solar cells, including c-Si heterojunction solar cells, a dopant-free c-Si solar cell with a MoOx layer, and an n-type passivated emitter with rear locally diffused solar cell. The modeling of the recombination loss further allows the extraction of the minority carrier diffusion length and surface recombination velocity from the EQE analysis. Based on the EQE analysis results, the current loss mechanisms in different types of c-Si solar cells are discussed.

Inlining 3d Reconstruction, Multi-Source Texture Mapping and Semantic Analysis Using Oblique Aerial Imagery

NASA Astrophysics Data System (ADS)

Frommholz, D.; Linkiewicz, M.; Poznanska, A. M.

2016-06-01

This paper proposes an in-line method for the simplified reconstruction of city buildings from nadir and oblique aerial images that at the same time are being used for multi-source texture mapping with minimal resampling. Further, the resulting unrectified texture atlases are analyzed for façade elements like windows to be reintegrated into the original 3D models. Tests on real-world data of Heligoland/ Germany comprising more than 800 buildings exposed a median positional deviation of 0.31 m at the façades compared to the cadastral map, a correctness of 67% for the detected windows and good visual quality when being rendered with GPU-based perspective correction. As part of the process building reconstruction takes the oriented input images and transforms them into dense point clouds by semi-global matching (SGM). The point sets undergo local RANSAC-based regression and topology analysis to detect adjacent planar surfaces and determine their semantics. Based on this information the roof, wall and ground surfaces found get intersected and limited in their extension to form a closed 3D building hull. For texture mapping the hull polygons are projected into each possible input bitmap to find suitable color sources regarding the coverage and resolution. Occlusions are detected by ray-casting a full-scale digital surface model (DSM) of the scene and stored in pixel-precise visibility maps. These maps are used to derive overlap statistics and radiometric adjustment coefficients to be applied when the visible image parts for each building polygon are being copied into a compact texture atlas without resampling whenever possible. The atlas bitmap is passed to a commercial object-based image analysis (OBIA) tool running a custom rule set to identify windows on the contained façade patches. Following multi-resolution segmentation and classification based on brightness and contrast differences potential window objects are evaluated against geometric constraints and conditionally grown, fused and filtered morphologically. The output polygons are vectorized and reintegrated into the previously reconstructed buildings by sparsely ray-tracing their vertices. Finally the enhanced 3D models get stored as textured geometry for visualization and semantically annotated "LOD-2.5" CityGML objects for GIS applications.

Spatial Anisotropies and Temporal Fluctuations in Extracellular Matrix Network Texture during Early Embryogenesis

PubMed Central

Loganathan, Rajprasad; Potetz, Brian R.; Rongish, Brenda J.; Little, Charles D.

2012-01-01

Early stages of vertebrate embryogenesis are characterized by a remarkable series of shape changes. The resulting morphological complexity is driven by molecular, cellular, and tissue-scale biophysical alterations. Operating at the cellular level, extracellular matrix (ECM) networks facilitate cell motility. At the tissue level, ECM networks provide material properties required to accommodate the large-scale deformations and forces that shape amniote embryos. In other words, the primordial biomaterial from which reptilian, avian, and mammalian embryos are molded is a dynamic composite comprised of cells and ECM. Despite its central importance during early morphogenesis we know little about the intrinsic micrometer-scale surface properties of primordial ECM networks. Here we computed, using avian embryos, five textural properties of fluorescently tagged ECM networks — (a) inertia, (b) correlation, (c) uniformity, (d) homogeneity, and (e) entropy. We analyzed fibronectin and fibrillin-2 as examples of fibrous ECM constituents. Our quantitative data demonstrated differences in the surface texture between the fibronectin and fibrillin-2 network in Day 1 (gastrulating) embryos, with the fibronectin network being relatively coarse compared to the fibrillin-2 network. Stage-specific regional anisotropy in fibronectin texture was also discovered. Relatively smooth fibronectin texture was exhibited in medial regions adjoining the primitive streak (PS) compared with the fibronectin network investing the lateral plate mesoderm (LPM), at embryonic stage 5. However, the texture differences had changed by embryonic stage 6, with the LPM fibronectin network exhibiting a relatively smooth texture compared with the medial PS-oriented network. Our data identify, and partially characterize, stage-specific regional anisotropy of fibronectin texture within tissues of a warm-blooded embryo. The data suggest that changes in ECM textural properties reflect orderly time-dependent rearrangements of a primordial biomaterial. We conclude that the ECM microenvironment changes markedly in time and space during the most important period of amniote morphogenesis—as determined by fluctuating textural properties. PMID:22693609

Automatic Texture Mapping of Architectural and Archaeological 3d Models

NASA Astrophysics Data System (ADS)

Kersten, T. P.; Stallmann, D.

2012-07-01

Today, detailed, complete and exact 3D models with photo-realistic textures are increasingly demanded for numerous applications in architecture and archaeology. Manual texture mapping of 3D models by digital photographs with software packages, such as Maxon Cinema 4D, Autodesk 3Ds Max or Maya, still requires a complex and time-consuming workflow. So, procedures for automatic texture mapping of 3D models are in demand. In this paper two automatic procedures are presented. The first procedure generates 3D surface models with textures by web services, while the second procedure textures already existing 3D models with the software tmapper. The program tmapper is based on the Multi Layer 3D image (ML3DImage) algorithm and developed in the programming language C++. The studies showing that the visibility analysis using the ML3DImage algorithm is not sufficient to obtain acceptable results of automatic texture mapping. To overcome the visibility problem the Point Cloud Painter algorithm in combination with the Z-buffer-procedure will be applied in the future.

Cell nuclei attributed relational graphs for efficient representation and classification of gastric cancer in digital histopathology

NASA Astrophysics Data System (ADS)

Sharma, Harshita; Zerbe, Norman; Heim, Daniel; Wienert, Stephan; Lohmann, Sebastian; Hellwich, Olaf; Hufnagl, Peter

2016-03-01

This paper describes a novel graph-based method for efficient representation and subsequent classification in histological whole slide images of gastric cancer. Her2/neu immunohistochemically stained and haematoxylin and eosin stained histological sections of gastric carcinoma are digitized. Immunohistochemical staining is used in practice by pathologists to determine extent of malignancy, however, it is laborious to visually discriminate the corresponding malignancy levels in the more commonly used haematoxylin and eosin stain, and this study attempts to solve this problem using a computer-based method. Cell nuclei are first isolated at high magnification using an automatic cell nuclei segmentation strategy, followed by construction of cell nuclei attributed relational graphs of the tissue regions. These graphs represent tissue architecture comprehensively, as they contain information about cell nuclei morphology as vertex attributes, along with knowledge of neighborhood in the form of edge linking and edge attributes. Global graph characteristics are derived and ensemble learning is used to discriminate between three types of malignancy levels, namely, non-tumor, Her2/neu positive tumor and Her2/neu negative tumor. Performance is compared with state of the art methods including four texture feature groups (Haralick, Gabor, Local Binary Patterns and Varma Zisserman features), color and intensity features, and Voronoi diagram and Delaunay triangulation. Texture, color and intensity information is also combined with graph-based knowledge, followed by correlation analysis. Quantitative assessment is performed using two cross validation strategies. On investigating the experimental results, it can be concluded that the proposed method provides a promising way for computer-based analysis of histopathological images of gastric cancer.

Early prediction of lung cancer recurrence after stereotactic radiotherapy using second order texture statistics

NASA Astrophysics Data System (ADS)

Mattonen, Sarah A.; Palma, David A.; Haasbeek, Cornelis J. A.; Senan, Suresh; Ward, Aaron D.

2014-03-01

Benign radiation-induced lung injury is a common finding following stereotactic ablative radiotherapy (SABR) for lung cancer, and is often difficult to differentiate from a recurring tumour due to the ablative doses and highly conformal treatment with SABR. Current approaches to treatment response assessment have shown limited ability to predict recurrence within 6 months of treatment. The purpose of our study was to evaluate the accuracy of second order texture statistics for prediction of eventual recurrence based on computed tomography (CT) images acquired within 6 months of treatment, and compare with the performance of first order appearance and lesion size measures. Consolidative and ground-glass opacity (GGO) regions were manually delineated on post-SABR CT images. Automatic consolidation expansion was also investigated to act as a surrogate for GGO position. The top features for prediction of recurrence were all texture features within the GGO and included energy, entropy, correlation, inertia, and first order texture (standard deviation of density). These predicted recurrence with 2-fold cross validation (CV) accuracies of 70-77% at 2- 5 months post-SABR, with energy, entropy, and first order texture having leave-one-out CV accuracies greater than 80%. Our results also suggest that automatic expansion of the consolidation region could eliminate the need for manual delineation, and produced reproducible results when compared to manually delineated GGO. If validated on a larger data set, this could lead to a clinically useful computer-aided diagnosis system for prediction of recurrence within 6 months of SABR and allow for early salvage therapy for patients with recurrence.

Improved sample manipulation at the STRESS-SPEC neutron diffractometer using an industrial 6-axis robot for texture and strain analyses

NASA Astrophysics Data System (ADS)

Randau, C.; Brokmeier, H. G.; Gan, W. M.; Hofmann, M.; Voeller, M.; Tekouo, W.; Al-hamdany, N.; Seidl, G.; Schreyer, A.

2015-09-01

The materials science neutron diffractometer STRESS-SPEC located at FRM II is a dedicated instrument for strain and pole figure measurements. Both methods make complementary demands on sample handling. On one hand pole figure measurements need a high degree of freedom to orient small samples and on the other hand in strain investigations it is often necessary to handle large and heavy components. Therefore a robot based sample positioning system was developed, which has the capability to provide both possibilities. Based on this new robot system further developments like a full automated sample changer system for texture measurements were accomplished. Moreover this system opens the door for combined strain and texture analysis at STRESS-SPEC.

Computer-based quantitative computed tomography image analysis in idiopathic pulmonary fibrosis: A mini review.

PubMed

Ohkubo, Hirotsugu; Nakagawa, Hiroaki; Niimi, Akio

2018-01-01

Idiopathic pulmonary fibrosis (IPF) is the most common type of progressive idiopathic interstitial pneumonia in adults. Many computer-based image analysis methods of chest computed tomography (CT) used in patients with IPF include the mean CT value of the whole lungs, density histogram analysis, density mask technique, and texture classification methods. Most of these methods offer good assessment of pulmonary functions, disease progression, and mortality. Each method has merits that can be used in clinical practice. One of the texture classification methods is reported to be superior to visual CT scoring by radiologist for correlation with pulmonary function and prediction of mortality. In this mini review, we summarize the current literature on computer-based CT image analysis of IPF and discuss its limitations and several future directions. Copyright © 2017 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.

Texture for script identification.

PubMed

Busch, Andrew; Boles, Wageeh W; Sridharan, Sridha

2005-11-01

The problem of determining the script and language of a document image has a number of important applications in the field of document analysis, such as indexing and sorting of large collections of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate the use of texture as a tool for determining the script of a document image, based on the observation that text has a distinct visual texture. An experimental evaluation of a number of commonly used texture features is conducted on a newly created script database, providing a qualitative measure of which features are most appropriate for this task. Strategies for improving classification results in situations with limited training data and multiple font types are also proposed.

Aural analysis of image texture via cepstral filtering and sonification

NASA Astrophysics Data System (ADS)

Rangayyan, Rangaraj M.; Martins, Antonio C. G.; Ruschioni, Ruggero A.

1996-03-01

Texture plays an important role in image analysis and understanding, with many applications in medical imaging and computer vision. However, analysis of texture by image processing is a rather difficult issue, with most techniques being oriented towards statistical analysis which may not have readily comprehensible perceptual correlates. We propose new methods for auditory display (AD) and sonification of (quasi-) periodic texture (where a basic texture element or `texton' is repeated over the image field) and random texture (which could be modeled as filtered or `spot' noise). Although the AD designed is not intended to be speech- like or musical, we draw analogies between the two types of texture mentioned above and voiced/unvoiced speech, and design a sonification algorithm which incorporates physical and perceptual concepts of texture and speech. More specifically, we present a method for AD of texture where the projections of the image at various angles (Radon transforms or integrals) are mapped to audible signals and played in sequence. In the case of random texture, the spectral envelopes of the projections are related to the filter spot characteristics, and convey the essential information for texture discrimination. In the case of periodic texture, the AD provides timber and pitch related to the texton and periodicity. In another procedure for sonification of periodic texture, we propose to first deconvolve the image using cepstral analysis to extract information about the texton and horizontal and vertical periodicities. The projections of individual textons at various angles are used to create a voiced-speech-like signal with each projection mapped to a basic wavelet, the horizontal period to pitch, and the vertical period to rhythm on a longer time scale. The sound pattern then consists of a serial, melody-like sonification of the patterns for each projection. We believe that our approaches provide the much-desired `natural' connection between the image data and the sounds generated. We have evaluated the sonification techniques with a number of synthetic textures. The sound patterns created have demonstrated the potential of the methods in distinguishing between different types of texture. We are investigating the application of these techniques to auditory analysis of texture in medical images such as magnetic resonance images.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, B; Yu, H; Jara, H

Purpose: To compare enhanced Laws texture derived from parametric proton density (PD) maps to other MRI-based surrogate markers (T2, PD, ADC) in assessing degrees of liver fibrosis in a murine model of hepatic fibrosis using 11.7T scanner. Methods: This animal study was IACUC approved. Fourteen mice were divided into control (n=1) and experimental (n=13). The latter were fed a DDC-supplemented diet to induce hepatic fibrosis. Liver specimens were imaged using an 11.7T scanner; the parametric PD, T2, and ADC maps were generated from spin-echo pulsed field gradient and multi-echo spin-echo acquisitions. Enhanced Laws texture analysis was applied to the PDmore » maps: first, hepatic blood vessels and liver margins were segmented/removed using an automated dual-clustering algorithm; secondly, an optimal thresholding algorithm was applied to reduce the partial volume artifact; next, mean and stdev were corrected to minimize grayscale variation across images; finally, Laws texture was extracted. Degrees of fibrosis was assessed by an experienced pathologist and digital image analysis (%Area Fibrosis). Scatterplots comparing enhanced Laws texture, T2, PD, and ADC values to degrees of fibrosis were generated and correlation coefficients were calculated. Unenhanced Laws texture was also compared to assess the effectiveness of the proposed enhancements. Results: Hepatic fibrosis and the enhanced Laws texture were strongly correlated with higher %Area Fibrosis associated with higher Laws texture (r=0.89). Only a moderate correlation was detected between %Area Fibrosis and unenhanced Laws texture (r=0.70). Strong correlation also existed between ADC and %Area Fibrosis (r=0.86). Moderate correlations were seen between %Area Fibrosis and PD (r=0.65) and T2 (r=0.66). Conclusions: Higher degrees of hepatic fibrosis are associated with increased Laws texture. The proposed enhancements improve the accuracy of Laws texture. Enhanced Laws texture features are more accurate than PD and T2 in assessing fibrosis, and can potentially serve as an accurate surrogate marker for hepatic fibrosis.« less

Genetic programming approach to evaluate complexity of texture images

NASA Astrophysics Data System (ADS)

Ciocca, Gianluigi; Corchs, Silvia; Gasparini, Francesca

2016-11-01

We adopt genetic programming (GP) to define a measure that can predict complexity perception of texture images. We perform psychophysical experiments on three different datasets to collect data on the perceived complexity. The subjective data are used for training, validation, and test of the proposed measure. These data are also used to evaluate several possible candidate measures of texture complexity related to both low level and high level image features. We select four of them (namely roughness, number of regions, chroma variance, and memorability) to be combined in a GP framework. This approach allows a nonlinear combination of the measures and could give hints on how the related image features interact in complexity perception. The proposed complexity measure M exhibits Pearson correlation coefficients of 0.890 on the training set, 0.728 on the validation set, and 0.724 on the test set. M outperforms each of all the single measures considered. From the statistical analysis of different GP candidate solutions, we found that the roughness measure evaluated on the gray level image is the most dominant one, followed by the memorability, the number of regions, and finally the chroma variance.

Local feature saliency classifier for real-time intrusion monitoring

NASA Astrophysics Data System (ADS)

Buch, Norbert; Velastin, Sergio A.

2014-07-01

We propose a texture saliency classifier to detect people in a video frame by identifying salient texture regions. The image is classified into foreground and background in real time. No temporal image information is used during the classification. The system is used for the task of detecting people entering a sterile zone, which is a common scenario for visual surveillance. Testing is performed on the Imagery Library for Intelligent Detection Systems sterile zone benchmark dataset of the United Kingdom's Home Office. The basic classifier is extended by fusing its output with simple motion information, which significantly outperforms standard motion tracking. A lower detection time can be achieved by combining texture classification with Kalman filtering. The fusion approach running at 10 fps gives the highest result of F1=0.92 for the 24-h test dataset. The paper concludes with a detailed analysis of the computation time required for the different parts of the algorithm.

Texture as a basis for acoustic classification of substrate in the nearshore region

NASA Astrophysics Data System (ADS)

Dennison, A.; Wattrus, N. J.

2016-12-01

Segmentation and classification of substrate type from two locations in Lake Superior, are predicted using multivariate statistical processing of textural measures derived from shallow-water, high-resolution multibeam bathymetric data. During a multibeam sonar survey, both bathymetric and backscatter data are collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on substrate type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. Preliminary results from an analysis of bathymetric data and ground-truth samples collected from the Amnicon River, Superior, Wisconsin, and the Lester River, Duluth, Minnesota, demonstrate the ability to process and develop a novel classification scheme of the bottom type in two geomorphologically distinct areas.

Design and evaluation of a computer based system to monitor and generalise, by areas, data from ERTS precision imagery tapes

NASA Technical Reports Server (NTRS)

Clayton, K. M. (Principal Investigator)

1975-01-01

The author has identified the following significant results. An objective system for regionalization is described, using ERTS-1 (or LANDSAT) computer compatible tapes. A range of computer programs for analysis of these tapes was developed. Emphasis is on a level of generalization appropriate to a satellite system whith repetitive global coverage. The main variables are land/water ratios and vegetation cover. The scale or texture of the pattern of change in these variables varies a good deal across the earth's surface, and it seems best if the unit of generalization adopted varies in sympathy with the surface being analyzed.

Extraction and textural characterization of above-ground areas from aerial stereo pairs: a quality assessment

NASA Astrophysics Data System (ADS)

Baillard, C.; Dissard, O.; Jamet, O.; Maître, H.

Above-ground analysis is a key point to the reconstruction of urban scenes, but it is a difficult task because of the diversity of the involved objects. We propose a new method to above-ground extraction from an aerial stereo pair, which does not require any assumption about object shape or nature. A Digital Surface Model is first produced by a stereoscopic matching stage preserving discontinuities, and then processed by a region-based Markovian classification algorithm. The produced above-ground areas are finally characterized as man-made or natural according to the grey level information. The quality of the results is assessed and discussed.

Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept

NASA Astrophysics Data System (ADS)

Vallières, Martin; Laberge, Sébastien; Diamant, André; El Naqa, Issam

2017-11-01

Texture-based radiomic models constructed from medical images have the potential to support cancer treatment management via personalized assessment of tumour aggressiveness. While the identification of stable texture features under varying imaging settings is crucial for the translation of radiomics analysis into routine clinical practice, we hypothesize in this work that a complementary optimization of image acquisition parameters prior to texture feature extraction could enhance the predictive performance of texture-based radiomic models. As a proof of concept, we evaluated the possibility of enhancing a model constructed for the early prediction of lung metastases in soft-tissue sarcomas by optimizing PET and MR image acquisition protocols via computerized simulations of image acquisitions with varying parameters. Simulated PET images from 30 STS patients were acquired by varying the extent of axial data combined per slice (‘span’). Simulated T 1-weighted and T 2-weighted MR images were acquired by varying the repetition time and echo time in a spin-echo pulse sequence, respectively. We analyzed the impact of the variations of PET and MR image acquisition parameters on individual textures, and we investigated how these variations could enhance the global response and the predictive properties of a texture-based model. Our results suggest that it is feasible to identify an optimal set of image acquisition parameters to improve prediction performance. The model constructed with textures extracted from simulated images acquired with a standard clinical set of acquisition parameters reached an average AUC of 0.84 +/- 0.01 in bootstrap testing experiments. In comparison, the model performance significantly increased using an optimal set of image acquisition parameters (p = 0.04 ), with an average AUC of 0.89 +/- 0.01 . Ultimately, specific acquisition protocols optimized to generate superior radiomics measurements for a given clinical problem could be developed and standardized via dedicated computer simulations and thereafter validated using clinical scanners.

High-resolution land cover classification using low resolution global data

NASA Astrophysics Data System (ADS)

Carlotto, Mark J.

2013-05-01

A fusion approach is described that combines texture features from high-resolution panchromatic imagery with land cover statistics derived from co-registered low-resolution global databases to obtain high-resolution land cover maps. The method does not require training data or any human intervention. We use an MxN Gabor filter bank consisting of M=16 oriented bandpass filters (0-180°) at N resolutions (3-24 meters/pixel). The size range of these spatial filters is consistent with the typical scale of manmade objects and patterns of cultural activity in imagery. Clustering reduces the complexity of the data by combining pixels that have similar texture into clusters (regions). Texture classification assigns a vector of class likelihoods to each cluster based on its textural properties. Classification is unsupervised and accomplished using a bank of texture anomaly detectors. Class likelihoods are modulated by land cover statistics derived from lower resolution global data over the scene. Preliminary results from a number of Quickbird scenes show our approach is able to classify general land cover features such as roads, built up area, forests, open areas, and bodies of water over a wide range of scenes.

Threshold responses in runoff from sub-humid heterogeneous low relief regions

NASA Astrophysics Data System (ADS)

Devito, K.; Hokanson, K. J.; Chasmer, L.; Kettridge, N.; Lukenbach, M.; Mendoza, C. A.; Moore, P.; Peters, D.; Silins, U.

2017-12-01

We examined runoff in 20 catchments (50 to 50000 km2) over a 25 year wet and dry climate cycle to understand temporal and spatial thresholds in runoff generation responses in the water limited, glaciated continental Boreal Plains (BP) eco-region of Western Canada. Annual runoff ranged over 3 orders of magnitude (<3 mm to >300 mm/year) but was poorly correlated with annual precipitation. A threshold relationship was observed with multi-year cumulative moisture deficit (CMD) that reflected temporal and spatial differences in effective storage, antecedent moisture state and hydrologic connectivity among catchments with differing portions of land-cover (e.g. wetland vs. forestland) and glacial-deposit types. During dry states (CMD< -200 mm), catchment annual low flow ranged by over one order of magnitude (2 to 80 mm/yr), and increased with percent area of coarse textured deposits. In fine textured catchments, runoff was only observed in catchments with >30% wetland area. During mesic conditions (CMD 0 mm), runoff remained very low in catchments with large proportions of forests and poorly connected open water depressions associated with fine-textured moraines. Runoff was positively correlated with percent peatland area, suggesting that peatland networks were the primary source areas of surface water to regional runoff. During the infrequent wet states (CMD > 200 mm) of the study period, runoff coefficients were similar among all catchments indicating that both forests and peatlands contributed to catchment runoff. . Rather than estimating regional runoff from topographic drainage networks, integrating CMD with the classification of catchments based on land-cover configuration and glacial-deposit type can: 1) better represent water cycling and regional sink-source dynamics controlling regional runoff, and 2) provide an effective management framework for predicting climate and land-use impacts on regional runoff in low relief glacial landscapes such as the Boreal Plain.

Shear-Coupled Grain Growth and Texture Development in a Nanocrystalline Ni-Fe Alloy during Cold Rolling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Li; Ungár, Tamás; Toth, Laszlo S.

The evolution of texture, grain size, grain shape, dislocation and twin density has been determined by synchrotron X-ray diffraction and line profile analysis in a nanocrystalline Ni- Fe alloy after cold rolling along different directions related to the initial fiber and the long axis of grains. The texture evolution has been simulated by the Taylor-type relaxed constraints viscoplastic polycrystal model. The simulations were based on the activity of partial dislocations in correlation with the experimental results of dislocation density determination. The concept of stress-induced shear-coupling is supported and strengthened by both the texture simulations and the experimentally determined evolution ofmore » the microstructure parameters. Grain-growth and texture evolution are shown to proceed by the shear-coupling mechanism supported by dislocation activity as long as the grain size is not smaller than about 20 nm.« less

A change detection method for remote sensing image based on LBP and SURF feature

NASA Astrophysics Data System (ADS)

Hu, Lei; Yang, Hao; Li, Jin; Zhang, Yun

2018-04-01

Finding the change in multi-temporal remote sensing image is important in many the image application. Because of the infection of climate and illumination, the texture of the ground object is more stable relative to the gray in high-resolution remote sensing image. And the texture features of Local Binary Patterns (LBP) and Speeded Up Robust Features (SURF) are outstanding in extracting speed and illumination invariance. A method of change detection for matched remote sensing image pair is present, which compares the similarity by LBP and SURF to detect the change and unchanged of the block after blocking the image. And region growing is adopted to process the block edge zone. The experiment results show that the method can endure some illumination change and slight texture change of the ground object.

A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves

NASA Astrophysics Data System (ADS)

Montzka, Carsten; Herbst, Michael; Weihermüller, Lutz; Verhoef, Anne; Vereecken, Harry

2017-07-01

Agroecosystem models, regional and global climate models, and numerical weather prediction models require adequate parameterization of soil hydraulic properties. These properties are fundamental for describing and predicting water and energy exchange processes at the transition zone between solid earth and atmosphere, and regulate evapotranspiration, infiltration and runoff generation. Hydraulic parameters describing the soil water retention (WRC) and hydraulic conductivity (HCC) curves are typically derived from soil texture via pedotransfer functions (PTFs). Resampling of those parameters for specific model grids is typically performed by different aggregation approaches such a spatial averaging and the use of dominant textural properties or soil classes. These aggregation approaches introduce uncertainty, bias and parameter inconsistencies throughout spatial scales due to nonlinear relationships between hydraulic parameters and soil texture. Therefore, we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the mentioned problems. The approach is based on Miller-Miller scaling in the relaxed form by Warrick, that fits the parameters of the WRC through all sub-grid WRCs to provide an effective parameterization for the grid cell at model resolution; at the same time it preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters. Based on the Mualem-van Genuchten approach we also derive the unsaturated hydraulic conductivity from the water retention functions, thereby assuming that the local parameters are also valid for this function. In addition, via the Warrick scaling parameter λ, information on global sub-grid scaling variance is given that enables modellers to improve dynamical downscaling of (regional) climate models or to perturb hydraulic parameters for model ensemble output generation. The present analysis is based on the ROSETTA PTF of Schaap et al. (2001) applied to the SoilGrids1km data set of Hengl et al. (2014). The example data set is provided at a global resolution of 0.25° at https://doi.org/10.1594/PANGAEA.870605.

Improved disparity map analysis through the fusion of monocular image segmentations

NASA Technical Reports Server (NTRS)

Perlant, Frederic P.; Mckeown, David M.

1991-01-01

The focus is to examine how estimates of three dimensional scene structure, as encoded in a scene disparity map, can be improved by the analysis of the original monocular imagery. The utilization of surface illumination information is provided by the segmentation of the monocular image into fine surface patches of nearly homogeneous intensity to remove mismatches generated during stereo matching. These patches are used to guide a statistical analysis of the disparity map based on the assumption that such patches correspond closely with physical surfaces in the scene. Such a technique is quite independent of whether the initial disparity map was generated by automated area-based or feature-based stereo matching. Stereo analysis results are presented on a complex urban scene containing various man-made and natural features. This scene contains a variety of problems including low building height with respect to the stereo baseline, buildings and roads in complex terrain, and highly textured buildings and terrain. The improvements are demonstrated due to monocular fusion with a set of different region-based image segmentations. The generality of this approach to stereo analysis and its utility in the development of general three dimensional scene interpretation systems are also discussed.

Diabetic peripheral neuropathy assessment through texture based analysis of corneal nerve images

NASA Astrophysics Data System (ADS)

Silva, Susana F.; Gouveia, Sofia; Gomes, Leonor; Negrão, Luís; João Quadrado, Maria; Domingues, José Paulo; Morgado, António Miguel

2015-05-01

Diabetic peripheral neuropathy (DPN) is one common complication of diabetes. Early diagnosis of DPN often fails due to the non-availability of a simple, reliable, non-invasive method. Several published studies show that corneal confocal microscopy (CCM) can identify small nerve fibre damage and quantify the severity of DPN, using nerve morphometric parameters. Here, we used image texture features, extracted from corneal sub-basal nerve plexus images, obtained in vivo by CCM, to identify DPN patients, using classification techniques. A SVM classifier using image texture features was used to identify (DPN vs. No DPN) DPN patients. The accuracies were 80.6%, when excluding diabetic patients without neuropathy, and 73.5%, when including diabetic patients without diabetic neuropathy jointly with healthy controls. The results suggest that texture analysis might be used as a complementing technique for DPN diagnosis, without requiring nerve segmentation in CCM images. The results also suggest that this technique has enough sensitivity to detect early disorders in the corneal nerves of diabetic patients.

Unsupervised classification of cirrhotic livers using MRI data

NASA Astrophysics Data System (ADS)

Lee, Gobert; Kanematsu, Masayuki; Kato, Hiroki; Kondo, Hiroshi; Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Hoshi, Hiroaki

2008-03-01

Cirrhosis of the liver is a chronic disease. It is characterized by the presence of widespread nodules and fibrosis in the liver which results in characteristic texture patterns. Computerized analysis of hepatic texture patterns is usually based on regions-of-interest (ROIs). However, not all ROIs are typical representatives of the disease stage of the liver from which the ROIs originated. This leads to uncertainties in the ROI labels (diseased or non-diseased). On the other hand, supervised classifiers are commonly used in determining the assignment rule. This presents a problem as the training of a supervised classifier requires the correct labels of the ROIs. The main purpose of this paper is to investigate the use of an unsupervised classifier, the k-means clustering, in classifying ROI based data. In addition, a procedure for generating a receiver operating characteristic (ROC) curve depicting the classification performance of k-means clustering is also reported. Hepatic MRI images of 44 patients (16 cirrhotic; 28 non-cirrhotic) are used in this study. The MRI data are derived from gadolinium-enhanced equilibrium phase images. For each patient, 10 ROIs selected by an experienced radiologist and 7 texture features measured on each ROI are included in the MRI data. Results of the k-means classifier are depicted using an ROC curve. The area under the curve (AUC) has a value of 0.704. This is slightly lower than but comparable to that of LDA and ANN classifiers which have values 0.781 and 0.801, respectively. Methods in constructing ROC curve in relation to k-means clustering have not been previously reported in the literature.

A multi-topographical-instrument analysis: the breast implant texture measurement

NASA Astrophysics Data System (ADS)

Garabédian, Charles; Delille, Rémi; Deltombe, Raphaël; Anselme, Karine; Atlan, Michael; Bigerelle, Maxence

2017-06-01

Capsular contracture is a major complication after implant-based breast augmentation. To address this tissue reaction, most manufacturers texture the outer breast implant surfaces with calibrated salt grains. However, the analysis of these surfaces on sub-micron scales has been under-studied. This scale range is of interest to understand the future of silicone particles potentially released from the implant surface and the aetiology of newly reported complications, such as Anaplastic Large Cell Lymphoma. The surface measurements were accomplished by tomography and by two optical devices based on interferometry and on focus variation. The robustness of the measurements was investigated from the tissue scale to the cellular scale. The macroscopic pore-based structure of the textured implant surfaces is consistently measured by the three instruments. However, the multi-scale analyses start to be discrepant in a scale range between 50 µm and 500 µm characteristic of a finer secondary roughness regardless of the pore shape. The focus variation and the micro-tomography would fail to capture this roughness regime because of a focus-related optical artefact and of step-shaped artefact respectively.

Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement.

PubMed

Shen, Qijun; Shan, Yanna; Hu, Zhengyu; Chen, Wenhui; Yang, Bing; Han, Jing; Huang, Yanfang; Xu, Wen; Feng, Zhan

2018-04-30

To objectively quantify intracranial hematoma (ICH) enlargement by analysing the image texture of head CT scans and to provide objective and quantitative imaging parameters for predicting early hematoma enlargement. We retrospectively studied 108 ICH patients with baseline non-contrast computed tomography (NCCT) and 24-h follow-up CT available. Image data were assessed by a chief radiologist and a resident radiologist. Consistency analysis between observers was tested. The patients were divided into training set (75%) and validation set (25%) by stratified sampling. Patients in the training set were dichotomized according to 24-h hematoma expansion ≥ 33%. Using the Laplacian of Gaussian bandpass filter, we chose different anatomical spatial domains ranging from fine texture to coarse texture to obtain a series of derived parameters (mean grayscale intensity, variance, uniformity) in order to quantify and evaluate all data. The parameters were externally validated on validation set. Significant differences were found between the two groups of patients within variance at V 1.0 and in uniformity at U 1.0 , U 1.8 and U 2.5 . The intraclass correlation coefficients for the texture parameters were between 0.67 and 0.99. The area under the ROC curve between the two groups of ICH cases was between 0.77 and 0.92. The accuracy of validation set by CTTA was 0.59-0.85. NCCT texture analysis can objectively quantify the heterogeneity of ICH and independently predict early hematoma enlargement. • Heterogeneity is helpful in predicting ICH enlargement. • CTTA could play an important role in predicting early ICH enlargement. • After filtering, fine texture had the best diagnostic performance. • The histogram-based uniformity parameters can independently predict ICH enlargement. • CTTA is more objective, more comprehensive, more independently operable, than previous methods.

Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features

NASA Astrophysics Data System (ADS)

Qin, Xulei; Lu, Guolan; Sechopoulos, Ioannis; Fei, Baowei

2014-03-01

Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.

Texture Analysis of Recurrence Plots Based on Wavelets and PSO for Laryngeal Pathologies Detection.

PubMed

Souza, Taciana A; Vieira, Vinícius J D; Correia, Suzete E N; Costa, Silvana L N C; de A Costa, Washington C; Souza, Micael A

2015-01-01

This paper deals with the discrimination between healthy and pathological speech signals using recurrence plots and wavelet transform with texture features. Approximation and detail coefficients are obtained from the recurrence plots using Haar wavelet transform, considering one decomposition level. The considered laryngeal pathologies are: paralysis, Reinke's edema and nodules. Accuracy rates above 86% were obtained by means of the employed method.

Characterization of Plastic flow and Resulting Micro-Textures in a Friction Stir Weld

NASA Technical Reports Server (NTRS)

Schneider, J. A.; Nunes, A. C., Jr.

2003-01-01

The mechanically affected zone of a friction stir weld (FSW) cross section exhibits two distinct microstructural regions, possibly the residues of two distinct currents of metal in the FSW flow process. In this study the respective textures of these microstructural regions are investigated using orientation image mapping (OIM).

Geospatial and Remote Sensing-based Indicators of Settlement Type---Differentiating Informal and Formal Settlements in Guatemala City

NASA Astrophysics Data System (ADS)

Owen, Karen K.

This research addresses the need for reliable, repeatable, quantitative measures to differentiate informal (slum) from formal (planned) settlements using commercial very high resolution imagery and elevation data. Measuring the physical, spatial and spectral qualities of informal settlements is an important precursor for evaluating success toward improving the lives of 100 million slum dwellers worldwide, as pledged by the United Nations Millennium Development Goal Target 7D. A variety of measures were tested based on surface material spectral properties, texture, built-up structure, road network accessibility, and geomorphology from twelve communities in Guatemala City to reveal statistically significant differences between informal and formal settlements that could be applied to other parts of the world without the need for costly or dangerous field surveys. When information from satellite imagery is constrained to roads and residential boundaries, a more precise understanding of human habitation is produced. A classification and regression tree (CART) approach and linear discriminant function analysis enabled a variable dimensionality reduction from the original 23 to 6 variables that are sufficient to differentiate a settlement as informal or formal. The results demonstrate that the entropy texture of roads, the degree of asphalt road surface, the vegetation patch compactness and patch size, the percent of bare soil land cover, the geomorphic profile convexity of the terrain, and the road density distinguish informal from formal settlements with 87--92% accuracy when results are cross-validated. The variables with highest contribution to model outcome that are common to both approaches are entropy texture of roads, vegetation patch size, and vegetation compactness suggesting that road texture, surface materials and vegetation provide the necessary characteristics to distinguish the level of informality of a settlement. The results will assist urban planners and settlement analysts who must process vast amounts of imagery worldwide, enabling them to report annually on slum conditions. An added benefit is the ability to use the measures in data-poor regions of the world without field surveys.

A Peek into a Cul-De-Sac and a Mews of Martian Dust Storm Activity: Western Hellas and Syria-Claritas Fossae During Mars Year 29

NASA Astrophysics Data System (ADS)

Heavens, N. G.

2016-12-01

Western Hellas Planitia (WHP) and the region encompassed by Syria Planum and Claritas Fossae are the main centers of textured dust storm activity in Mars's southern low to mid-latitudes. (Texture in this context refers to distinct fine structure at the cloud tops indicative of active lifting.) WHP is a well-known initiation zone for regional and global dust storm activity and often the end point of the Utopia "flushing storm" track. Syria-Claritas Fossae (SCF), too, can be a lifting center in global dust storm activity. Indeed, SCF and the area to its west was the region most denuded of dust by the Mars Year (MY) 25 global dust storm, perhaps suggesting that SCF contained the principal lifting center of the storm. Thus, if the Acidalia and Utopia storm tracks are Mars's dust storm alleys, through which dust storms pass quickly again and again; WHP might be a cul-de-sac and SCF something like a mews, where dust storm activity can enter more or less easily but may not as easily leave. In this presentation, I will focus on dust storm activity in these areas in a typical non-global dust storm year, MY 29. Synthesizing visible imagery by the Mars Color Imager (MARCI) on board Mars Reconnaissance Orbiter (MRO) and Mars Climate Sounder (MCS) also on board MRO, I will consider the climatology, morphology, texture, and vertical structure of dust storm activity in these areas in order to infer their governing dynamics. This investigation has two aims: (1) to understand why these areas are centers of textured dust storm activity; and (2) to connect the characteristics of smaller-scale dust storm activity in these regions to the underlying dynamics in order to understand the role of WHP and SCF in the dynamics of global dust storms. This work is supported by NASA's Mars Data Analysis Program (NNX14AM32G).

An Active Patch Model for Real World Texture and Appearance Classification

PubMed Central

Mao, Junhua; Zhu, Jun; Yuille, Alan L.

2014-01-01

This paper addresses the task of natural texture and appearance classification. Our goal is to develop a simple and intuitive method that performs at state of the art on datasets ranging from homogeneous texture (e.g., material texture), to less homogeneous texture (e.g., the fur of animals), and to inhomogeneous texture (the appearance patterns of vehicles). Our method uses a bag-of-words model where the features are based on a dictionary of active patches. Active patches are raw intensity patches which can undergo spatial transformations (e.g., rotation and scaling) and adjust themselves to best match the image regions. The dictionary of active patches is required to be compact and representative, in the sense that we can use it to approximately reconstruct the images that we want to classify. We propose a probabilistic model to quantify the quality of image reconstruction and design a greedy learning algorithm to obtain the dictionary. We classify images using the occurrence frequency of the active patches. Feature extraction is fast (about 100 ms per image) using the GPU. The experimental results show that our method improves the state of the art on a challenging material texture benchmark dataset (KTH-TIPS2). To test our method on less homogeneous or inhomogeneous images, we construct two new datasets consisting of appearance image patches of animals and vehicles cropped from the PASCAL VOC dataset. Our method outperforms competing methods on these datasets. PMID:25531013

Mesh-free based variational level set evolution for breast region segmentation and abnormality detection using mammograms.

PubMed

Kashyap, Kanchan L; Bajpai, Manish K; Khanna, Pritee; Giakos, George

2018-01-01

Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c-means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function. Copyright © 2017 John Wiley & Sons, Ltd.

Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis.

PubMed

Pu, Hongbin; Sun, Da-Wen; Ma, Ji; Cheng, Jun-Hu

2015-01-01

The potential of visible and near infrared hyperspectral imaging was investigated as a rapid and nondestructive technique for classifying fresh and frozen-thawed meats by integrating critical spectral and image features extracted from hyperspectral images in the region of 400-1000 nm. Six feature wavelengths (400, 446, 477, 516, 592 and 686 nm) were identified using uninformative variable elimination and successive projections algorithm. Image textural features of the principal component images from hyperspectral images were obtained using histogram statistics (HS), gray level co-occurrence matrix (GLCM) and gray level-gradient co-occurrence matrix (GLGCM). By these spectral and textural features, probabilistic neural network (PNN) models for classification of fresh and frozen-thawed pork meats were established. Compared with the models using the optimum wavelengths only, optimum wavelengths with HS image features, and optimum wavelengths with GLCM image features, the model integrating optimum wavelengths with GLGCM gave the highest classification rate of 93.14% and 90.91% for calibration and validation sets, respectively. Results indicated that the classification accuracy can be improved by combining spectral features with textural features and the fusion of critical spectral and textural features had better potential than single spectral extraction in classifying fresh and frozen-thawed pork meat. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effect of Par Frying on Composition and Texture of Breaded and Battered Catfish

PubMed Central

Woods, Kristin; Lea, Jeanne M.; Brashear, Suzanne S.; Boue, Stephen M.; Daigle, Kim W.; Bett-Garber, Karen L.

2018-01-01

Catfish is often consumed as a breaded and battered fried product; however, there is increasing interest in breaded and battered baked products as a healthier alternative. Par frying can improve the texture properties of breaded and battered baked products, but there are concerns about the increase in lipid uptake from par frying. The objective of this study was to examine the effect of different batters (rice, corn, and wheat) and the effect of par frying on the composition and texture properties of baked catfish. Catfish fillets were cut strips and then coated with batters, which had similar viscosities. Half of the strips were par fried in 177 °C vegetable oil for 1 min and the other half were not par fried. Samples were baked at 177 °C for 25 min. Analysis included % batter adhesion, cooking loss, protein, lipid, ash, and moisture, plus hardness and fracture quality measured using a texture analyzer. A trained sensory panel evaluated both breading and flesh texture attributes. Results found the lipid content of par fried treatments were significantly higher for both corn and wheat batters than for non-par fried treatments. Sensory analysis indicated that the texture of the coatings in the par fried treatments were significantly greater for hardness attributes. Fillet flakiness was significantly greater in the par fried treatments and corn-based batters had moister fillet strips compared to the wheat flour batters. Texture analyzer hardness values were higher for the par fried treatments. PMID:29570660

Segmentation of anatomical branching structures based on texture features and conditional random field

NASA Astrophysics Data System (ADS)

Nuzhnaya, Tatyana; Bakic, Predrag; Kontos, Despina; Megalooikonomou, Vasileios; Ling, Haibin

2012-02-01

This work is a part of our ongoing study aimed at understanding a relation between the topology of anatomical branching structures with the underlying image texture. Morphological variability of the breast ductal network is associated with subsequent development of abnormalities in patients with nipple discharge such as papilloma, breast cancer and atypia. In this work, we investigate complex dependence among ductal components to perform segmentation, the first step for analyzing topology of ductal lobes. Our automated framework is based on incorporating a conditional random field with texture descriptors of skewness, coarseness, contrast, energy and fractal dimension. These features are selected to capture the architectural variability of the enhanced ducts by encoding spatial variations between pixel patches in galactographic image. The segmentation algorithm was applied to a dataset of 20 x-ray galactograms obtained at the Hospital of the University of Pennsylvania. We compared the performance of the proposed approach with fully and semi automated segmentation algorithms based on neural network classification, fuzzy-connectedness, vesselness filter and graph cuts. Global consistency error and confusion matrix analysis were used as accuracy measurements. For the proposed approach, the true positive rate was higher and the false negative rate was significantly lower compared to other fully automated methods. This indicates that segmentation based on CRF incorporated with texture descriptors has potential to efficiently support the analysis of complex topology of the ducts and aid in development of realistic breast anatomy phantoms.

Morphological image analysis for classification of gastrointestinal tissues using optical coherence tomography

NASA Astrophysics Data System (ADS)

Garcia-Allende, P. Beatriz; Amygdalos, Iakovos; Dhanapala, Hiruni; Goldin, Robert D.; Hanna, George B.; Elson, Daniel S.

2012-01-01

Computer-aided diagnosis of ophthalmic diseases using optical coherence tomography (OCT) relies on the extraction of thickness and size measures from the OCT images, but such defined layers are usually not observed in emerging OCT applications aimed at "optical biopsy" such as pulmonology or gastroenterology. Mathematical methods such as Principal Component Analysis (PCA) or textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric auto-correlation (CSAC) and spatial grey-level dependency matrices (SGLDM), as well as, quantitative measurements of the attenuation coefficient have been previously proposed to overcome this problem. We recently proposed an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technology for feature quantification. OCT images were first segmented in the axial direction in an automated manner according to intensity. Afterwards, a morphological analysis of the segmented OCT images was employed for quantifying the features that served for tissue classification. In this study, a PCA processing of the extracted features is accomplished to combine their discriminative power in a lower number of dimensions. Ready discrimination of gastrointestinal surgical specimens is attained demonstrating that the approach further surpasses the algorithms previously reported and is feasible for tissue classification in the clinical setting.

Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease

NASA Astrophysics Data System (ADS)

Kato, Noriji; Fukui, Motofumi; Isozaki, Takashi

2009-02-01

Many automated techniques have been proposed to classify diffuse lung disease patterns. Most of the techniques utilize texture analysis approaches with second and higher order statistics, and show successful classification result among various lung tissue patterns. However, the approaches do not work well for the patterns with inhomogeneous texture distribution within a region of interest (ROI), such as reticular and honeycombing patterns, because the statistics can only capture averaged feature over the ROI. In this work, we have introduced the bag-of-features approach to overcome this difficulty. In the approach, texture images are represented as histograms or distributions of a few basic primitives, which are obtained by clustering local image features. The intensity descriptor and the Scale Invariant Feature Transformation (SIFT) descriptor are utilized to extract the local features, which have significant discriminatory power due to their specificity to a particular image class. In contrast, the drawback of the local features is lack of invariance under translation and rotation. We improved the invariance by sampling many local regions so that the distribution of the local features is unchanged. We evaluated the performance of our system in the classification task with 5 image classes (ground glass, reticular, honeycombing, emphysema, and normal) using 1109 ROIs from 211 patients. Our system achieved high classification accuracy of 92.8%, which is superior to that of the conventional system with the gray level co-occurrence matrix (GLCM) feature especially for inhomogeneous texture patterns.

Textures in spinel peridotite mantle xenoliths using micro-CT scanning: Examples from Canary Islands and France

NASA Astrophysics Data System (ADS)

Bhanot, K. K.; Downes, H.; Petrone, C. M.; Humphreys-Williams, E.

2017-04-01

Spinel pyroxene-clusters, which are intergrowths of spinel, orthopyroxene and clinopyroxene in mantle xenoliths, have been investigated through the use of micro-CT (μ-CT) in this study. Samples have been studied from two different tectonic settings: (1) the northern Massif Central, France, an uplifted and rifted plateau on continental lithosphere and (2) Lanzarote in the Canary Islands, an intraplate volcanic island on old oceanic lithosphere. μ-CT analysis of samples from both locations has revealed a range of spinel textures from small < 2 mm microcrystals which can be either spatially concentrated or distributed more evenly throughout the rock with a lineation, to large 4-12 mm individual clusters with ellipsoidal complex vermicular textures in random orientation. Microprobe analyses of pyroxenes inside and outside the clusters show broadly similar compositions. Spinel-pyroxene clusters are the result of a transition of shallow lithospheric mantle from the garnet stability field to the spinel stability field. Both the northern Massif Central and Lanzarote are regions that have experienced significant lithospheric thinning. This process provides a mechanism where the sub-solidus reaction of olivine + garnet = orthopyroxene + clinopyroxene + spinel is satisfied by providing a pathway from garnet peridotite to spinel peridotite. We predict that such textures would only occur in the mantle beneath regions that show evidence of thinning of the lithospheric mantle. Metasomatic reactions are seen around spinel-pyroxene clusters in some Lanzarote xenoliths, so metasomatism post-dated cluster formation.

(abstract) Topographic Signatures in Geology

NASA Technical Reports Server (NTRS)

Farr, Tom G.; Evans, Diane L.

1996-01-01

Topographic information is required for many Earth Science investigations. For example, topography is an important element in regional and global geomorphic studies because it reflects the interplay between the climate-driven processes of erosion and the tectonic processes of uplift. A number of techniques have been developed to analyze digital topographic data, including Fourier texture analysis. A Fourier transform of the topography of an area allows the spatial frequency content of the topography to be analyzed. Band-pass filtering of the transform produces images representing the amplitude of different spatial wavelengths. These are then used in a multi-band classification to map units based on their spatial frequency content. The results using a radar image instead of digital topography showed good correspondence to a geologic map, however brightness variations in the image unrelated to topography caused errors. An additional benefit to the use of Fourier band-pass images for the classification is that the textural signatures of the units are quantative measures of the spatial characteristics of the units that may be used to map similar units in similar environments.

Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy.

PubMed

Pyka, Thomas; Bundschuh, Ralph A; Andratschke, Nicolaus; Mayer, Benedikt; Specht, Hanno M; Papp, Laszló; Zsótér, Norbert; Essler, Markus

2015-04-22

Textural features in FDG-PET have been shown to provide prognostic information in a variety of tumor entities. Here we evaluate their predictive value for recurrence and prognosis in NSCLC patients receiving primary stereotactic radiation therapy (SBRT). 45 patients with early stage NSCLC (T1 or T2 tumor, no lymph node or distant metastases) were included in this retrospective study and followed over a median of 21.4 months (range 3.1-71.1). All patients were considered non-operable due to concomitant disease and referred to SBRT as the primary treatment modality. Pre-treatment FDG-PET/CT scans were obtained from all patients. SUV and volume-based analysis as well as extraction of textural features based on neighborhood gray-tone difference matrices (NGTDM) and gray-level co-occurence matrices (GLCM) were performed using InterView Fusion™ (Mediso Inc., Budapest). The ability to predict local recurrence (LR), lymph node (LN) and distant metastases (DM) was measured using the receiver operating characteristic (ROC). Univariate and multivariate analysis of overall and disease-specific survival were executed. 7 out of 45 patients (16%) experienced LR, 11 (24%) LN and 11 (24%) DM. ROC revealed a significant correlation of several textural parameters with LR with an AUC value for entropy of 0.872. While there was also a significant correlation of LR with tumor size in the overall cohort, only texture was predictive when examining T1 (tumor diameter < = 3 cm) and T2 (>3 cm) subgroups. No correlation of the examined PET parameters with LN or DM was shown. In univariate survival analysis, both heterogeneity and tumor size were predictive for disease-specific survival, but only texture determined by entropy was determined as an independent factor in multivariate analysis (hazard ratio 7.48, p = .016). Overall survival was not significantly correlated to any examined parameter, most likely due to the high comorbidity in our cohort. Our study adds to the growing evidence that tumor heterogeneity as described by FDG-PET texture is associated with response to radiation therapy in NSCLC. The results may be helpful into identifying patients who might profit from an intensified treatment regime, but need to be verified in a prospective patient cohort before being incorporated into routine clinical practice.

Computer-aided diagnosis of liver tumors on computed tomography images.

PubMed

Chang, Chin-Chen; Chen, Hong-Hao; Chang, Yeun-Chung; Yang, Ming-Yang; Lo, Chung-Ming; Ko, Wei-Chun; Lee, Yee-Fan; Liu, Kao-Lang; Chang, Ruey-Feng

2017-07-01

Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images. A total of 71 histologically-proven liver tumors including 49 benign and 22 malignant lesions were evaluated with the proposed CAD system to evaluate its performance. Tumors were identified by the user and then segmented using a region growing algorithm. After tumor segmentation, three kinds of features were obtained for each tumor, including texture, shape, and kinetic curve. The texture was quantified using 3 dimensional (3-D) texture data of the tumor based on the grey level co-occurrence matrix (GLCM). Compactness, margin, and an elliptic model were used to describe the 3-D shape of the tumor. The kinetic curve was established from each phase of tumor and represented as variations in density between each phase. Backward elimination was used to select the best combination of features, and binary logistic regression analysis was used to classify the tumors with leave-one-out cross validation. The accuracy and sensitivity for the texture were 71.82% and 68.18%, respectively, which were better than for the shape and kinetic curve under closed specificity. Combining all of the features achieved the highest accuracy (58/71, 81.69%), sensitivity (18/22, 81.82%), and specificity (40/49, 81.63%). The Az value of combining all features was 0.8713. Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using our proposed CAD system. Copyright © 2017 Elsevier B.V. All rights reserved.

Temporal radiographic texture analysis in the detection of periprosthetic osteolysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilkie, Joel R.; Giger, Maryellen L.; Chinander, Michael R.

2008-01-15

Periprosthetic osteolysis is one of the most serious long-term problems in total hip arthroplasty. It has been primarily attributed to the body's inflammatory response to submicron polyethylene particles worn from the hip implant, and it leads to bone loss and structural deterioration in the surrounding bone. It was previously demonstrated that radiographic texture analysis (RTA) has the ability to distinguish between osteolysis and normal cases at the time of clinical detection of the disease; however, that analysis did not take into account the changes in texture over time. The goal of this preliminary analysis, however, is to assess the abilitymore » of temporal radiographic texture analysis (tRTA) to distinguish between patients who develop osteolysis and normal cases. Two tRTA methods were used in the study: the RTA feature change from baseline at various follow-up intervals and the slope of the best-fit line to the RTA data series. These tRTA methods included Fourier-based and fractal-based features calculated from digitized images of 202 total hip replacement cases, including 70 that developed osteolysis. Results show that separation between the osteolysis and normal groups increased over time for the feature difference method, as the disease progressed, with area under the curve (AUC) values from receiver operating characteristic analysis of 0.65 to 0.72 at 15 years postsurgery. Separation for the slope method was also evident, with AUC values ranging from 0.65 to 0.76 for the task of distinguishing between osteolysis and normal cases. The results suggest that tRTA methods have the ability to measure changes in trabecular structure, and may be useful in the early detection of periprosthetic osteolysis.« less

Automatic optic disc segmentation based on image brightness and contrast

NASA Astrophysics Data System (ADS)

Lu, Shijian; Liu, Jiang; Lim, Joo Hwee; Zhang, Zhuo; Tan, Ngan Meng; Wong, Wing Kee; Li, Huiqi; Wong, Tien Yin

2010-03-01

Untreated glaucoma leads to permanent damage of the optic nerve and resultant visual field loss, which can progress to blindness. As glaucoma often produces additional pathological cupping of the optic disc (OD), cupdisc- ratio is one measure that is widely used for glaucoma diagnosis. This paper presents an OD localization method that automatically segments the OD and so can be applied for the cup-disc-ratio based glaucoma diagnosis. The proposed OD segmentation method is based on the observations that the OD is normally much brighter and at the same time have a smoother texture characteristics compared with other regions within retinal images. Given a retinal image we first capture the ODs smooth texture characteristic by a contrast image that is constructed based on the local maximum and minimum pixel lightness within a small neighborhood window. The centre of the OD can then be determined according to the density of the candidate OD pixels that are detected by retinal image pixels of the lowest contrast. After that, an OD region is approximately determined by a pair of morphological operations and the OD boundary is finally determined by an ellipse that is fitted by the convex hull of the detected OD region. Experiments over 71 retinal images of different qualities show that the OD region overlapping reaches up to 90.37% according to the OD boundary ellipses determined by our proposed method and the one manually plotted by an ophthalmologist.

A simplified regional-scale electromagnetic induction - Salinity calibration model using ANOCOVA modeling techniques

USDA-ARS?s Scientific Manuscript database

Directed soil sampling based on geospatial measurements of apparent soil electrical conductivity (ECa) is a potential means of characterizing the spatial variability of any soil property that influences ECa including soil salinity, water content, texture, bulk density, organic matter, and cation exc...

Approach for scene reconstruction from the analysis of a triplet of still images

NASA Astrophysics Data System (ADS)

Lechat, Patrick; Le Mestre, Gwenaelle; Pele, Danielle

1997-03-01

Three-dimensional modeling of a scene from the automatic analysis of 2D image sequences is a big challenge for future interactive audiovisual services based on 3D content manipulation such as virtual vests, 3D teleconferencing and interactive television. We propose a scheme that computes 3D objects models from stereo analysis of image triplets shot by calibrated cameras. After matching the different views with a correlation based algorithm, a depth map referring to a given view is built by using a fusion criterion taking into account depth coherency, visibility constraints and correlation scores. Because luminance segmentation helps to compute accurate object borders and to detect and improve the unreliable depth values, a two steps segmentation algorithm using both depth map and graylevel image is applied to extract the objects masks. First an edge detection segments the luminance image in regions and a multimodal thresholding method selects depth classes from the depth map. Then the regions are merged and labelled with the different depth classes numbers by using a coherence test on depth values according to the rate of reliable and dominant depth values and the size of the regions. The structures of the segmented objects are obtained with a constrained Delaunay triangulation followed by a refining stage. Finally, texture mapping is performed using open inventor or VRML1.0 tools.

Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery

NASA Astrophysics Data System (ADS)

Beguet, Benoit; Guyon, Dominique; Boukir, Samia; Chehata, Nesrine

2014-10-01

The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick's texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRiF, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on Quickbird and Pléiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure variables from VHR satellite images. Thus an average prediction error of ˜ 1.1 m is expected on crown diameter, ˜ 0.9 m on tree spacing, ˜ 3 m on height and ˜ 0.06 m on diameter at breast height.

ASCII Art Synthesis from Natural Photographs.

PubMed

Xu, Xuemiao; Zhong, Linyuan; Xie, Minshan; Liu, Xueting; Qin, Jing; Wong, Tien-Tsin

2017-08-01

While ASCII art is a worldwide popular art form, automatic generating structure-based ASCII art from natural photographs remains challenging. The major challenge lies on extracting the perception-sensitive structure from the natural photographs so that a more concise ASCII art reproduction can be produced based on the structure. However, due to excessive amount of texture in natural photos, extracting perception-sensitive structure is not easy, especially when the structure may be weak and within the texture region. Besides, to fit different target text resolutions, the amount of the extracted structure should also be controllable. To tackle these challenges, we introduce a visual perception mechanism of non-classical receptive field modulation (non-CRF modulation) from physiological findings to this ASCII art application, and propose a new model of non-CRF modulation which can better separate the weak structure from the crowded texture, and also better control the scale of texture suppression. Thanks to our non-CRF model, more sensible ASCII art reproduction can be obtained. In addition, to produce more visually appealing ASCII arts, we propose a novel optimization scheme to obtain the optimal placement of proportional-font characters. We apply our method on a rich variety of images, and visually appealing ASCII art can be obtained in all cases.

Through-process modelling of texture and anisotropy in AA5182

NASA Astrophysics Data System (ADS)

Crumbach, M.; Neumann, L.; Goerdeler, M.; Aretz, H.; Gottstein, G.; Kopp, R.

2006-07-01

A through-process texture and anisotropy prediction for AA5182 sheet production from hot rolling through cold rolling and annealing is reported. Thermo-mechanical process data predicted by the finite element method (FEM) package T-Pack based on the software LARSTRAN were fed into a combination of physics based microstructure models for deformation texture (GIA), work hardening (3IVM), nucleation texture (ReNuc), and recrystallization texture (StaRT). The final simulated sheet texture was fed into a FEM simulation of cup drawing employing a new concept of interactively updated texture based yield locus predictions. The modelling results of texture development and anisotropy were compared to experimental data. The applicability to other alloys and processes is discussed.

SU-E-J-262: Variability in Texture Analysis of Gynecological Tumors in the Context of An 18F-FDG PET Adaptive Protocol

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nawrocki, J; Chino, J; Das, S

Purpose: This study examines the effect on texture analysis due to variable reconstruction of PET images in the context of an adaptive FDG PET protocol for node positive gynecologic cancer patients. By measuring variability in texture features from baseline and intra-treatment PET-CT, we can isolate unreliable texture features due to large variation. Methods: A subset of seven patients with node positive gynecological cancers visible on PET was selected for this study. Prescribed dose varied between 45–50.4Gy, with a 55–70Gy boost to the PET positive nodes. A baseline and intratreatment (between 30–36Gy) PET-CT were obtained on a Siemens Biograph mCT. Eachmore » clinical PET image set was reconstructed 6 times using a TrueX+TOF algorithm with varying iterations and Gaussian filter. Baseline and intra-treatment primary GTVs were segmented using PET Edge (MIM Software Inc., Cleveland, OH), a semi-automatic gradient-based algorithm, on the clinical PET and transferred to the other reconstructed sets. Using an in-house MATLAB program, four 3D texture matrices describing relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 textural features characterizing texture were calculated in addition to SUV histogram features. The percent variability among parameters was first calculated. Each reconstructed texture feature from baseline and intra-treatment per patient was normalized to the clinical baseline scan and compared using the Wilcoxon signed-rank test in order to isolate variations due to reconstruction parameters. Results: For the baseline scans, 13 texture features showed a mean range greater than 10%. For the intra scans, 28 texture features showed a mean range greater than 10%. Comparing baseline to intra scans, 25 texture features showed p <0.05. Conclusion: Variability due to different reconstruction parameters increased with treatment, however, the majority of texture features showed significant changes during treatment independent of reconstruction effects.« less

Volumetric characterization of human patellar cartilage matrix on phase contrast x-ray computed tomography

NASA Astrophysics Data System (ADS)

Abidin, Anas Z.; Nagarajan, Mahesh B.; Checefsky, Walter A.; Coan, Paola; Diemoz, Paul C.; Hobbs, Susan K.; Huber, Markus B.; Wismüller, Axel

2015-03-01

Phase contrast X-ray computed tomography (PCI-CT) has recently emerged as a novel imaging technique that allows visualization of cartilage soft tissue, subsequent examination of chondrocyte patterns, and their correlation to osteoarthritis. Previous studies have shown that 2D texture features are effective at distinguishing between healthy and osteoarthritic regions of interest annotated in the radial zone of cartilage matrix on PCI-CT images. In this study, we further extend the texture analysis to 3D and investigate the ability of volumetric texture features at characterizing chondrocyte patterns in the cartilage matrix for purposes of classification. Here, we extracted volumetric texture features derived from Minkowski Functionals and gray-level co-occurrence matrices (GLCM) from 496 volumes of interest (VOI) annotated on PCI-CT images of human patellar cartilage specimens. The extracted features were then used in a machine-learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The best classification performance was observed with GLCM features correlation (AUC = 0.83 +/- 0.06) and homogeneity (AUC = 0.82 +/- 0.07), which significantly outperformed all Minkowski Functionals (p < 0.05). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving GLCM-derived statistical features can distinguish between healthy and osteoarthritic tissue with high accuracy.

Photodegradation of polycyclic aromatic hydrocarbons in soils under a climate change base scenario.

PubMed

Marquès, Montse; Mari, Montse; Audí-Miró, Carme; Sierra, Jordi; Soler, Albert; Nadal, Martí; Domingo, José L

2016-04-01

The photodegradation of polycyclic aromatic hydrocarbons (PAHs) in two typical Mediterranean soils, either coarse- or fine-textured, was here investigated. Soil samples, spiked with the 16 US EPA priority PAHs, were incubated in a climate chamber at stable conditions of temperature (20 °C) and light (9.6 W m(-2)) for 28 days, simulating a climate change base scenario. PAH concentrations in soils were analyzed throughout the experiment, and correlated with data obtained by means of Microtox(®) ecotoxicity test. Photodegradation was found to be dependent on exposure time, molecular weight of each hydrocarbon, and soil texture. Fine-textured soil was able to enhance sorption, being PAHs more photodegraded than in coarse-textured soil. According to the EC50 values reported by Microtox(®), a higher detoxification was observed in fine-textured soil, being correlated with the outcomes of the analytical study. Significant photodegradation rates were detected for a number of PAHs, namely phenanthrene, anthracene, benzo(a)pyrene, and indeno(123-cd)pyrene. Benzo(a)pyrene, commonly used as an indicator for PAH pollution, was completely removed after 7 days of light exposure. In addition to the PAH chemical analysis and the ecotoxicity tests, a hydrogen isotope analysis of benzo(a)pyrene was also carried out. The degradation of this specific compound was associated to a high enrichment in (2)H, obtaining a maximum δ(2)H isotopic shift of +232‰. This strong isotopic effect observed in benzo(a)pyrene suggests that compound-specific isotope analysis (CSIA) may be a powerful tool to monitor in situ degradation of PAHs. Moreover, hydrogen isotopes of benzo(a)pyrene evidenced a degradation process of unknown origin occurring in the darkness. Copyright © 2016 Elsevier Ltd. All rights reserved.

Classification of Global Urban Centers Using ASTER Data: Preliminary Results From the Urban Environmental Monitoring Program

NASA Astrophysics Data System (ADS)

Stefanov, W. L.; Stefanov, W. L.; Christensen, P. R.

2001-05-01

Land cover and land use changes associated with urbanization are important drivers of global ecologic and climatic change. Quantification and monitoring of these changes are part of the primary mission of the ASTER instrument, and comprise the fundamental research objective of the Urban Environmental Monitoring (UEM) Program. The UEM program will acquire day/night, visible through thermal infrared ASTER data twice per year for 100 global urban centers over the duration of the mission (6 years). Data are currently available for a number of these urban centers and allow for initial comparison of global city structure using spatial variance texture analysis of the 15 m/pixel visible to near infrared ASTER bands. Variance texture analysis highlights changes in pixel edge density as recorded by sharp transitions from bright to dark pixels. In human-dominated landscapes these brightness variations correlate well with urbanized vs. natural land cover and are useful for characterizing the geographic extent and internal structure of cities. Variance texture analysis was performed on twelve urban centers (Albuquerque, Baghdad, Baltimore, Chongqing, Istanbul, Johannesburg, Lisbon, Madrid, Phoenix, Puebla, Riyadh, Vancouver) for which cloud-free daytime ASTER data are available. Image transects through each urban center produce texture profiles that correspond to urban density. These profiles can be used to classify cities into centralized (ex. Baltimore), decentralized (ex. Phoenix), or intermediate (ex. Madrid) structural types. Image texture is one of the primary data inputs (with vegetation indices and visible to thermal infrared image spectra) to a knowledge-based land cover classifier currently under development for application to ASTER UEM data as it is acquired. Collaboration with local investigators is sought to both verify the accuracy of the knowledge-based system and to develop more sophisticated classification models.

Unsupervised segmentation of lungs from chest radiographs

NASA Astrophysics Data System (ADS)

Ghosh, Payel; Antani, Sameer K.; Long, L. Rodney; Thoma, George R.

2012-03-01

This paper describes our preliminary investigations for deriving and characterizing coarse-level textural regions present in the lung field on chest radiographs using unsupervised grow-cut (UGC), a cellular automaton based unsupervised segmentation technique. The segmentation has been performed on a publicly available data set of chest radiographs. The algorithm is useful for this application because it automatically converges to a natural segmentation of the image from random seed points using low-level image features such as pixel intensity values and texture features. Our goal is to develop a portable screening system for early detection of lung diseases for use in remote areas in developing countries. This involves developing automated algorithms for screening x-rays as normal/abnormal with a high degree of sensitivity, and identifying lung disease patterns on chest x-rays. Automatically deriving and quantitatively characterizing abnormal regions present in the lung field is the first step toward this goal. Therefore, region-based features such as geometrical and pixel-value measurements were derived from the segmented lung fields. In the future, feature selection and classification will be performed to identify pathological conditions such as pulmonary tuberculosis on chest radiographs. Shape-based features will also be incorporated to account for occlusions of the lung field and by other anatomical structures such as the heart and diaphragm.

Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms.

PubMed

Solomon, Justin; Ba, Alexandre; Bochud, François; Samei, Ehsan

2016-12-01

To use novel voxel-based 3D printed textured phantoms in order to compare low-contrast detectability between two reconstruction algorithms, FBP (filtered-backprojection) and SAFIRE (sinogram affirmed iterative reconstruction) and determine what impact background texture (i.e., anatomical noise) has on estimating the dose reduction potential of SAFIRE. Liver volumes were segmented from 23 abdominal CT cases. The volumes were characterized in terms of texture features from gray-level co-occurrence and run-length matrices. Using a 3D clustered lumpy background (CLB) model, a fitting technique based on a genetic optimization algorithm was used to find CLB textures that were reflective of the liver textures, accounting for CT system factors of spatial blurring and noise. With the modeled background texture as a guide, four cylindrical phantoms (Textures A-C and uniform, 165 mm in diameter, and 30 mm height) were designed, each containing 20 low-contrast spherical signals (6 mm diameter at nominal contrast levels of ∼3.2, 5.2, 7.2, 10, and 14 HU with four repeats per signal). The phantoms were voxelized and input into a commercial multimaterial 3D printer (Object Connex 350), with custom software for voxel-based printing (using principles of digital dithering). Images of the textured phantoms and a corresponding uniform phantom were acquired at six radiation dose levels (SOMATOM Flash, Siemens Healthcare) and observer model detection performance (detectability index of a multislice channelized Hotelling observer) was estimated for each condition (5 contrasts × 6 doses × 2 reconstructions × 4 backgrounds = 240 total conditions). A multivariate generalized regression analysis was performed (linear terms, no interactions, random error term, log link function) to assess whether dose, reconstruction algorithm, signal contrast, and background type have statistically significant effects on detectability. Also, fitted curves of detectability (averaged across contrast levels) as a function of dose were constructed for each reconstruction algorithm and background texture. FBP and SAFIRE were compared for each background type to determine the improvement in detectability at a given dose, and the reduced dose at which SAFIRE had equivalent performance compared to FBP at 100% dose. Detectability increased with increasing radiation dose (P = 2.7 × 10 -59 ) and contrast level (P = 2.2 × 10 -86 ) and was higher in the uniform phantom compared to the textured phantoms (P = 6.9 × 10 -51 ). Overall, SAFIRE had higher d' compared to FBP (P = 0.02). The estimated dose reduction potential of SAFIRE was found to be 8%, 10%, 27%, and 8% for Texture-A, Texture-B, Texture-C and uniform phantoms. In all background types, detectability was higher with SAFIRE compared to FBP. However, the relative improvement observed from SAFIRE was highly dependent on the complexity of the background texture. Iterative algorithms such as SAFIRE should be assessed in the most realistic context possible.

An improved high order texture features extraction method with application to pathological diagnosis of colon lesions for CT colonography

NASA Astrophysics Data System (ADS)

Song, Bowen; Zhang, Guopeng; Lu, Hongbing; Wang, Huafeng; Han, Fangfang; Zhu, Wei; Liang, Zhengrong

2014-03-01

Differentiation of colon lesions according to underlying pathology, e.g., neoplastic and non-neoplastic, is of fundamental importance for patient management. Image intensity based textural features have been recognized as a useful biomarker for the differentiation task. In this paper, we introduce high order texture features, beyond the intensity, such as gradient and curvature, for that task. Based on the Haralick texture analysis method, we introduce a virtual pathological method to explore the utility of texture features from high order differentiations, i.e., gradient and curvature, of the image intensity distribution. The texture features were validated on database consisting of 148 colon lesions, of which 35 are non-neoplastic lesions, using the random forest classifier and the merit of area under the curve (AUC) of the receiver operating characteristics. The results show that after applying the high order features, the AUC was improved from 0.8069 to 0.8544 in differentiating non-neoplastic lesion from neoplastic ones, e.g., hyperplastic polyps from tubular adenomas, tubulovillous adenomas and adenocarcinomas. The experimental results demonstrated that texture features from the higher order images can significantly improve the classification accuracy in pathological differentiation of colorectal lesions. The gain in differentiation capability shall increase the potential of computed tomography (CT) colonography for colorectal cancer screening by not only detecting polyps but also classifying them from optimal polyp management for the best outcome in personalized medicine.

Soil Bacteria And Fungi Respond On Different Spatial Scales To Invasion By The Legume Lespedeza Cuneata

DTIC Science & Technology

2011-06-03

Permutationalmultivariate analysis of variance ( PerMANOVA ; McArdle and Anderson, 2001) was used to test hypotheses regard- ing regions and invasion level...for the differences due to invasion level after removing any differences due to regions, soil texture, and habitat. The null distribution for PerMANOVA ...soil neigh- borhoods, PerMANOVA tests were carried out separately for each site. We did not use a stratified randomization scheme for these tests, under

Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition

NASA Astrophysics Data System (ADS)

Sultana, Maryam; Bhatti, Naeem; Javed, Sajid; Jung, Soon Ki

2017-09-01

Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges.

PubMed

Bashir, Usman; Siddique, Muhammad Musib; Mclean, Emma; Goh, Vicky; Cook, Gary J

2016-09-01

Texture analysis involves the mathematic processing of medical images to derive sets of numeric quantities that measure heterogeneity. Studies on lung cancer have shown that texture analysis may have a role in characterizing tumors and predicting patient outcome. This article outlines the mathematic basis of and the most recent literature on texture analysis in lung cancer imaging. We also describe the challenges facing the clinical implementation of texture analysis. Texture analysis of lung cancer images has been applied successfully to FDG PET and CT scans. Different texture parameters have been shown to be predictive of the nature of disease and of patient outcome. In general, it appears that more heterogeneous tumors on imaging tend to be more aggressive and to be associated with poorer outcomes and that tumor heterogeneity on imaging decreases with treatment. Despite these promising results, there is a large variation in the reported data and strengths of association.

Adaptive thresholding image series from fluorescence confocal scanning laser microscope using orientation intensity profiles

NASA Astrophysics Data System (ADS)

Feng, Judy J.; Ip, Horace H.; Cheng, Shuk H.

2004-05-01

Many grey-level thresholding methods based on histogram or other statistic information about the interest image such as maximum entropy and so on have been proposed in the past. However, most methods based on statistic analysis of the images concerned little about the characteristics of morphology of interest objects, which sometimes could provide very important indication which can help to find the optimum threshold, especially for those organisms which have special texture morphologies such as vasculature, neuro-network etc. in medical imaging. In this paper, we propose a novel method for thresholding the fluorescent vasculature image series recorded from Confocal Scanning Laser Microscope. After extracting the basic orientation of the slice of vessels inside a sub-region partitioned from the images, we analysis the intensity profiles perpendicular to the vessel orientation to get the reasonable initial threshold for each region. Then the threshold values of those regions near the interest one both in x-y and optical directions have been referenced to get the final result of thresholds of the region, which makes the whole stack of images look more continuous. The resulting images are characterized by suppressing both noise and non-interest tissues conglutinated to vessels, while improving the vessel connectivities and edge definitions. The value of the method for idealized thresholding the fluorescence images of biological objects is demonstrated by a comparison of the results of 3D vascular reconstruction.

Content-based cell pathology image retrieval by combining different features

NASA Astrophysics Data System (ADS)

Zhou, Guangquan; Jiang, Lu; Luo, Limin; Bao, Xudong; Shu, Huazhong

2004-04-01

Content Based Color Cell Pathology Image Retrieval is one of the newest computer image processing applications in medicine. Recently, some algorithms have been developed to achieve this goal. Because of the particularity of cell pathology images, the result of the image retrieval based on single characteristic is not satisfactory. A new method for pathology image retrieval by combining color, texture and morphologic features to search cell images is proposed. Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation and mathematics morphology. The features that include color, texture and morphologic features are extracted from single leukocyte to represent main attribute in the search query. The features are then normalized because the numerical value range and physical meaning of extracted features are different. Finally, the relevance feedback system is introduced. So that the system can automatically adjust the weights of different features and improve the results of retrieval system according to the feedback information. Retrieval results using the proposed method fit closely with human perception and are better than those obtained with the methods based on single feature.

Detection of lobular structures in normal breast tissue.

PubMed

Apou, Grégory; Schaadt, Nadine S; Naegel, Benoît; Forestier, Germain; Schönmeyer, Ralf; Feuerhake, Friedrich; Wemmert, Cédric; Grote, Anne

2016-07-01

Ongoing research into inflammatory conditions raises an increasing need to evaluate immune cells in histological sections in biologically relevant regions of interest (ROIs). Herein, we compare different approaches to automatically detect lobular structures in human normal breast tissue in digitized whole slide images (WSIs). This automation is required to perform objective and consistent quantitative studies on large data sets. In normal breast tissue from nine healthy patients immunohistochemically stained for different markers, we evaluated and compared three different image analysis methods to automatically detect lobular structures in WSIs: (1) a bottom-up approach using the cell-based data for subsequent tissue level classification, (2) a top-down method starting with texture classification at tissue level analysis of cell densities in specific ROIs, and (3) a direct texture classification using deep learning technology. All three methods result in comparable overall quality allowing automated detection of lobular structures with minor advantage in sensitivity (approach 3), specificity (approach 2), or processing time (approach 1). Combining the outputs of the approaches further improved the precision. Different approaches of automated ROI detection are feasible and should be selected according to the individual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quantitative and qualitative variation of fat in model vanilla custard desserts: effects on sensory properties and consumer acceptance.

PubMed

Tomaschunas, Maja; Köhn, Ehrhard; Bennwitz, Petra; Hinrichs, Jörg; Busch-Stockfisch, Mechthild

2013-06-01

The effects of variation in fat content (0.1% to 15.8%) and type of fat, using different types of milk, dairy cream, or vegetable fat cream, on sensory characteristics and consumer acceptance of starch-based vanilla model custards were studied. Descriptive analysis with trained panelists and consumer testing with untrained assessors were applied. Descriptive data were related to hedonic data using principal component analysis to determine drivers of liking and disliking. Results demonstrated an increasing effect of fat concerning visual and oral thickness, creamy flavor, and fat-related texture properties, as well as a decreasing effect concerning yellow color and surface shine. A lack of fat caused moderate intensities in pudding-like flavor attributes and an intensive jelly texture. Adding a vegetable fat cream led to lower intensities in attributes yellow color, cooked flavor, thick, and jelly texture, whereas intensities in vegetable fat flavor and fat-related texture properties increased. All consumers favored custards with medium fat contents, being high in pudding-like and vegetable fat flavor as well as in fat-related texture attributes. Nonfat custards were rejected due to jelly texture and moderate intensities in pudding-flavor attributes. High-fat samples were liked by some consumers, but their high intensities in thickness, white color, and creamy flavor also drove disliking for others. © 2013 Institute of Food Technologists®

Feature Selection for Classification of Polar Regions Using a Fuzzy Expert System

NASA Technical Reports Server (NTRS)

Penaloza, Mauel A.; Welch, Ronald M.

1996-01-01

Labeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the list, of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow / ice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce the features into a more optimal set. For each selection method,features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set features, not only does it produce the highest classification accuracy, but also it has the lowest computation requirements. A reduction of the set of features produced by the divergence method using the fuzzy expert system results in an overall classification accuracy of over 95 %. However, this increase of accuracy has a high computation cost.

Temporal evolution of soil moisture statistical fractal and controls by soil texture and regional groundwater flow

NASA Astrophysics Data System (ADS)

Ji, Xinye; Shen, Chaopeng; Riley, William J.

2015-12-01

Soil moisture statistical fractal is an important tool for downscaling remotely-sensed observations and has the potential to play a key role in multi-scale hydrologic modeling. The fractal was first introduced two decades ago, but relatively little is known regarding how its scaling exponents evolve in time in response to climatic forcings. Previous studies have neglected the process of moisture re-distribution due to regional groundwater flow. In this study we used a physically-based surface-subsurface processes model and numerical experiments to elucidate the patterns and controls of fractal temporal evolution in two U.S. Midwest basins. Groundwater flow was found to introduce large-scale spatial structure, thereby reducing the scaling exponents (τ), which has implications for the transferability of calibrated parameters to predict τ. However, the groundwater effects depend on complex interactions with other physical controls such as soil texture and land use. The fractal scaling exponents, while in general showing a seasonal mode that correlates with mean moisture content, display hysteresis after storm events that can be divided into three phases, consistent with literature findings: (a) wetting, (b) re-organizing, and (c) dry-down. Modeling experiments clearly show that the hysteresis is attributed to soil texture, whose "patchiness" is the primary contributing factor. We generalized phenomenological rules for the impacts of rainfall, soil texture, groundwater flow, and land use on τ evolution. Grid resolution has a mild influence on the results and there is a strong correlation between predictions of τ from different resolutions. Overall, our results suggest that groundwater flow should be given more consideration in studies of the soil moisture statistical fractal, especially in regions with a shallow water table.

Built-up Areas Extraction in High Resolution SAR Imagery based on the method of Multiple Feature Weighted Fusion

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, J. X.; Zhao, Z.; Ma, A. D.

2015-06-01

Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It's of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.

Northeast Artificial Intelligence Consortium Annual Report for 1987. Volume 4. Research in Automated Photointerpretation

DTIC Science & Technology

1989-03-01

KOWLEDGE INFERENCE IMAGE DAAAEENGINE DATABASE Automated Photointerpretation Testbed. 4.1.7 Fig. .1.1-2 An Initial Segmentation of an Image / zx...MRF) theory provide a powerful alternative texture model and have resulted in intensive research activity in MRF model- based texture analysis...interpretation process. 5. Additional, and perhaps more powerful , features have to be incorporated into the image segmentation procedure. 6. Object detection

WE-E-17A-02: Predictive Modeling of Outcome Following SABR for NSCLC Based On Radiomics of FDG-PET Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, R; Aguilera, T; Shultz, D

2014-06-15

Purpose: This study aims to develop predictive models of patient outcome by extracting advanced imaging features (i.e., Radiomics) from FDG-PET images. Methods: We acquired pre-treatment PET scans for 51 stage I NSCLC patients treated with SABR. We calculated 139 quantitative features from each patient PET image, including 5 morphological features, 8 statistical features, 27 texture features, and 100 features from the intensity-volume histogram. Based on the imaging features, we aim to distinguish between 2 risk groups of patients: those with regional failure or distant metastasis versus those without. We investigated 3 pattern classification algorithms: linear discriminant analysis (LDA), naive Bayesmore » (NB), and logistic regression (LR). To avoid the curse of dimensionality, we performed feature selection by first removing redundant features and then applying sequential forward selection using the wrapper approach. To evaluate the predictive performance, we performed 10-fold cross validation with 1000 random splits of the data and calculated the area under the ROC curve (AUC). Results: Feature selection identified 2 texture features (homogeneity and/or wavelet decompositions) for NB and LR, while for LDA SUVmax and one texture feature (correlation) were identified. All 3 classifiers achieved statistically significant improvements over conventional PET imaging metrics such as tumor volume (AUC = 0.668) and SUVmax (AUC = 0.737). Overall, NB achieved the best predictive performance (AUC = 0.806). This also compares favorably with MTV using the best threshold at an SUV of 11.6 (AUC = 0.746). At a sensitivity of 80%, NB achieved 69% specificity, while SUVmax and tumor volume only had 36% and 47% specificity. Conclusion: Through a systematic analysis of advanced PET imaging features, we are able to build models with improved predictive value over conventional imaging metrics. If validated in a large independent cohort, the proposed techniques could potentially aid in identifying patients who might benefit from adjuvant therapy.« less

The influence of physical factors on recognizing blood cells in the computer microscopy systems of acute leukemia diagnosis

NASA Astrophysics Data System (ADS)

Nikitaev, V. G.; Pronichev, A. N.; Polyakov, E. V.; Dmitrieva, V. V.; Tupitsyn, N. N.; Frenkel, M. A.; Mozhenkova, A. V.

2017-01-01

The work investigated the effect of the choice of color space component on blood cell detection based on the calculation of texture attributes of blood cells nuclei in bone marrow. The study identified the most informative color space and texture characteristics of blood cells, designed for components of these spaces. Significance ratio was introduced to assess the quality of features. We offered features that have enabled to divide lymphocytes from lymphoblasts. The selection of the features was based on the results of the data analysis.

Classification Features of US Images Liver Extracted with Co-occurrence Matrix Using the Nearest Neighbor Algorithm

NASA Astrophysics Data System (ADS)

Moldovanu, Simona; Bibicu, Dorin; Moraru, Luminita; Nicolae, Mariana Carmen

2011-12-01

Co-occurrence matrix has been applied successfully for echographic images characterization because it contains information about spatial distribution of grey-scale levels in an image. The paper deals with the analysis of pixels in selected regions of interest of an US image of the liver. The useful information obtained refers to texture features such as entropy, contrast, dissimilarity and correlation extract with co-occurrence matrix. The analyzed US images were grouped in two distinct sets: healthy liver and steatosis (or fatty) liver. These two sets of echographic images of the liver build a database that includes only histological confirmed cases: 10 images of healthy liver and 10 images of steatosis liver. The healthy subjects help to compute four textural indices and as well as control dataset. We chose to study these diseases because the steatosis is the abnormal retention of lipids in cells. The texture features are statistical measures and they can be used to characterize irregularity of tissues. The goal is to extract the information using the Nearest Neighbor classification algorithm. The K-NN algorithm is a powerful tool to classify features textures by means of grouping in a training set using healthy liver, on the one hand, and in a holdout set using the features textures of steatosis liver, on the other hand. The results could be used to quantify the texture information and will allow a clear detection between health and steatosis liver.

The Effect of Petrographic Characteristics on Engineering Properties of Conglomerates from Famenin Region, Northeast of Hamedan, Iran

NASA Astrophysics Data System (ADS)

Khanlari, G. R.; Heidari, M.; Noori, M.; Momeni, A.

2016-07-01

To assess relationship between engineering characteristics and petrographic features, conglomerates samples related to Qom formation from Famenin region in northeast of Hamedan province were studied. Samples were tested in laboratory to determine the uniaxial compressive strength, point load strength index, modulus of elasticity, porosity, dry and saturation densities. For determining petrographic features, textural and mineralogical parameters, thin sections of the samples were prepared and studied. The results show that the effect of textural characteristics on the engineering properties of conglomerates supposed to be more important than mineralogical composition. It also was concluded that the packing proximity, packing density, grain shape and mean grain size, cement and matrix frequency are as textural features that have a significant effect on the physical and mechanical properties of the studied conglomerates. In this study, predictive statistical relationships were developed to estimate the physical and mechanical properties of the rocks based on the results of petrographic features. Furthermore, multivariate linear regression was used in four different steps comprising various combinations of petrographical characteristics for each engineering parameters. Finally, the best equations with specific arrangement were suggested to estimate engineering properties of the Qom formation conglomerates.

Global Synthesis of Drought Effects on Food Legume Production

PubMed Central

Daryanto, Stefani; Wang, Lixin; Jacinthe, Pierre-André

2015-01-01

Food legume crops play important roles in conservation farming systems and contribute to food security in the developing world. However, in many regions of the world, their production has been adversely affected by drought. Although water scarcity is a severe abiotic constraint of legume crops productivity, it remains unclear how the effects of drought co-vary with legume species, soil texture, agroclimatic region, and drought timing. To address these uncertainties, we collected literature data between 1980 and 2014 that reported monoculture legume yield responses to drought under field conditions, and analyzed this data set using meta-analysis techniques. Our results showed that the amount of water reduction was positively related with yield reduction, but the extent of the impact varied with legume species and the phenological state during which drought occurred. Overall, lentil (Lens culinaris), groundnut (Arachis hypogaea), and pigeon pea (Cajanus cajan) were found to experience lower drought-induced yield reduction compared to legumes such as cowpea (Vigna unguiculata) and green gram (Vigna radiate). Yield reduction was generally greater when legumes experienced drought during their reproductive stage compared to during their vegetative stage. Legumes grown in soil with medium texture also exhibited greater yield reduction compared to those planted on soil of either coarse or fine texture. In contrast, regions and their associated climatic factors did not significantly affect legume yield reduction. In the face of changing climate, our study provides useful information for agricultural planning and research directions for development of drought-resistant legume species to improve adaptation and resilience of agricultural systems in the drought-prone regions of the world. PMID:26061704

The Allende multicompound chondrule (ACC)—Chondrule formation in a local super-dense region of the early solar system

NASA Astrophysics Data System (ADS)

Bischoff, Addi; Wurm, Gerhard; Chaussidon, Marc; Horstmann, Marian; Metzler, Knut; Weyrauch, Mona; Weinauer, Julia

2017-05-01

In Allende, a very complex compound chondrule (Allende compound chondrule; ACC) was found consisting of at least 16 subchondrules (14 siblings and 2 independents). Its overall texture can roughly be described as a barred olivine object (BO). The BO texture is similar in all siblings, but does not exist in the two independents, which appear as relatively compact olivine-rich units. Because of secondary alteration of pristine Allende components and the ACC in particular, only limited predictions can be made concerning the original compositions of the colliding melt droplets. Based on textural and mineralogical characteristics, the siblings must have been formed on a very short time scale in a dense, local environment. This is also supported by oxygen isotope systematics showing similar compositions for all 16 subchondrules. Furthermore, the ACC subchondrules are isotopically distinct from typical Allende chondrules, indicating formation in or reaction with a more 16O-poor reservoir. We modeled constraints on the particle density required at the ACC formation location, using textural, mineral-chemical, and isotopic observations on this multicompound chondrule to define melt droplet collision conditions. In this context, we discuss the possible relationship between the formation of complex chondrules and the formation of macrochondrules and cluster chondrites. While macrochondrules may have formed under similar or related conditions as complex chondrules, cluster chondrites certainly require different formation conditions. Cluster chondrites represent a mixture of viscously deformed, seemingly young chondrules of different chemical and textural types and a population of older chondrules. Concerning the formation of ACC calculations suggest the existence of very local, kilometer-sized, and super-dense chondrule-forming regions with extremely high solid-to-gas mass ratios of 1000 or more.

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples.

PubMed

Turkki, Riku; Linder, Nina; Kovanen, Panu E; Pellinen, Teijo; Lundin, Johan

2016-01-01

Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists. In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92-0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87-0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (κ = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (κ = 0.78). Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists' visual assessment.

Cellular automata rule characterization and classification using texture descriptors

NASA Astrophysics Data System (ADS)

Machicao, Jeaneth; Ribas, Lucas C.; Scabini, Leonardo F. S.; Bruno, Odermir M.

2018-05-01

The cellular automata (CA) spatio-temporal patterns have attracted the attention from many researchers since it can provide emergent behavior resulting from the dynamics of each individual cell. In this manuscript, we propose an approach of texture image analysis to characterize and classify CA rules. The proposed method converts the CA spatio-temporal patterns into a gray-scale image. The gray-scale is obtained by creating a binary number based on the 8-connected neighborhood of each dot of the CA spatio-temporal pattern. We demonstrate that this technique enhances the CA rule characterization and allow to use different texture image analysis algorithms. Thus, various texture descriptors were evaluated in a supervised training approach aiming to characterize the CA's global evolution. Our results show the efficiency of the proposed method for the classification of the elementary CA (ECAs), reaching a maximum of 99.57% of accuracy rate according to the Li-Packard scheme (6 classes) and 94.36% for the classification of the 88 rules scheme. Moreover, within the image analysis context, we found a better performance of the method by means of a transformation of the binary states to a gray-scale.

Constraints on texture zero and cofactor zero models for neutrino mass

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whisnant, K.; Liao, Jiajun; Marfatia, D.

2014-06-24

Imposing a texture or cofactor zero on the neutrino mass matrix reduces the number of independent parameters from nine to seven. Since five parameters have been measured, only two independent parameters would remain in such models. We find the allowed regions for single texture zero and single cofactor zero models. We also find strong similarities between single texture zero models with one mass hierarchy and single cofactor zero models with the opposite mass hierarchy. We show that this correspondence can be generalized to texture-zero and cofactor-zero models with the same homogeneous costraints on the elements and cofactors.

Understanding the effect of watershed characteristic on the runoff using SCS curve number

NASA Astrophysics Data System (ADS)

Damayanti, Frieta; Schneider, Karl

2015-04-01

Runoff modeling is a key component in watershed management. The temporal course and amount of runoff is a complex function of a multitude of parameters such as climate, soil, topography, land use, and water management. Against the background of the current rapid environmental change, which is due to both i) man-made changes (e.g. urban development, land use change, water management) as well as ii) changes in the natural systems (e.g. climate change), understanding and predicting the impacts of these changes upon the runoff is very important and affects the wellbeing of many people living in the watershed. A main tool for predictions is hydrologic models. Particularly process based models are the method of choice to assess the impact of land use and climate change. However, many regions which experience large changes in the watersheds can be described as rather data poor, which limits the applicability of such models. This is particularly also true for the Telomoyo Watershed (545 km2) which is located in southern part of Central Java province. The average annual rainfall of the study area reaches 2971 mm. Irrigated paddy field are the dominating land use (35%), followed by built-up area and dry land agriculture. The only available soil map is the FAO soil digital map of the world, which provides rather general soil information. A field survey accompanied by a lab analysis 65 soil samples of was carried out to provide more detailed soil texture information. The soil texture map is a key input in the SCS method to define hydrological soil groups. In the frame of our study on 'Integrated Analysis on Flood Risk of Telomoyo Watershed in Response to the Climate and Land Use Change' funded by the German Academic Exchange service (DAAD) we analyzed the sensitivity of the modeled runoff upon the choice of the method to estimate the CN values using the SCS-CN method. The goal of this study is to analyze the impact of different data sources on the curve numbers and the estimated runoff. CN values were estimated using the field measurements of soil textures for different combinations of land use and topography. To transfer the local soil texture measurements to the watershed domain a statistical analysis using the frequency distribution of the measured soil textures is applied and used to derive the effective CN value for a given land use, topography and soil texture combination. Since the curve numbers change as a function of parameter combinations, the effect of different methods to estimate the curve number upon the runoff is analyzed and compared to the straight forward method of using the data from the FAO soil map.

Lithological and Petrographic Analyses of Carbonates and Sandstones From the Southern Gulf of Mexico

NASA Astrophysics Data System (ADS)

Garcia-Avendaño, A.; Urrutia-Fucugauchi, J.

2012-04-01

We present results of sedimentological and petrological studies of drill cores from the Bay of Campeche in the southern Gulf of Mexico. Based on reports on drill cores obtained from oil exploratory wells in the Cantarell Complex located 80 kilometres offshore in the Bay of Campeche and studies related to regional geology composite simplified stratigraphic columns for offshore Campeche region have been constructed up to depths of approximately 5000 m. The stratigraphic column is formed by a thick sediment sequence of Middle Jurassic age (evaporites, Callovian), Late Jurassic (terrigenous, calcareous clays and calcareous layers), Lower Cretaceous (carbonates), Upper Cretaceous-Paleogene (calcareous breccias), Paleogene-Neogene (terrigenous-carbonates intercalations) and Quaternary (terrigenous). The core samples studied come from wells in the Sihil and Akal fields in Cantarell. Analysis of reports on lithological descriptions indicates that these wells sample dolomitized sedimentary breccias from the Upper Cretaceous-Paleocene and fine-grained sandstones from the Late Jurassic Tithonian, respectively. Based on results of petrographic studies, the texture, cementing material and porosity of the units have been documented. The thin sections for carbonates were classified based on their texture according to Dunham (1962) for carbonate rocks, classified according to their components using the ternary diagrams of Folk (1974). Percentages refer to the data presented in tables, which were obtained by point-counting technique (with a total 250). Photomicrographs of scanning electron microscope (SEM) provide magnification for easy documentation of crystalline arrangements and description of micro-porous for different types of carbonates such as dolomite, in addition to the morphology of authigenic clays. Results of these studies and previous works in the area permit characterization of diagenetic processes of the carbonate sediments in the Campeche Bay, and provide information related to oil maturation, storage and potential flow in the Cantarell reservoirs.

RIS4E at Kilauea's December 1974 Flow: Lava Flow Texture LiDAR Signatures

NASA Astrophysics Data System (ADS)

Whelley, P.; Garry, W. B.; Scheidt, S. P.; Bleacher, J. E.; Hamilton, C.

2015-12-01

High-resolution point clouds and digital terrain models (DTMs) are used to investigate lava textures on the Big Island of Hawaii. Lava texture (e.g., ´áā and pāhoehoe) depends significantly on eruption conditions, and it is therefore instructive, if accurately determined. In places where field investigations are prohibitive (e.g., on other planets and remote regions of Earth) lava texture must be assessed from remote sensing data. A reliable method for doing so remains elusive. The December 1974 flow from Kilauea, in the Kau desert, presents an excellent field site to develop techniques for identifying lava texture. The eruption is young and the textures are well preserved. We present results comparing properties of lava textures observed in Terrestrial Laser Scanning (TLS) data. The authors collected the TLS data during May 2014 and June 2015 field seasons. Scans are a quantitative representation of what a geologist, or robotic system, sees "on the ground" and provides "ground truth" for airborne or orbital remote sensing analysis by enabling key parameters of lava morphology to be quantified. While individual scans have a heterogeneous point density, multiple scans are merged such that sub-cm lava textures can be quantified. Results indicate that TLS-derived surface roughness (i.e., de-trended RMS roughness) is useful for differentiating lava textures and assists volcanologic interpretations. As many lava types are quite rough, it is not simply roughness that is the most advantageous parameter for differentiating lava textures; rather co-occurrence patterns in surface roughness are used. Gradually forming textures (e.g., pāhoehoe) are elevated in statistics that measure smoothness (e.g., homogeneity) while lava with disrupted crusts (e.g., slabby and platy flow) have more random distributions of roughness (i.e., high entropy). A similar technique will be used to analyze high-resolution DTMs of martian lava flows using High Resolution Imaging Science Experiment DTMs. This work will lead to faster and more reliable volcanic mapping efforts for planetary exploration as well as terrestrial geohazards.

Comparison of Texture Features Used for Classification of Life Stages of Malaria Parasite.

PubMed

Bairagi, Vinayak K; Charpe, Kshipra C

2016-01-01

Malaria is a vector borne disease widely occurring at equatorial region. Even after decades of campaigning of malaria control, still today it is high mortality causing disease due to improper and late diagnosis. To prevent number of people getting affected by malaria, the diagnosis should be in early stage and accurate. This paper presents an automatic method for diagnosis of malaria parasite in the blood images. Image processing techniques are used for diagnosis of malaria parasite and to detect their stages. The diagnosis of parasite stages is done using features like statistical features and textural features of malaria parasite in blood images. This paper gives a comparison of the textural based features individually used and used in group together. The comparison is made by considering the accuracy, sensitivity, and specificity of the features for the same images in database.

Dielectric and acoustical high frequency characterisation of PZT thin films

NASA Astrophysics Data System (ADS)

Conde, Janine; Muralt, Paul

2010-02-01

Pb(Zr, Ti)O3 (PZT) is an interesting material for bulk acoustic wave resonator applications due to its high electromechanical coupling constant, which would enable fabrication of large bandwidth frequency filters. The major challenge of the PZT solid solution system is to overcome mechanical losses generally observed in PZT ceramics. To increase the understanding of these losses in textured thin films, thin film bulk acoustic resonators (TFBAR's) based on PZT thin films with compositions either in the tetragonal region or at the morphotropic phase boundary and (111) or {100} textures were fabricated and studied up to 2 GHz. The dielectric and elastic materials coefficients were extracted from impedance measurements at the resonance frequency. The dispersion of the dielectric constant was obtained from impedance measurements up to 2 GHz. The films with varying compositions, textures and deposition methods (sol-gel or sputtering) were compared in terms of dielectric and acoustical properties.

Application of texture analysis method for classification of benign and malignant thyroid nodules in ultrasound images.

PubMed

Abbasian Ardakani, Ali; Gharbali, Akbar; Mohammadi, Afshin

2015-01-01

The aim of this study was to evaluate computer aided diagnosis (CAD) system with texture analysis (TA) to improve radiologists' accuracy in identification of thyroid nodules as malignant or benign. A total of 70 cases (26 benign and 44 malignant) were analyzed in this study. We extracted up to 270 statistical texture features as a descriptor for each selected region of interests (ROIs) in three normalization schemes (default, 3s and 1%-99%). Then features by the lowest probability of classification error and average correlation coefficients (POE+ACC), and Fisher coefficient (Fisher) eliminated to 10 best and most effective features. These features were analyzed under standard and nonstandard states. For TA of the thyroid nodules, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA) were applied. First Nearest-Neighbour (1-NN) classifier was performed for the features resulting from PCA and LDA. NDA features were classified by artificial neural network (A-NN). Receiver operating characteristic (ROC) curve analysis was used for examining the performance of TA methods. The best results were driven in 1-99% normalization with features extracted by POE+ACC algorithm and analyzed by NDA with the area under the ROC curve ( Az) of 0.9722 which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Our results indicate that TA is a reliable method, can provide useful information help radiologist in detection and classification of benign and malignant thyroid nodules.

Diagnostic and prognostic value of amyloid PET textural and shape features: comparison with classical semi-quantitative rating in 760 patients from the ADNI-2 database.

PubMed

Ben Bouallègue, Fayçal; Vauchot, Fabien; Mariano-Goulart, Denis; Payoux, Pierre

2018-02-09

We evaluated the performance of amyloid PET textural and shape features in discriminating normal and Alzheimer's disease (AD) subjects, and in predicting conversion to AD in subjects with mild cognitive impairment (MCI) or significant memory concern (SMC). Subjects from the Alzheimer's Disease Neuroimaging Initiative with available baseline 18 F-florbetapir and T1-MRI scans were included. The cross-sectional cohort consisted of 181 controls and 148 AD subjects. The longitudinal cohort consisted of 431 SMC/MCI subjects, 85 of whom converted to AD during follow-up. PET images were normalized to MNI space and post-processed using in-house software. Relative retention indices (SUVr) were computed with respect to pontine, cerebellar, and composite reference regions. Several textural and shape features were extracted then combined using a support vector machine (SVM) to build a predictive model of AD conversion. Diagnostic and prognostic performance was evaluated using ROC analysis and survival analysis with the Cox proportional hazard model. The three SUVr and all the tested features effectively discriminated AD subjects in cross-sectional analysis (all p < 0.001). In longitudinal analysis, the variables with the highest prognostic value were composite SUVr (AUC 0.86; accuracy 81%), skewness (0.87; 83%), local minima (0.85; 79%), Geary's index (0.86; 81%), gradient norm maximal argument (0.83; 82%), and the SVM model (0.91; 86%). The adjusted hazard ratio for AD conversion was 5.5 for the SVM model, compared with 4.0, 2.6, and 3.8 for cerebellar, pontine and composite SUVr (all p < 0.001), indicating that appropriate amyloid textural and shape features predict conversion to AD with at least as good accuracy as classical SUVr.

Effect of the wooden breast condition on shear force and texture profile analysis of raw and cooked broiler pectoralis major

USDA-ARS?s Scientific Manuscript database

The objective was to characterize texture properties of raw and cooked broiler fillets (Pectoralis major) with the wooden breast condition (WBC) using the instrumental texture techniques of Meullenet-Owens Razor Shear (MORS) and Texture Profile Analysis (TPA). Deboned (3 h post-mortem) broiler fille...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ranjanomennahary, P.; Ghalila, S. Sevestre; Malouche, D

Purpose: Hip fracture is a serious health problem and textural methods are being developed to assess bone quality. The authors aimed to perform textural analysis at femur on high-resolution digital radiographs compared to three-dimensional (3D) microarchitecture comparatively to bone mineral density. Methods: Sixteen cadaveric femurs were imaged with an x-ray device using a C-MOS sensor. One 17 mm square region of interest (ROI) was selected in the femoral head (FH) and one in the great trochanter (GT). Two-dimensional (2D) textural features from the co-occurrence matrices were extracted. Site-matched measurements of bone mineral density were performed. Inside each ROI, a 16more » mm diameter core was extracted. Apparent density (D{sub app}) and bone volume proportion (BV/TV{sub Arch}) were measured from a defatted bone core using Archimedes' principle. Microcomputed tomography images of the entire length of the core were obtained (Skyscan 1072) at 19.8 {mu}m of resolution and usual 3D morphometric parameters were computed on the binary volume after calibration from BV/TV{sub Arch}. Then, bone surface/bone volume, trabecular thickness, trabecular separation, and trabecular number were obtained by direct methods without model assumption and the structure model index was calculated. Results: In univariate analysis, the correlation coefficients between 2D textural features and 3D morphological parameters reached 0.83 at the FH and 0.79 at the GT. In multivariate canonical correlation analysis, coefficients of the first component reached 0.95 at the FH and 0.88 at the GT. Conclusions: Digital radiographs, widely available and economically viable, are an alternative method for evaluating bone microarchitectural structure.« less

Optical devices featuring textured semiconductor layers

DOEpatents

Moustakas, Theodore D [Dover, MA; Cabalu, Jasper S [Cary, NC

2011-10-11

A semiconductor sensor, solar cell or emitter, or a precursor therefor, has a substrate and one or more textured semiconductor layers deposited onto the substrate. The textured layers enhance light extraction or absorption. Texturing in the region of multiple quantum wells greatly enhances internal quantum efficiency if the semiconductor is polar and the quantum wells are grown along the polar direction. Electroluminescence of LEDs of the invention is dichromatic, and results in variable color LEDs, including white LEDs, without the use of phosphor.

Optical devices featuring textured semiconductor layers

DOEpatents

Moustakas, Theodore D [Dover, MA; Cabalu, Jasper S [Cary, NC

2012-08-07

A semiconductor sensor, solar cell or emitter, or a precursor therefor, has a substrate and one or more textured semiconductor layers deposited onto the substrate. The textured layers enhance light extraction or absorption. Texturing in the region of multiple quantum wells greatly enhances internal quantum efficiency if the semiconductor is polar and the quantum wells are grown along the polar direction. Electroluminescence of LEDs of the invention is dichromatic, and results in variable color LEDs, including white LEDs, without the use of phosphor.

Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

NASA Astrophysics Data System (ADS)

Tan, Maxine; Pu, Jiantao; Zheng, Bin

2014-08-01

The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were given to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC = 0.793  ±  0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography.

Hyperspectral imaging with wavelet transform for classification of colon tissue biopsy samples

NASA Astrophysics Data System (ADS)

Masood, Khalid

2008-08-01

Automatic classification of medical images is a part of our computerised medical imaging programme to support the pathologists in their diagnosis. Hyperspectral data has found its applications in medical imagery. Its usage is increasing significantly in biopsy analysis of medical images. In this paper, we present a histopathological analysis for the classification of colon biopsy samples into benign and malignant classes. The proposed study is based on comparison between 3D spectral/spatial analysis and 2D spatial analysis. Wavelet textural features in the wavelet domain are used in both these approaches for classification of colon biopsy samples. Experimental results indicate that the incorporation of wavelet textural features using a support vector machine, in 2D spatial analysis, achieve best classification accuracy.

Detection of damaged areas caused by the oil extraction in a steppe region using winter landsat imagery

NASA Astrophysics Data System (ADS)

Mjachina, Ksenya; Hu, Zhiyong; Chibilyev, Alexander

2018-01-01

Oil production in a steppe region disturbs the landscape and damages the steppe ecosystem. The objective of this research was to detect areas damaged by oil production in an oil field within the Russian Volga-Ural steppe region using winter Landsat imagery. We developed a practicable and effective approach using winter snow season multispectral Landsat satellite imagery. To this end, we applied seven algorithms of spectral or texture-based transformation: K-means, maximum likelihood estimation, topsoil grain size index, soil brightness, normalized differential snow index, tasselled cap, and co-occurrence measures. The co-occurrence texture measure variance shows the optimal result of identifying damaged areas. The unique feature of our method is that it can differentiate damaged areas from the bare soil of cropland within a cold steppe region where the area damaged by oil production is mixed with bare (fallow) croplands that have a polygonal shape similar to well pads. Such similarities can lead to confusion in object-based classification. Using the co-occurrence measures, we found that from 1988 to 2015, damaged area is nearly three times as big in the peak period of the oil field development (2001 and 2009) as in 1988. Landscape fragmentation also peaked in 2001 and 2009. Our approach for this project is useful and cost effective regular monitoring of damages from oil production for both the Volga-Ural steppe region and other cold steppe regions.

Efficient optical analysis of surface texture combinations for silicon solar cells

NASA Astrophysics Data System (ADS)

Tucher, Nico; Eisenlohr, Johannes; Kiefel, Peter; Gebrewold, Habtamu; Höhn, Oliver; Hauser, Hubert; Müller, Claas; Goldschmidt, Jan Christoph; Bläsi, Benedikt

2016-04-01

Surface textures can significantly improve anti-reflective and light trapping properties of silicon solar cells. Combining standard pyramidal front side textures with scattering or diffractive rear side textures has the potential to further increase the light path length inside the silicon and thereby increase the solar cell efficiency. In this work we introduce the OPTOS (Optical Properties of Textured Optical Sheets) simulation formalism and apply it to the modelling of silicon solar cells with different surface textures at front and rear side. OPTOS is a matrix-based method that allows for the computationally-efficient calculation of non-coherent light propagation within textured solar cells, featuring multiple textures that may operate in different optical regimes. After calculating redistribution matrices for each individual surface texture with the most appropriate technique, optical properties like angle dependent reflectance, transmittance or absorptance can be determined via matrix multiplications. Using OPTOS, we demonstrate for example that the integration of a diffractive grating at the rear side of solar cells with random pyramids at the front results in an absorptance gain that corresponds to a photocurrent density enhancement of 0.73 mA/cm2 for a 250 μm thick cell. The re-usability of matrices enables the investigation of different solar cell thicknesses within minutes. For thicknesses down to 50 μm the simulated gain increases up to 1.22 mA/cm2. The OPTOS formalism is furthermore not restricted with respect to the number of textured interfaces. By combining two or more textured sheets to effective interfaces, it is possible to optically model a complete photovoltaic module including EVA and potentially textured glass layers with one calculation tool.