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.

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.

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.

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.

Visualization and Quantitative Analysis of Crack-Tip Plastic Zone in Pure Nickel

NASA Astrophysics Data System (ADS)

Kelton, Randall; Sola, Jalal Fathi; Meletis, Efstathios I.; Huang, Haiying

2018-05-01

Changes in surface morphology have long been thought to be associated with crack propagation in metallic materials. We have studied areal surface texture changes around crack tips in an attempt to understand the correlations between surface texture changes and crack growth behavior. Detailed profiling of the fatigue sample surface was carried out at short fatigue intervals. An image processing algorithm was developed to calculate the surface texture changes. Quantitative analysis of the crack-tip plastic zone, crack-arrested sites near triple points, and large surface texture changes associated with crack release from arrested locations was carried out. The results indicate that surface texture imaging enables visualization of the development of plastic deformation around a crack tip. Quantitative analysis of the surface texture changes reveals the effects of local microstructures on the crack growth behavior.

Quantitative evaluation methods of skin condition based on texture feature parameters.

PubMed

Pang, Hui; Chen, Tianhua; Wang, Xiaoyi; Chang, Zhineng; Shao, Siqi; Zhao, Jing

2017-03-01

In order to quantitatively evaluate the improvement of the skin condition after using skin care products and beauty, a quantitative evaluation method for skin surface state and texture is presented, which is convenient, fast and non-destructive. Human skin images were collected by image sensors. Firstly, the median filter of the 3 × 3 window is used and then the location of the hairy pixels on the skin is accurately detected according to the gray mean value and color information. The bilinear interpolation is used to modify the gray value of the hairy pixels in order to eliminate the negative effect of noise and tiny hairs on the texture. After the above pretreatment, the gray level co-occurrence matrix (GLCM) is calculated. On the basis of this, the four characteristic parameters, including the second moment, contrast, entropy and correlation, and their mean value are calculated at 45 ° intervals. The quantitative evaluation model of skin texture based on GLCM is established, which can calculate the comprehensive parameters of skin condition. Experiments show that using this method evaluates the skin condition, both based on biochemical indicators of skin evaluation methods in line, but also fully consistent with the human visual experience. This method overcomes the shortcomings of the biochemical evaluation method of skin damage and long waiting time, also the subjectivity and fuzziness of the visual evaluation, which achieves the non-destructive, rapid and quantitative evaluation of skin condition. It can be used for health assessment or classification of the skin condition, also can quantitatively evaluate the subtle improvement of skin condition after using skin care products or stage beauty.

Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery

PubMed Central

Chaddad, Ahmad; Desrosiers, Christian; Bouridane, Ahmed; Toews, Matthew; Hassan, Lama; Tanougast, Camel

2016-01-01

Purpose This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. Materials and Methods In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models. Results Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%. Conclusions These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images. PMID:26901134

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

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.

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.

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

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.

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

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

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.

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

Objective and quantitative definitions of modified food textures based on sensory and rheological methodology.

PubMed

Wendin, Karin; Ekman, Susanne; Bülow, Margareta; Ekberg, Olle; Johansson, Daniel; Rothenberg, Elisabet; Stading, Mats

2010-06-28

Patients who suffer from chewing and swallowing disorders, i.e. dysphagia, may have difficulties ingesting normal food and liquids. In these patients a texture modified diet may enable that the patient maintain adequate nutrition. However, there is no generally accepted definition of 'texture' that includes measurements describing different food textures. Objectively define and quantify categories of texture-modified food by conducting rheological measurements and sensory analyses. A further objective was to facilitate the communication and recommendations of appropriate food textures for patients with dysphagia. About 15 food samples varying in texture qualities were characterized by descriptive sensory and rheological measurements. Soups were perceived as homogenous; thickened soups were perceived as being easier to swallow, more melting and creamy compared with soups without thickener. Viscosity differed between the two types of soups. Texture descriptors for pâtés were characterized by high chewing resistance, firmness, and having larger particles compared with timbales and jellied products. Jellied products were perceived as wobbly, creamy, and easier to swallow. Concerning the rheological measurements, all solid products were more elastic than viscous (G'>G''), belonging to different G' intervals: jellied products (low G') and timbales together with pâtés (higher G'). By combining sensory and rheological measurements, a system of objective, quantitative, and well-defined food textures was developed that characterizes the different texture categories.

Quantitative fabric analysis of eclogite facies mylonites: texture and microtomography

NASA Astrophysics Data System (ADS)

Gomez Barreiro, J.; Voltolini, M.; Martinez-Catalan, J. R.; Benitez-Perez, J. M.; Diez-fernandez, R.; Wenk, H. R.; Vogel, S. C.; Mancini, L.

2014-12-01

Understanding the flow of rock deformed under eclogite facies conditions is crucial to constraint the dynamics of a subducting slab. Prograde metamorphism during burial in a subduction zone proceeds across several lithologies, resulting in heterogeneous eclogitization and potentially different processes. In order to explore the expression of such a variety in terms of a deformative fabric, we have analyzed texture and shape fabric of eclogites and eclogitic orthogneisses from the Malpica-Tui unit (NW Spain). We explore the same rock volumes with TOF-neutron diffraction (HIPPO @ LANSCE) and synchrotron microtomography (SYRMEP @Elettra). Orientation distribution functions were extracted after Rietveld refinement in MAUD and morphometric data (size, aspect ratio, orientation) were obtained after image processing with FIJI, Blob3D and MATLAB. Shape fabric reflects the macroscopic foliation and lineation and correlates with texture. Garnet fabric is particularly important because of the rheological implications of its mechanical behavior. Garnet shows little elongation in both samples, and texture is significant, what probably points to a relatively dry deformative environment, with diffusion-assisted dislocation. This eclogites could represent a rigidification stage in the subduction channel preserved during the exhumation at high-P and high-T documented in the Malpica-Tui unit during the Variscan orogeny.

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.

Quantitative comparison of noise texture across CT scanners from different manufacturers.

PubMed

Solomon, Justin B; Christianson, Olav; Samei, Ehsan

2012-10-01

To quantitatively compare noise texture across computed tomography (CT) scanners from different manufacturers using the noise power spectrum (NPS). The American College of Radiology CT accreditation phantom (Gammex 464, Gammex, Inc., Middleton, WI) was imaged on two scanners: Discovery CT 750HD (GE Healthcare, Waukesha, WI), and SOMATOM Definition Flash (Siemens Healthcare, Germany), using a consistent acquisition protocol (120 kVp, 0.625∕0.6 mm slice thickness, 250 mAs, and 22 cm field of view). Images were reconstructed using filtered backprojection and a wide selection of reconstruction kernels. For each image set, the 2D NPS were estimated from the uniform section of the phantom. The 2D spectra were normalized by their integral value, radially averaged, and filtered by the human visual response function. A systematic kernel-by-kernel comparison across manufacturers was performed by computing the root mean square difference (RMSD) and the peak frequency difference (PFD) between the NPS from different kernels. GE and Siemens kernels were compared and kernel pairs that minimized the RMSD and |PFD| were identified. The RMSD (|PFD|) values between the NPS of GE and Siemens kernels varied from 0.01 mm(2) (0.002 mm(-1)) to 0.29 mm(2) (0.74 mm(-1)). The GE kernels "Soft," "Standard," "Chest," and "Lung" closely matched the Siemens kernels "B35f," "B43f," "B41f," and "B80f" (RMSD < 0.05 mm(2), |PFD| < 0.02 mm(-1), respectively). The GE "Bone," "Bone+," and "Edge" kernels all matched most closely with Siemens "B75f" kernel but with sizeable RMSD and |PFD| values up to 0.18 mm(2) and 0.41 mm(-1), respectively. These sizeable RMSD and |PFD| values corresponded to visually perceivable differences in the noise texture of the images. It is possible to use the NPS to quantitatively compare noise texture across CT systems. The degree to which similar texture across scanners could be achieved varies and is limited by the kernels available on each scanner.

Texture of Frozen Food

NASA Astrophysics Data System (ADS)

Wani, Kohmei

Quantitative determination of textural quality of frozen food due to freezing and storage conditions is complicated,since the texture is consisted of multi-dimensiona1 factors. The author reviewed the importance of texture in food quality and the factors which is proposed by a priori estimation. New classification of expression words of textural properties by subjective evaluation and an application of four elements mechanical model for analysis of physical characteristics was studied on frozen meat patties. Combination of freezing-thawing condition on the subjective properties and physiochemical characteristics of beef lean meat and hamachi fish (Yellow-tail) meat was studied. Change of the plasticity and the deformability of these samples differed by freezing-thawing rate and cooking procedure. Also optimum freezing-thawing condition was differed from specimens.

Objective and quantitative definitions of modified food textures based on sensory and rheological methodology

PubMed Central

Wendin, Karin; Ekman, Susanne; Bülow, Margareta; Ekberg, Olle; Johansson, Daniel; Rothenberg, Elisabet; Stading, Mats

2010-01-01

Introduction Patients who suffer from chewing and swallowing disorders, i.e. dysphagia, may have difficulties ingesting normal food and liquids. In these patients a texture modified diet may enable that the patient maintain adequate nutrition. However, there is no generally accepted definition of ‘texture’ that includes measurements describing different food textures. Objective Objectively define and quantify categories of texture-modified food by conducting rheological measurements and sensory analyses. A further objective was to facilitate the communication and recommendations of appropriate food textures for patients with dysphagia. Design About 15 food samples varying in texture qualities were characterized by descriptive sensory and rheological measurements. Results Soups were perceived as homogenous; thickened soups were perceived as being easier to swallow, more melting and creamy compared with soups without thickener. Viscosity differed between the two types of soups. Texture descriptors for pâtés were characterized by high chewing resistance, firmness, and having larger particles compared with timbales and jellied products. Jellied products were perceived as wobbly, creamy, and easier to swallow. Concerning the rheological measurements, all solid products were more elastic than viscous (G′>G″), belonging to different G′ intervals: jellied products (low G′) and timbales together with pâtés (higher G′). Conclusion By combining sensory and rheological measurements, a system of objective, quantitative, and well-defined food textures was developed that characterizes the different texture categories. PMID:20592965

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.

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

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.

Quantitative comparison of noise texture across CT scanners from different manufacturers

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

Solomon, Justin B.; Christianson, Olav; Samei, Ehsan

2012-10-15

Purpose: To quantitatively compare noise texture across computed tomography (CT) scanners from different manufacturers using the noise power spectrum (NPS). Methods: The American College of Radiology CT accreditation phantom (Gammex 464, Gammex, Inc., Middleton, WI) was imaged on two scanners: Discovery CT 750HD (GE Healthcare, Waukesha, WI), and SOMATOM Definition Flash (Siemens Healthcare, Germany), using a consistent acquisition protocol (120 kVp, 0.625/0.6 mm slice thickness, 250 mAs, and 22 cm field of view). Images were reconstructed using filtered backprojection and a wide selection of reconstruction kernels. For each image set, the 2D NPS were estimated from the uniform section ofmore » the phantom. The 2D spectra were normalized by their integral value, radially averaged, and filtered by the human visual response function. A systematic kernel-by-kernel comparison across manufacturers was performed by computing the root mean square difference (RMSD) and the peak frequency difference (PFD) between the NPS from different kernels. GE and Siemens kernels were compared and kernel pairs that minimized the RMSD and |PFD| were identified. Results: The RMSD (|PFD|) values between the NPS of GE and Siemens kernels varied from 0.01 mm{sup 2} (0.002 mm{sup -1}) to 0.29 mm{sup 2} (0.74 mm{sup -1}). The GE kernels 'Soft,''Standard,''Chest,' and 'Lung' closely matched the Siemens kernels 'B35f,''B43f,''B41f,' and 'B80f' (RMSD < 0.05 mm{sup 2}, |PFD| < 0.02 mm{sup -1}, respectively). The GE 'Bone,''Bone+,' and 'Edge' kernels all matched most closely with Siemens 'B75f' kernel but with sizeable RMSD and |PFD| values up to 0.18 mm{sup 2} and 0.41 mm{sup -1}, respectively. These sizeable RMSD and |PFD| values corresponded to visually perceivable differences in the noise texture of the images. Conclusions: It is possible to use the NPS to quantitatively compare noise texture across CT systems. The degree to which similar texture across scanners could be achieved varies and

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.

Color and texture associations in voice-induced synesthesia

PubMed Central

Moos, Anja; Simmons, David; Simner, Julia; Smith, Rachel

2013-01-01

Voice-induced synesthesia, a form of synesthesia in which synesthetic perceptions are induced by the sounds of people's voices, appears to be relatively rare and has not been systematically studied. In this study we investigated the synesthetic color and visual texture perceptions experienced in response to different types of “voice quality” (e.g., nasal, whisper, falsetto). Experiences of three different groups—self-reported voice synesthetes, phoneticians, and controls—were compared using both qualitative and quantitative analysis in a study conducted online. Whilst, in the qualitative analysis, synesthetes used more color and texture terms to describe voices than either phoneticians or controls, only weak differences, and many similarities, between groups were found in the quantitative analysis. Notable consistent results between groups were the matching of higher speech fundamental frequencies with lighter and redder colors, the matching of “whispery” voices with smoke-like textures, and the matching of “harsh” and “creaky” voices with textures resembling dry cracked soil. These data are discussed in the light of current thinking about definitions and categorizations of synesthesia, especially in cases where individuals apparently have a range of different synesthetic inducers. PMID:24032023

Texture analysis at neutron diffractometer STRESS-SPEC

NASA Astrophysics Data System (ADS)

Brokmeier, H.-G.; Gan, W. M.; Randau, C.; Völler, M.; Rebelo-Kornmeier, J.; Hofmann, M.

2011-06-01

In response to the development of new materials and the application of materials and components in advanced technologies, non-destructive measurement methods of textures and residual stresses have gained worldwide significance in recent years. The materials science neutron diffractometer STRESS-SPEC at FRM II (Garching, Germany) is designed to be applied equally to texture and residual stress analyses by virtue of its very flexible configuration. Due to the high penetration capabilities of neutrons and the high neutron flux of STRESS-SPEC it allows a combined analysis of global texture, local texture, strain pole figure and FWHM pole figure in a wide variety of materials including metals, alloys, composites, ceramics and geological materials. Especially, the analysis of texture gradients in bulk materials using neutron diffraction has advantages over laboratory X-rays and EBSD for many scientific cases. Moreover, neutron diffraction is favourable for coarse-grained materials, where bulk information averaged over texture inhomogeneities is needed, and also stands out due to easy sample preparation. In future, the newly developed robot system for STRESS-SPEC will allow much more flexibility than an Eulerian cradle as on standard instruments. Five recent measurements are shown to demonstrate the wide range of possible texture applications at STRESS-SPEC diffractometer.

Quantitative analysis on collagen of dermatofibrosarcoma protuberans skin by second harmonic generation microscopy.

PubMed

Wu, Shulian; Huang, Yudian; Li, Hui; Wang, Yunxia; Zhang, Xiaoman

2015-01-01

Dermatofibrosarcoma protuberans (DFSP) is a skin cancer usually mistaken as other benign tumors. Abnormal DFSP resection results in tumor recurrence. Quantitative characterization of collagen alteration on the skin tumor is essential for developing a diagnostic technique. In this study, second harmonic generation (SHG) microscopy was performed to obtain images of the human DFSP skin and normal skin. Subsequently, structure and texture analysis methods were applied to determine the differences in skin texture characteristics between the two skin types, and the link between collagen alteration and tumor was established. Results suggest that combining SHG microscopy and texture analysis methods is a feasible and effective method to describe the characteristics of skin tumor like DFSP. © Wiley Periodicals, Inc.

Please Don't Move-Evaluating Motion Artifact From Peripheral Quantitative Computed Tomography Scans Using Textural Features.

PubMed

Rantalainen, Timo; Chivers, Paola; Beck, Belinda R; Robertson, Sam; Hart, Nicolas H; Nimphius, Sophia; Weeks, Benjamin K; McIntyre, Fleur; Hands, Beth; Siafarikas, Aris

Most imaging methods, including peripheral quantitative computed tomography (pQCT), are susceptible to motion artifacts particularly in fidgety pediatric populations. Methods currently used to address motion artifact include manual screening (visual inspection) and objective assessments of the scans. However, previously reported objective methods either cannot be applied on the reconstructed image or have not been tested for distal bone sites. Therefore, the purpose of the present study was to develop and validate motion artifact classifiers to quantify motion artifact in pQCT scans. Whether textural features could provide adequate motion artifact classification performance in 2 adolescent datasets with pQCT scans from tibial and radial diaphyses and epiphyses was tested. The first dataset was split into training (66% of sample) and validation (33% of sample) datasets. Visual classification was used as the ground truth. Moderate to substantial classification performance (J48 classifier, kappa coefficients from 0.57 to 0.80) was observed in the validation dataset with the novel texture-based classifier. In applying the same classifier to the second cross-sectional dataset, a slight-to-fair (κ = 0.01-0.39) classification performance was observed. Overall, this novel textural analysis-based classifier provided a moderate-to-substantial classification of motion artifact when the classifier was specifically trained for the measurement device and population. Classification based on textural features may be used to prescreen obviously acceptable and unacceptable scans, with a subsequent human-operated visual classification of any remaining scans. Copyright © 2017 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.

Research of second harmonic generation images based on texture analysis

NASA Astrophysics Data System (ADS)

Liu, Yao; Li, Yan; Gong, Haiming; Zhu, Xiaoqin; Huang, Zufang; Chen, Guannan

2014-09-01

Texture analysis plays a crucial role in identifying objects or regions of interest in an image. It has been applied to a variety of medical image processing, ranging from the detection of disease and the segmentation of specific anatomical structures, to differentiation between healthy and pathological tissues. Second harmonic generation (SHG) microscopy as a potential noninvasive tool for imaging biological tissues has been widely used in medicine, with reduced phototoxicity and photobleaching. In this paper, we clarified the principles of texture analysis including statistical, transform, structural and model-based methods and gave examples of its applications, reviewing studies of the technique. Moreover, we tried to apply texture analysis to the SHG images for the differentiation of human skin scar tissues. Texture analysis method based on local binary pattern (LBP) and wavelet transform was used to extract texture features of SHG images from collagen in normal and abnormal scars, and then the scar SHG images were classified into normal or abnormal ones. Compared with other texture analysis methods with respect to the receiver operating characteristic analysis, LBP combined with wavelet transform was demonstrated to achieve higher accuracy. It can provide a new way for clinical diagnosis of scar types. At last, future development of texture analysis in SHG images were discussed.

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.

The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis

NASA Astrophysics Data System (ADS)

Leijenaar, Ralph T. H.; Nalbantov, Georgi; Carvalho, Sara; van Elmpt, Wouter J. C.; Troost, Esther G. C.; Boellaard, Ronald; Aerts, Hugo J. W. L.; Gillies, Robert J.; Lambin, Philippe

2015-08-01

FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) RD, dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) RB, maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, RB was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values-which was used as a surrogate for textural feature interpretation-between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

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

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.

Textural states of a hot-worked MA2-1 magnesium alloy

NASA Astrophysics Data System (ADS)

Serebryany, V. N.; Kochubei, A. Ya.; Kurtasov, S. F.; Mel'Nikov, K. E.

2007-02-01

Quantitative texture analysis is used to study texture formation in an MA2-1 magnesium alloy subjected to axisymmetric upsetting at temperatures of 250-450°C and strain rates of 10-4-100 -1. The deformed structure is examined by optical microscopy, and the results obtained are used to plot the structural-state diagram of the alloy after 50% upsetting. The experimental textures are compared with the textures calculated in terms of a thermoactivation model.

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.

Classification of grass pollen through the quantitative analysis of surface ornamentation and texture.

PubMed

Mander, Luke; Li, Mao; Mio, Washington; Fowlkes, Charless C; Punyasena, Surangi W

2013-11-07

Taxonomic identification of pollen and spores uses inherently qualitative descriptions of morphology. Consequently, identifications are restricted to categories that can be reliably classified by multiple analysts, resulting in the coarse taxonomic resolution of the pollen and spore record. Grass pollen represents an archetypal example; it is not routinely identified below family level. To address this issue, we developed quantitative morphometric methods to characterize surface ornamentation and classify grass pollen grains. This produces a means of quantifying morphological features that are traditionally described qualitatively. We used scanning electron microscopy to image 240 specimens of pollen from 12 species within the grass family (Poaceae). We classified these species by developing algorithmic features that quantify the size and density of sculptural elements on the pollen surface, and measure the complexity of the ornamentation they form. These features yielded a classification accuracy of 77.5%. In comparison, a texture descriptor based on modelling the statistical distribution of brightness values in image patches yielded a classification accuracy of 85.8%, and seven human subjects achieved accuracies between 68.33 and 81.67%. The algorithmic features we developed directly relate to biologically meaningful features of grass pollen morphology, and could facilitate direct interpretation of unsupervised classification results from fossil material.

The neutron texture diffractometer at the China Advanced Research Reactor

NASA Astrophysics Data System (ADS)

Li, Mei-Juan; Liu, Xiao-Long; Liu, Yun-Tao; Tian, Geng-Fang; Gao, Jian-Bo; Yu, Zhou-Xiang; Li, Yu-Qing; Wu, Li-Qi; Yang, Lin-Feng; Sun, Kai; Wang, Hong-Li; Santisteban, J. r.; Chen, Dong-Feng

2016-03-01

The first neutron texture diffractometer in China has been built at the China Advanced Research Reactor, due to strong demand for texture measurement with neutrons from the domestic user community. This neutron texture diffractometer has high neutron intensity, moderate resolution and is mainly applied to study texture in commonly used industrial materials and engineering components. In this paper, the design and characteristics of this instrument are described. The results for calibration with neutrons and quantitative texture analysis of zirconium alloy plate are presented. The comparison of texture measurements with the results obtained in HIPPO at LANSCE and Kowari at ANSTO illustrates the reliability of the texture diffractometer. Supported by National Nature Science Foundation of China (11105231, 11205248, 51327902) and International Atomic Energy Agency-TC program (CPR0012)

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.

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

Quantitative Relationship of Soil Texture with the Observed Population Density Reduction of Heterodera glycines after Annual Corn Rotation in Nebraska

PubMed Central

Pérez-Hernández, Oscar; Giesler, Loren J.

2014-01-01

Soil texture has been commonly associated with the population density of Heterodera glycines (soybean cyst nematode: SCN), but such an association has been mainly described in terms of textural classes. In this study, multivariate analysis and a generalized linear modeling approach were used to elucidate the quantitative relationship of soil texture with the observed SCN population density reduction after annual corn rotation in Nebraska. Forty-five commercial production fields were sampled in 2009, 2010, and 2011 and SCN population density (eggs/100 cm3 of soil) for each field was determined before (Pi) and after (Pf) annual corn rotation from ten 3 × 3-m sampling grids. Principal components analysis revealed that, compared with silt and clay, sand had a stronger association with SCN Pi and Pf. Cluster analysis using the average linkage method and confirmed through 1,000 bootstrap simulations identified two groups: one corresponding to predominant silt-and-clay fields and other to sand-predominant fields. This grouping suggested that SCN relative percent population decline was higher in the sandy than in the silt-and-clay predominant group. However, when groups were compared for their SCN population density reduction using Pf as the response, Pi as a covariate, and incorporating the year and field variability, a negative binomial generalized linear model indicated that the SCN population density reduction was not statistically different between the sand-predominant field group and the silt-and-clay predominant group. PMID:24987160

Structural analysis of natural textures.

PubMed

Vilnrotter, F M; Nevatia, R; Price, K E

1986-01-01

Many textures can be described structurally, in terms of the individual textural elements and their spatial relationships. This paper describes a system to generate useful descriptions of natural textures in these terms. The basic approach is to determine an initial, partial description of the elements using edge features. This description controls the extraction of the texture elements. The elements are grouped by type, and spatial relationships between elements are computed. The descriptions are shown to be useful for recognition of the textures, and for reconstruction of periodic textures.

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.

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.

The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia.

PubMed

Diamond, James; Anderson, Neil H; Bartels, Peter H; Montironi, Rodolfo; Hamilton, Peter W

2004-09-01

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at x 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 x 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.

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.

Quantitative Morphologic Analysis of Boulder Shape and Surface Texture to Infer Environmental History: A Case Study of Rock Breakdown at the Ephrata Fan, Channeled Scabland, Washington

NASA Technical Reports Server (NTRS)

Ehlmann, Bethany L.; Viles, Heather A.; Bourke, Mary C.

2008-01-01

Boulder morphology reflects both lithology and climate and is dictated by the combined effects of erosion, transport, and weathering. At present, morphologic information at the boulder scale is underutilized as a recorder of environmental processes, partly because of the lack of a systematic quantitative parameter set for reporting and comparing data sets. We develop such a parameter set, incorporating a range of measures of boulder form and surface texture. We use standard shape metrics measured in the field and fractal and morphometric classification methods borrowed from landscape analysis and applied to laser-scanned molds. The parameter set was pilot tested on three populations of basalt boulders with distinct breakdown histories in the Channeled Scabland, Washington: (1) basalt outcrop talus; (2) flood-transported boulders recently excavated from a quarry; and (3) flood-transported boulders, extensively weathered in situ on the Ephrata Fan surface. Size and shape data were found to distinguish between flood-transported and untransported boulders. Size and edge angles (approximately 120 degrees) of flood-transported boulders suggest removal by preferential fracturing along preexisting columnar joints, and curvature data indicate rounding relative to outcrop boulders. Surface textural data show that boulders which have been exposed at the surface are significantly rougher than those buried by fan sediments. Past signatures diagnostic of flood transport still persist on surface boulders, despite ongoing overprinting by processes in the present breakdown environment through roughening and fracturing in situ. Further use of this quantitative boulder parameter set at other terrestrial and planetary sites will aid in cataloging and understanding morphologic signatures of environmental processes.

Structural texture similarity metrics for image analysis and retrieval.

PubMed

Zujovic, Jana; Pappas, Thrasyvoulos N; Neuhoff, David L

2013-07-01

We develop new metrics for texture similarity that accounts for human visual perception and the stochastic nature of textures. The metrics rely entirely on local image statistics and allow substantial point-by-point deviations between textures that according to human judgment are essentially identical. The proposed metrics extend the ideas of structural similarity and are guided by research in texture analysis-synthesis. They are implemented using a steerable filter decomposition and incorporate a concise set of subband statistics, computed globally or in sliding windows. We conduct systematic tests to investigate metric performance in the context of "known-item search," the retrieval of textures that are "identical" to the query texture. This eliminates the need for cumbersome subjective tests, thus enabling comparisons with human performance on a large database. Our experimental results indicate that the proposed metrics outperform peak signal-to-noise ratio (PSNR), structural similarity metric (SSIM) and its variations, as well as state-of-the-art texture classification metrics, using standard statistical measures.

The promise and limits of PET texture analysis.

PubMed

Cheng, Nai-Ming; Fang, Yu-Hua Dean; Yen, Tzu-Chen

2013-11-01

Metabolic heterogeneity is a recognized characteristic of malignant tumors. Positron emission tomography (PET) texture analysis evaluated intratumoral heterogeneity in the uptake of (18)F-fluorodeoxyglucose. There were recent evidences that PET textural features were of prognostic significance in patients with different solid tumors. Unfortunately, there are still crucial standardization challenges to transform PET texture parameters from their current use as research tools into the arena of validated technologies for use in oncology practice. Testing its generalizability, robustness, consistency, and limitations is necessary before implementing it in daily patient care.

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.

Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis.

PubMed

Attiyeh, Marc A; Chakraborty, Jayasree; Doussot, Alexandre; Langdon-Embry, Liana; Mainarich, Shiana; Gönen, Mithat; Balachandran, Vinod P; D'Angelica, Michael I; DeMatteo, Ronald P; Jarnagin, William R; Kingham, T Peter; Allen, Peter J; Simpson, Amber L; Do, Richard K

2018-04-01

Pancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients. A prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19-9 levels and image features (model A), and then on CA19-9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation. A total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19-9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19-9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data. We present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.

Diagnostic analysis of liver B ultrasonic texture features based on LM neural network

NASA Astrophysics Data System (ADS)

Chi, Qingyun; Hua, Hu; Liu, Menglin; Jiang, Xiuying

2017-03-01

In this study, B ultrasound images of 124 benign and malignant patients were randomly selected as the study objects. The B ultrasound images of the liver were treated by enhanced de-noising. By constructing the gray level co-occurrence matrix which reflects the information of each angle, Principal Component Analysis of 22 texture features were extracted and combined with LM neural network for diagnosis and classification. Experimental results show that this method is a rapid and effective diagnostic method for liver imaging, which provides a quantitative basis for clinical diagnosis of liver diseases.

Characterizing commercial pureed foods: sensory, nutritional, and textural analysis.

PubMed

Ettinger, Laurel; Keller, Heather H; Duizer, Lisa M

2014-01-01

Dysphagia (swallowing impairment) is a common consequence of stroke and degenerative diseases such as Parkinson's and Alzheimer's. Limited research is available on pureed foods, specifically the qualities of commercial products. Because research has linked pureed foods, specifically in-house pureed products, to malnutrition due to inferior sensory and nutritional qualities, commercial purees also need to be investigated. Proprietary research on sensory attributes of commercial foods is available; however direct comparisons of commercial pureed foods have never been reported. Descriptive sensory analysis as well as nutritional and texture analysis of commercially pureed prepared products was performed using a trained descriptive analysis panel. The pureed foods tested included four brands of carrots, of turkey, and two of bread. Each commercial puree was analyzed for fat (Soxhlet), protein (Dumas), carbohydrate (proximate analysis), fiber (total fiber), and sodium content (Quantab titrator strips). The purees were also texturally compared with a line spread test and a back extrusion test. Differences were found in the purees for sensory attributes as well as nutritional and textural properties. Findings suggest that implementation of standards is required to reduce variability between products, specifically regarding the textural components of the products. This would ensure all commercial products available in Canada meet standards established as being considered safe for swallowing.

Texture analysis of pulmonary parenchyma in normal and emphysematous lung

NASA Astrophysics Data System (ADS)

Uppaluri, Renuka; Mitsa, Theophano; Hoffman, Eric A.; McLennan, Geoffrey; Sonka, Milan

1996-04-01

Tissue characterization using texture analysis is gaining increasing importance in medical imaging. We present a completely automated method for discriminating between normal and emphysematous regions from CT images. This method involves extracting seventeen features which are based on statistical, hybrid and fractal texture models. The best subset of features is derived from the training set using the divergence technique. A minimum distance classifier is used to classify the samples into one of the two classes--normal and emphysema. Sensitivity and specificity and accuracy values achieved were 80% or greater in most cases proving that texture analysis holds great promise in identifying emphysema.

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

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.

Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase 18F-FET PET accuracy without dynamic scans.

PubMed

Lohmann, Philipp; Stoffels, Gabriele; Ceccon, Garry; Rapp, Marion; Sabel, Michael; Filss, Christian P; Kamp, Marcel A; Stegmayr, Carina; Neumaier, Bernd; Shah, Nadim J; Langen, Karl-Josef; Galldiks, Norbert

2017-07-01

We investigated the potential of textural feature analysis of O-(2-[ 18 F]fluoroethyl)-L-tyrosine ( 18 F-FET) PET to differentiate radiation injury from brain metastasis recurrence. Forty-seven patients with contrast-enhancing brain lesions (n = 54) on MRI after radiotherapy of brain metastases underwent dynamic 18 F-FET PET. Tumour-to-brain ratios (TBRs) of 18 F-FET uptake and 62 textural parameters were determined on summed images 20-40 min post-injection. Tracer uptake kinetics, i.e., time-to-peak (TTP) and patterns of time-activity curves (TAC) were evaluated on dynamic PET data from 0-50 min post-injection. Diagnostic accuracy of investigated parameters and combinations thereof to discriminate between brain metastasis recurrence and radiation injury was compared. Diagnostic accuracy increased from 81 % for TBR mean alone to 85 % when combined with the textural parameter Coarseness or Short-zone emphasis. The accuracy of TBR max alone was 83 % and increased to 85 % after combination with the textural parameters Coarseness, Short-zone emphasis, or Correlation. Analysis of TACs resulted in an accuracy of 70 % for kinetic pattern alone and increased to 83 % when combined with TBR max . Textural feature analysis in combination with TBRs may have the potential to increase diagnostic accuracy for discrimination between brain metastasis recurrence and radiation injury, without the need for dynamic 18 F-FET PET scans. • Textural feature analysis provides quantitative information about tumour heterogeneity • Textural features help improve discrimination between brain metastasis recurrence and radiation injury • Textural features might be helpful to further understand tumour heterogeneity • Analysis does not require a more time consuming dynamic PET acquisition.

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.

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.

Texture analysis of ultrahigh field T2*-weighted MR images of the brain: application to Huntington's disease.

PubMed

Doan, Nhat Trung; van den Bogaard, Simon J A; Dumas, Eve M; Webb, Andrew G; van Buchem, Mark A; Roos, Raymund A C; van der Grond, Jeroen; Reiber, Johan H C; Milles, Julien

2014-03-01

To develop a framework for quantitative detection of between-group textural differences in ultrahigh field T2*-weighted MR images of the brain. MR images were acquired using a three-dimensional (3D) T2*-weighted gradient echo sequence on a 7 Tesla MRI system. The phase images were high-pass filtered to remove phase wraps. Thirteen textural features were computed for both the magnitude and phase images of a region of interest based on 3D Gray-Level Co-occurrence Matrix, and subsequently evaluated to detect between-group differences using a Mann-Whitney U-test. We applied the framework to study textural differences in subcortical structures between premanifest Huntington's disease (HD), manifest HD patients, and controls. In premanifest HD, four phase-based features showed a difference in the caudate nucleus. In manifest HD, 7 magnitude-based features showed a difference in the pallidum, 6 phase-based features in the caudate nucleus, and 10 phase-based features in the putamen. After multiple comparison correction, significant differences were shown in the putamen in manifest HD by two phase-based features (both adjusted P values=0.04). This study provides the first evidence of textural heterogeneity of subcortical structures in HD. Texture analysis of ultrahigh field T2*-weighted MR images can be useful for noninvasive monitoring of neurodegenerative diseases. Copyright © 2013 Wiley Periodicals, Inc.

Using multiscale texture and density features for near-term breast cancer risk analysis

PubMed Central

Sun, Wenqing; Tseng, Tzu-Liang (Bill); Qian, Wei; Zhang, Jianying; Saltzstein, Edward C.; Zheng, Bin; Lure, Fleming; Yu, Hui; Zhou, Shi

2015-01-01

Purpose: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. Methods: The authors’ dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the “prior” screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. Results: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). Conclusions: The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations. PMID:26127038

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.

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.

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.

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

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

3D tooth microwear texture analysis in fishes as a test of dietary hypotheses of durophagy

NASA Astrophysics Data System (ADS)

Purnell, Mark A.; Darras, Laurent P. G.

2016-03-01

An understanding of how extinct animals functioned underpins our understanding of past evolutionary events, including adaptive radiations, and the role of functional innovation and adaptation as drivers of both micro- and macroevolution. Yet analysis of function in extinct animals is fraught with difficulty. Hypotheses that interpret molariform teeth in fishes as evidence of durophagous (shell-crushing) diets provide a good example of the particular problems inherent in the methods of functional morphology. This is because the assumed close coupling of form and function upon which the approach is based is weakened by, among other things, behavioural flexibility and the absence of a clear one to one relationship between structures and functions. Here we show that ISO 25178-2 standard parameters for surface texture, derived from analysis of worn surfaces of molariform teeth of fishes, vary significantly between species that differ in the amount of hard-shelled prey they consume. Two populations of the Sheepshead Seabream (Archosargus probatocephalus) were studied. This fish is not a dietary specialist, and one of the populations is known to consume more vegetation and less hard-shelled prey than the other; this is reflected in significant differences in their microwear textures. The Archosargus populations differ significantly in their microwear from the specialist shell-crusher Anarhichas lupus (the Atlantic Wolffish). Multivariate analysis of these three groups of fishes lends further support to the relationship between diet and tooth microwear, and provides robust validation of the approach. Application of the multivariate models derived from microwear texture in Archosargus and Anarhichas to a third fish species—the cichlid Astatoreochromis alluaudi—successfully separates wild caught fish that ate hard-shelled prey from lab-raised fish that did not. This cross-taxon validation demonstrates that quantitative analysis of tooth microwear texture can

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.

Multi-Scale Fractal Analysis of Image Texture and Pattern

NASA Technical Reports Server (NTRS)

Emerson, Charles W.

1998-01-01

Fractals embody important ideas of self-similarity, in which the spatial behavior or appearance of a system is largely independent of scale. Self-similarity is defined as a property of curves or surfaces where each part is indistinguishable from the whole, or where the form of the curve or surface is invariant with respect to scale. An ideal fractal (or monofractal) curve or surface has a constant dimension over all scales, although it may not be an integer value. This is in contrast to Euclidean or topological dimensions, where discrete one, two, and three dimensions describe curves, planes, and volumes. Theoretically, if the digital numbers of a remotely sensed image resemble an ideal fractal surface, then due to the self-similarity property, the fractal dimension of the image will not vary with scale and resolution. However, most geographical phenomena are not strictly self-similar at all scales, but they can often be modeled by a stochastic fractal in which the scaling and self-similarity properties of the fractal have inexact patterns that can be described by statistics. Stochastic fractal sets relax the monofractal self-similarity assumption and measure many scales and resolutions in order to represent the varying form of a phenomenon as a function of local variables across space. In image interpretation, pattern is defined as the overall spatial form of related features, and the repetition of certain forms is a characteristic pattern found in many cultural objects and some natural features. Texture is the visual impression of coarseness or smoothness caused by the variability or uniformity of image tone or color. A potential use of fractals concerns the analysis of image texture. In these situations it is commonly observed that the degree of roughness or inexactness in an image or surface is a function of scale and not of experimental technique. The fractal dimension of remote sensing data could yield quantitative insight on the spatial complexity and

Textural and Mineralogical Analysis of Volcanic Rocks by µ-XRF Mapping.

PubMed

Germinario, Luigi; Cossio, Roberto; Maritan, Lara; Borghi, Alessandro; Mazzoli, Claudio

2016-06-01

In this study, µ-XRF was applied as a novel surface technique for quick acquisition of elemental X-ray maps of rocks, image analysis of which provides quantitative information on texture and rock-forming minerals. Bench-top µ-XRF is cost-effective, fast, and non-destructive, can be applied to both large (up to a few tens of cm) and fragile samples, and yields major and trace element analysis with good sensitivity. Here, X-ray mapping was performed with a resolution of 103.5 µm and spot size of 30 µm over sample areas of about 5×4 cm of Euganean trachyte, a volcanic porphyritic rock from the Euganean Hills (NE Italy) traditionally used in cultural heritage. The relative abundance of phenocrysts and groundmass, as well as the size and shape of the various mineral phases, were obtained from image analysis of the elemental maps. The quantified petrographic features allowed identification of various extraction sites, revealing an objective method for archaeometric provenance studies exploiting µ-XRF imaging.

"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.

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

Characterising the disintegration properties of tablets in opaque media using texture analysis.

PubMed

Scheuerle, Rebekah L; Gerrard, Stephen E; Kendall, Richard A; Tuleu, Catherine; Slater, Nigel K H; Mahbubani, Krishnaa T

2015-01-01

Tablet disintegration characterisation is used in pharmaceutical research, development, and quality control. Standard methods used to characterise tablet disintegration are often dependent on visual observation in measurement of disintegration times. This presents a challenge for disintegration studies of tablets in opaque, physiologically relevant media that could be useful for tablet formulation optimisation. This study has explored an application of texture analysis disintegration testing, a non-visual, quantitative means of determining tablet disintegration end point, by analysing the disintegration behaviour of two tablet formulations in opaque media. In this study, the disintegration behaviour of one tablet formulation manufactured in-house, and Sybedia Flashtab placebo tablets in water, bovine, and human milk were characterised. A novel method is presented to characterise the disintegration process and to quantify the disintegration end points of the tablets in various media using load data generated by a texture analyser probe. The disintegration times in the different media were found to be statistically different (P<0.0001) from one another for both tablet formulations using one-way ANOVA. Using the Tukey post-hoc test, the Sybedia Flashtab placebo tablets were found not to have statistically significant disintegration times from each other in human versus bovine milk (adjusted P value 0.1685). Copyright © 2015 Elsevier B.V. All rights reserved.

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.

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

Simulation of complex magnesium alloy texture using the axial component fit method with central normal distributions

NASA Astrophysics Data System (ADS)

Ivanova, T. M.; Serebryany, V. N.

2017-12-01

The component fit method in quantitative texture analysis assumes that the texture of the polycrystalline sample can be represented by a superposition of weighted standard distributions those are characterized by position in the orientation space, shape and sharpness of the scattering. The components of the peak and axial shapes are usually used. It is known that an axial texture develops in materials subjected to direct pressing. In this paper we considered the possibility of modelling a texture of a magnesium sample subjected to equal-channel angular pressing with axial components only. The results obtained make it possible to conclude that ECAP is also a process leading to the appearance of an axial texture in magnesium alloys.

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.

Quantitative Analysis of the Effect of Iterative Reconstruction Using a Phantom: Determining the Appropriate Blending Percentage

PubMed Central

Kim, Hyun Gi; Lee, Young Han; Choi, Jin-Young; Park, Mi-Suk; Kim, Myeong-Jin; Kim, Ki Whang

2015-01-01

Purpose To investigate the optimal blending percentage of adaptive statistical iterative reconstruction (ASIR) in a reduced radiation dose while preserving a degree of image quality and texture that is similar to that of standard-dose computed tomography (CT). Materials and Methods The CT performance phantom was scanned with standard and dose reduction protocols including reduced mAs or kVp. Image quality parameters including noise, spatial, and low-contrast resolution, as well as image texture, were quantitatively evaluated after applying various blending percentages of ASIR. The optimal blending percentage of ASIR that preserved image quality and texture compared to standard dose CT was investigated in each radiation dose reduction protocol. Results As the percentage of ASIR increased, noise and spatial-resolution decreased, whereas low-contrast resolution increased. In the texture analysis, an increasing percentage of ASIR resulted in an increase of angular second moment, inverse difference moment, and correlation and in a decrease of contrast and entropy. The 20% and 40% dose reduction protocols with 20% and 40% ASIR blending, respectively, resulted in an optimal quality of images with preservation of the image texture. Conclusion Blending the 40% ASIR to the 40% reduced tube-current product can maximize radiation dose reduction and preserve adequate image quality and texture. PMID:25510772

The Pore3D library package for the textural analysis of X-ray computed microtomographic images of rocks

NASA Astrophysics Data System (ADS)

Zandomeneghi, Daria; Mancini, Lucia; Voltolini, Marco; Brun, Francesco; Polacci, Margherita

2010-05-01

Many research fields in Geosciences require the knowledge of the three-dimensional (3D) texture of rocks. X-ray computed microtomography (μCT) supplies an effective method to directly acquire 3D information. Transmission X-ray μCT is a non-destructive technique based on the mapping of the linear attenuation coefficient of X-rays crossing the investigated sample. The 3D distribution of constituents and the contrast based on the different absorption properties of the components can be enhanced by phase-contrast imaging. On an X-ray tomographic dataset, if spatial resolution at the micron scale and proper software are available, a complete textural and morphological quantitative analysis can be carried out and a number of parameters can be extracted, including geometry and organization of discrete rock components (such as crystals, vesicles, fractures, alteration-compositional zones). In the case of volcanic rocks, μCT can be used to image and quantify the textural and morphological characteristics of the rock constituents, such as vesicles (gas bubbles in solidified, erupted products), crystals and glass fibers. For pyroclastic rocks, investigated parameters to characterize the vesicle portion are the size distribution, geometry and orientation of the pores, the pore-throat size and organization, the pore-surface roughness and the topology of the overall pore and pore-throat network. In this work we present several procedures able to extract quantitative information from CT images of volcanic rocks. The imaging experiments have been carried out at the Elettra Synchrotron Light Laboratory in Trieste (Italy) using both the synchrotron radiation at the SYRMEP beamline and a custom-developed μCT system, named TOMOLAB, equipped with a microfocus X-ray tube and based on a cone-beam geometry. The reconstructed 3D images (or volumes) have been elaborated with a software library, named Pore3D, custom-developed by the SYRMEP group at Elettra. The Pore3D software library

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.

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.

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

Can we trust the calculation of texture indices of CT images? A phantom study.

PubMed

Caramella, Caroline; Allorant, Adrien; Orlhac, Fanny; Bidault, Francois; Asselain, Bernard; Ammari, Samy; Jaranowski, Patricia; Moussier, Aurelie; Balleyguier, Corinne; Lassau, Nathalie; Pitre-Champagnat, Stephanie

2018-04-01

Texture analysis is an emerging tool in the field of medical imaging analysis. However, many issues have been raised in terms of its use in assessing patient images and it is crucial to harmonize and standardize this new imaging measurement tool. This study was designed to evaluate the reliability of texture indices of CT images on a phantom including a reproducibility study, to assess the discriminatory capacity of indices potentially relevant in CT medical images and to determine their redundancy. For the reproducibility and discriminatory analysis, eight identical CT acquisitions were performed on a phantom including one homogeneous insert and two close heterogeneous inserts. Texture indices were selected for their high reproducibility and capability of discriminating different textures. For the redundancy analysis, 39 acquisitions of the same phantom were performed using varying acquisition parameters and a correlation matrix was used to explore the 2 × 2 relationships. LIFEx software was used to explore 34 different parameters including first order and texture indices. Only eight indices of 34 exhibited high reproducibility and discriminated textures from each other. Skewness and kurtosis from histogram were independent from the six other indices but were intercorrelated, the other six indices correlated in diverse degrees (entropy, dissimilarity, and contrast of the co-occurrence matrix, contrast of the Neighborhood Gray Level difference matrix, SZE, ZLNU of the Gray-Level Size Zone Matrix). Care should be taken when using texture analysis as a tool to characterize CT images because changes in quantitation may be primarily due to internal variability rather than from real physio-pathological effects. Some textural indices appear to be sufficiently reliable and capable to discriminate close textures on CT images. © 2018 American Association of Physicists in Medicine.

Quantitative T2 combined with texture analysis of nuclear magnetic resonance images identify different degrees of muscle involvement in three mouse models of muscle dystrophy: mdx, Largemyd and mdx/Largemyd.

PubMed

Martins-Bach, Aurea B; Malheiros, Jackeline; Matot, Béatrice; Martins, Poliana C M; Almeida, Camila F; Caldeira, Waldir; Ribeiro, Alberto F; Loureiro de Sousa, Paulo; Azzabou, Noura; Tannús, Alberto; Carlier, Pierre G; Vainzof, Mariz

2015-01-01

Quantitative nuclear magnetic resonance imaging (MRI) has been considered a promising non-invasive tool for monitoring therapeutic essays in small size mouse models of muscular dystrophies. Here, we combined MRI (anatomical images and transverse relaxation time constant-T2-measurements) to texture analyses in the study of four mouse strains covering a wide range of dystrophic phenotypes. Two still unexplored mouse models of muscular dystrophies were analyzed: The severely affected Largemyd mouse and the recently generated and worst double mutant mdx/Largemyd mouse, as compared to the mildly affected mdx and normal mice. The results were compared to histopathological findings. MRI showed increased intermuscular fat and higher muscle T2 in the three dystrophic mouse models when compared to the wild-type mice (T2: mdx/Largemyd: 37.6±2.8 ms; mdx: 35.2±4.5 ms; Largemyd: 36.6±4.0 ms; wild-type: 29.1±1.8 ms, p<0.05), in addition to higher muscle T2 in the mdx/Largemyd mice when compared to mdx (p<0.05). The areas with increased muscle T2 in the MRI correlated spatially with the identified histopathological alterations such as necrosis, inflammation, degeneration and regeneration foci. Nevertheless, muscle T2 values were not correlated with the severity of the phenotype in the 3 dystrophic mouse strains, since the severely affected Largemyd showed similar values than both the mild mdx and worst mdx/Largemyd lineages. On the other hand, all studied mouse strains could be unambiguously identified with texture analysis, which reflected the observed differences in the distribution of signals in muscle MRI. Thus, combined T2 intensity maps and texture analysis is a powerful approach for the characterization and differentiation of dystrophic muscles with diverse genotypes and phenotypes. These new findings provide important noninvasive tools in the evaluation of the efficacy of new therapies, and most importantly, can be directly applied in human translational research.

Quantitative T2 Combined with Texture Analysis of Nuclear Magnetic Resonance Images Identify Different Degrees of Muscle Involvement in Three Mouse Models of Muscle Dystrophy: mdx, Largemyd and mdx/Largemyd

PubMed Central

Martins-Bach, Aurea B.; Malheiros, Jackeline; Matot, Béatrice; Martins, Poliana C. M.; Almeida, Camila F.; Caldeira, Waldir; Ribeiro, Alberto F.; Loureiro de Sousa, Paulo; Azzabou, Noura; Tannús, Alberto; Carlier, Pierre G.; Vainzof, Mariz

2015-01-01

Quantitative nuclear magnetic resonance imaging (MRI) has been considered a promising non-invasive tool for monitoring therapeutic essays in small size mouse models of muscular dystrophies. Here, we combined MRI (anatomical images and transverse relaxation time constant—T2—measurements) to texture analyses in the study of four mouse strains covering a wide range of dystrophic phenotypes. Two still unexplored mouse models of muscular dystrophies were analyzed: The severely affected Largemyd mouse and the recently generated and worst double mutant mdx/Largemyd mouse, as compared to the mildly affected mdx and normal mice. The results were compared to histopathological findings. MRI showed increased intermuscular fat and higher muscle T2 in the three dystrophic mouse models when compared to the wild-type mice (T2: mdx/Largemyd: 37.6±2.8 ms; mdx: 35.2±4.5 ms; Largemyd: 36.6±4.0 ms; wild-type: 29.1±1.8 ms, p<0.05), in addition to higher muscle T2 in the mdx/Largemyd mice when compared to mdx (p<0.05). The areas with increased muscle T2 in the MRI correlated spatially with the identified histopathological alterations such as necrosis, inflammation, degeneration and regeneration foci. Nevertheless, muscle T2 values were not correlated with the severity of the phenotype in the 3 dystrophic mouse strains, since the severely affected Largemyd showed similar values than both the mild mdx and worst mdx/Largemyd lineages. On the other hand, all studied mouse strains could be unambiguously identified with texture analysis, which reflected the observed differences in the distribution of signals in muscle MRI. Thus, combined T2 intensity maps and texture analysis is a powerful approach for the characterization and differentiation of dystrophic muscles with diverse genotypes and phenotypes. These new findings provide important noninvasive tools in the evaluation of the efficacy of new therapies, and most importantly, can be directly applied in human translational research

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.

Efficient Data Mining for Local Binary Pattern in Texture Image Analysis

PubMed Central

Kwak, Jin Tae; Xu, Sheng; Wood, Bradford J.

2015-01-01

Local binary pattern (LBP) is a simple gray scale descriptor to characterize the local distribution of the grey levels in an image. Multi-resolution LBP and/or combinations of the LBPs have shown to be effective in texture image analysis. However, it is unclear what resolutions or combinations to choose for texture analysis. Examining all the possible cases is impractical and intractable due to the exponential growth in a feature space. This limits the accuracy and time- and space-efficiency of LBP. Here, we propose a data mining approach for LBP, which efficiently explores a high-dimensional feature space and finds a relatively smaller number of discriminative features. The features can be any combinations of LBPs. These may not be achievable with conventional approaches. Hence, our approach not only fully utilizes the capability of LBP but also maintains the low computational complexity. We incorporated three different descriptors (LBP, local contrast measure, and local directional derivative measure) with three spatial resolutions and evaluated our approach using two comprehensive texture databases. The results demonstrated the effectiveness and robustness of our approach to different experimental designs and texture images. PMID:25767332

Use of MRI in Differentiation of Papillary Renal Cell Carcinoma Subtypes: Qualitative and Quantitative Analysis.

PubMed

Doshi, Ankur M; Ream, Justin M; Kierans, Andrea S; Bilbily, Matthew; Rusinek, Henry; Huang, William C; Chandarana, Hersh

2016-03-01

The purpose of this study was to determine whether qualitative and quantitative MRI feature analysis is useful for differentiating type 1 from type 2 papillary renal cell carcinoma (PRCC). This retrospective study included 21 type 1 and 17 type 2 PRCCs evaluated with preoperative MRI. Two radiologists independently evaluated various qualitative features, including signal intensity, heterogeneity, and margin. For the quantitative analysis, a radiology fellow and a medical student independently drew 3D volumes of interest over the entire tumor on T2-weighted HASTE images, apparent diffusion coefficient parametric maps, and nephrographic phase contrast-enhanced MR images to derive first-order texture metrics. Qualitative and quantitative features were compared between the groups. For both readers, qualitative features with greater frequency in type 2 PRCC included heterogeneous enhancement, indistinct margin, and T2 heterogeneity (all, p < 0.035). Indistinct margins and heterogeneous enhancement were independent predictors (AUC, 0.822). Quantitative analysis revealed that apparent diffusion coefficient, HASTE, and contrast-enhanced entropy were greater in type 2 PRCC (p < 0.05; AUC, 0.682-0.716). A combined quantitative and qualitative model had an AUC of 0.859. Qualitative features within the model had interreader concordance of 84-95%, and the quantitative data had intraclass coefficients of 0.873-0.961. Qualitative and quantitative features can help discriminate between type 1 and type 2 PRCC. Quantitative analysis may capture useful information that complements the qualitative appearance while benefiting from high interobserver agreement.

Diet of upper paleolithic modern humans: evidence from microwear texture analysis.

PubMed

El Zaatari, Sireen; Hublin, Jean-Jacques

2014-04-01

This article presents the results of the occlusal molar microwear texture analysis of 32 adult Upper Paleolithic modern humans from a total of 21 European sites dating to marine isotope stages 3 and 2. The occlusal molar microwear textures of these specimens were analyzed with the aim of examining the effects of the climatic, as well as the cultural, changes on the diets of the Upper Paleolithic modern humans. The results of this analysis do not reveal any environmentally driven dietary shifts for the Upper Paleolithic hominins indicating that the climatic and their associated paleoecological changes did not force these humans to significantly alter their diets in order to survive. However, the microwear texture analysis does detect culturally related changes in the Upper Paleolithic humans' diets. Specifically, significant differences in diet were found between the earlier Upper Paleolithic individuals, i.e., those belonging to the Aurignacian and Gravettian contexts, and the later Magdalenian ones, such that the diet of the latter group was more varied and included more abrasive foods compared with those of the former. Copyright © 2014 Wiley Periodicals, Inc.

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

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

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.

Multi-class texture analysis in colorectal cancer histology

NASA Astrophysics Data System (ADS)

Kather, Jakob Nikolas; Weis, Cleo-Aron; Bianconi, Francesco; Melchers, Susanne M.; Schad, Lothar R.; Gaiser, Timo; Marx, Alexander; Zöllner, Frank Gerrit

2016-06-01

Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.

BCC skin cancer diagnosis based on texture analysis techniques

NASA Astrophysics Data System (ADS)

Chuang, Shao-Hui; Sun, Xiaoyan; Chang, Wen-Yu; Chen, Gwo-Shing; Huang, Adam; Li, Jiang; McKenzie, Frederic D.

2011-03-01

In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% and 95%, respectively.

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.

About normal distribution on SO(3) group in texture analysis

NASA Astrophysics Data System (ADS)

Savyolova, T. I.; Filatov, S. V.

2017-12-01

This article studies and compares different normal distributions (NDs) on SO(3) group, which are used in texture analysis. Those NDs are: Fisher normal distribution (FND), Bunge normal distribution (BND), central normal distribution (CND) and wrapped normal distribution (WND). All of the previously mentioned NDs are central functions on SO(3) group. CND is a subcase for normal CLT-motivated distributions on SO(3) (CLT here is Parthasarathy’s central limit theorem). WND is motivated by CLT in R 3 and mapped to SO(3) group. A Monte Carlo method for modeling normally distributed values was studied for both CND and WND. All of the NDs mentioned above are used for modeling different components of crystallites orientation distribution function in texture analysis.

X-ray diffraction analysis of residual stresses in textured ZnO thin films

NASA Astrophysics Data System (ADS)

Dobročka, E.; Novák, P.; Búc, D.; Harmatha, L.; Murín, J.

2017-02-01

Residual stresses are commonly generated in thin films during the deposition process and can influence the film properties. Among a number of techniques developed for stress analysis, X-ray diffraction methods, especially the grazing incidence set-up, are of special importance due to their capability to analyze the stresses in very thin layers as well as to investigate the depth variation of the stresses. In this contribution a method combining multiple {hkl} and multiple χ modes of X-ray diffraction stress analysis in grazing incidence set-up is used for the measurement of residual stress in strongly textured ZnO thin films. The method improves the precision of the stress evaluation in textured samples. Because the measurements are performed at very low incidence angles, the effect of refraction of X-rays on the measured stress is analyzed in details for the general case of non-coplanar geometry. It is shown that this effect cannot be neglected if the angle of incidence approaches the critical angle. The X-ray stress factors are calculated for hexagonal fiber-textured ZnO for the Reuss model of grain-interaction and the effect of texture on the stress factors is analyzed. The texture in the layer is modelled by Gaussian distribution function. Numerical results indicate that in the process of stress evaluation the Reuss model can be replaced by much simpler crystallite group method if the standard deviation of Gaussian describing the texture is less than 6°. The results can be adapted for fiber-textured films of various hexagonal materials.

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.

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.

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.

Analyzing and improving surface texture by dual-rotation magnetorheological finishing

NASA Astrophysics Data System (ADS)

Wang, Yuyue; Zhang, Yun; Feng, Zhijing

2016-01-01

The main advantages of magnetorheological finishing (MRF) are its high convergence rate of surface error, the ability of polishing aspheric surfaces and nearly no subsurface damage. However, common MRF produces directional surface texture due to the constant flow direction of the magnetorheological (MR) polishing fluid. This paper studies the mechanism of surface texture formation by texture modeling. Dual-rotation magnetorheological finishing (DRMRF) is presented to suppress directional surface texture after analyzing the results of the texture model for common MRF. The results of the surface texture model for DRMRF and the proposed quantitative method based on mathematical statistics indicate the effective suppression of directional surface texture. An experimental setup is developed and experiments show directional surface texture and no directional surface texture in common MRF and DRMRF, respectively. As a result, the surface roughness of DRMRF is 0.578 nm (root-mean-square value) which is lower than 1.109 nm in common MRF.

Evaluation of quantitative image analysis criteria for the high-resolution microendoscopic detection of neoplasia in Barrett's esophagus

NASA Astrophysics Data System (ADS)

Muldoon, Timothy J.; Thekkek, Nadhi; Roblyer, Darren; Maru, Dipen; Harpaz, Noam; Potack, Jonathan; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2010-03-01

Early detection of neoplasia in patients with Barrett's esophagus is essential to improve outcomes. The aim of this ex vivo study was to evaluate the ability of high-resolution microendoscopic imaging and quantitative image analysis to identify neoplastic lesions in patients with Barrett's esophagus. Nine patients with pathologically confirmed Barrett's esophagus underwent endoscopic examination with biopsies or endoscopic mucosal resection. Resected fresh tissue was imaged with fiber bundle microendoscopy; images were analyzed by visual interpretation or by quantitative image analysis to predict whether the imaged sites were non-neoplastic or neoplastic. The best performing pair of quantitative features were chosen based on their ability to correctly classify the data into the two groups. Predictions were compared to the gold standard of histopathology. Subjective analysis of the images by expert clinicians achieved average sensitivity and specificity of 87% and 61%, respectively. The best performing quantitative classification algorithm relied on two image textural features and achieved a sensitivity and specificity of 87% and 85%, respectively. This ex vivo pilot trial demonstrates that quantitative analysis of images obtained with a simple microendoscope system can distinguish neoplasia in Barrett's esophagus with good sensitivity and specificity when compared to histopathology and to subjective image interpretation.

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.

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.

2D Magnetic Texture Analysis of Co-Cu Films

NASA Astrophysics Data System (ADS)

Bayirli, Mehmet; Karaagac, Oznur; Kockar, Hakan; Alper, Mursel

2017-05-01

The magnetic textures for the produced magnetic materials are important concepts in accordance with technical applications. Therefore, the aim of this article is to determine 2D magnetic textures of electrodeposited Co-Cu films by the measurement of hysteresis loops at the incremented angles. For that, Co-Cu films were deposited with different Co2+ in the electrolyte. In addition, the easy-axis orientation in the films from the squareness values of the angles, Mp(β) obtained by the hysteresis loops have been numerically studied using the Fourier series analysis. The differences observed in the magnetic easy-axis distributions were attributed to changes of the incorporation of Co in the films with the change of Co2+ in the electrolyte. The coefficients of Fourier series (A0 and A2n ) were also computed for 2D films. It is seen that a systematic and small decrease in A0 and an obvious decrease in A2n (n=1) were observed with increasing incorporated Co in the films. Results imply that interactions cause slightly demagnetization effect accordance with higher incorporation of Co in the films. Furthermore, the crystal structure of the Co-Cu films analysed by X-ray diffraction revealed that the films have dominantly face-centred cubic structure. Film contents analysed by energy-dispersive X-ray spectroscopy and film morphologies observed by scanning electron microscope also support the magnetic texture analysis results found by numerical computation.

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

Texture and anisotropy in ferroelectric lead metaniobate

NASA Astrophysics Data System (ADS)

Iverson, Benjamin John

Ferroelectric lead metaniobate, PbNb2O6, is a piezoelectric ceramic typically used because of its elevated Curie temperature and anisotropic properties. However, the piezoelectric constant, d33, is relatively low in randomly oriented ceramics when compared to other ferroelectrics. Crystallographic texturing is often employed to increase the piezoelectric constant because the spontaneous polarization axes of grains are better aligned. In this research, crystallographic textures induced through tape casting are distinguished from textures induced through electrical poling. Texture is described using multiple quantitative approaches utilizing X-ray and neutron time-of-flight diffraction. Tape casting lead metaniobate with an inclusion of acicular template particles induces an orthotropic texture distribution. Templated grain growth from seed particles oriented during casting results in anisotropic grain structures. The degree of preferred orientation is directly linked to the shear behavior of the tape cast slurry. Increases in template concentration, slurry viscosity, and casting velocity lead to larger textures by inducing more particle orientation in the tape casting plane. The maximum 010 texture distributions were two and a half multiples of a random distribution. Ferroelectric texture was induced by electrical poling. Electric poling increases the volume of material oriented with the spontaneous polarization direction in the material. Samples with an initial paraelectric texture exhibit a greater change in the domain volume fraction during electrical poling than randomly oriented ceramics. In tape cast samples, the resulting piezoelectric response is proportional to the 010 texture present prior to poling. This results in property anisotropy dependent on initial texture. Piezoelectric properties measured on the most textured ceramics were similar to those obtained with a commercial standard.

Sensory texture analysis of thickened liquids during ingestion.

PubMed

Chambers, Edgar; Jenkins, Alicia; Mertz Garcia, Jane

2017-12-01

Practitioners support the use of thickened liquids for many patients with disordered swallowing. Although physical measures have highlighted differences among products there are questions about the ability of the measures to fully explain the sensory texture effects during swallowing of thickened liquids. This study used a trained sensory panel to describe the textural aspects of liquids during ingestion and swallowing. The lexicon was able to characterize differences in beverages, thickeners, and thickness levels with the most important attribute being viscosity, which loaded heavily in the almost one-dimensional space that resulted from the sensory analysis of these beverages. Other effects, such as slipperiness provided some minimal additional information on the products. Trained sensory panelists were shown to be useful in the measurement of differences in thickened liquid products prescribed for patients with dysphagia. They were able to differentiate products based on perceived differences related to flow speed, viscosity, and other parameters suggesting their use in further studies of swallowing behavior and for development of products for disordered swallowing should be considered. Understanding how these variables might relate to clinical decision making about product selection or modification to best meet the nutritional needs of a person with disordered swallowing could be helpful. This is especially true given the difficulties in measuring texture instrumentally in these products. © 2017 Wiley Periodicals, Inc.

Understanding Crystal Populations: The Role of Textural Analysis in Determining Magmatic Timescales

NASA Astrophysics Data System (ADS)

Jerram, D. A.

2006-12-01

Crystal populations in igneous rocks that erupt at the Earths surface act as records of magma chamber processes at depth, predominantly recording episodes of growth/nucleation and geochemical changes within the host body. Detailed inspection of such crystal populations, however, reveals a complex crystal cargo that comprises crystals which have grown directly from the host, crystals that have spent one or more protracted periods being isolated from the host magma and crystals that originated from a completely different magma body and/or country rock. To further interrogate this crystal cargo we can use textural analysis techniques to fully quantify the crystal population and gather important information about the population, such as crystal morphology, spatial distribution and size relationships. When quantified, such data can be used to better constrain the different components of the resultant crystal population and how they relate to each other. Additionally, by combining textural analysis information with geochemical analysis, a powerful measure of magma timescales and magma chamber processes results. In this contribution the different types of textural analysis techniques in 2D and 3D are introduced with examples from both plutonic and volcanic systems presented to highlight the roll of this approach to quantifying magma timescales.

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

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

Texture-Based Automated Lithological Classification Using Aeromagenetic Anomaly Images

USGS Publications Warehouse

Shankar, Vivek

2009-01-01

This report consists of a thesis submitted to the faculty of the Department of Electrical and Computer Engineering, in partial fulfillment of the requirements for the degree of Master of Science, Graduate College, The University of Arizona, 2004 Aeromagnetic anomaly images are geophysical prospecting tools frequently used in the exploration of metalliferous minerals and hydrocarbons. The amplitude and texture content of these images provide a wealth of information to geophysicists who attempt to delineate the nature of the Earth's upper crust. These images prove to be extremely useful in remote areas and locations where the minerals of interest are concealed by basin fill. Typically, geophysicists compile a suite of aeromagnetic anomaly images, derived from amplitude and texture measurement operations, in order to obtain a qualitative interpretation of the lithological (rock) structure. Texture measures have proven to be especially capable of capturing the magnetic anomaly signature of unique lithological units. We performed a quantitative study to explore the possibility of using texture measures as input to a machine vision system in order to achieve automated classification of lithological units. This work demonstrated a significant improvement in classification accuracy over random guessing based on a priori probabilities. Additionally, a quantitative comparison between the performances of five classes of texture measures in their ability to discriminate lithological units was achieved.

Image statistics underlying natural texture selectivity of neurons in macaque V4

PubMed Central

Okazawa, Gouki; Tajima, Satohiro; Komatsu, Hidehiko

2015-01-01

Our daily visual experiences are inevitably linked to recognizing the rich variety of textures. However, how the brain encodes and differentiates a plethora of natural textures remains poorly understood. Here, we show that many neurons in macaque V4 selectively encode sparse combinations of higher-order image statistics to represent natural textures. We systematically explored neural selectivity in a high-dimensional texture space by combining texture synthesis and efficient-sampling techniques. This yielded parameterized models for individual texture-selective neurons. The models provided parsimonious but powerful predictors for each neuron’s preferred textures using a sparse combination of image statistics. As a whole population, the neuronal tuning was distributed in a way suitable for categorizing textures and quantitatively predicts human ability to discriminate textures. Together, we suggest that the collective representation of visual image statistics in V4 plays a key role in organizing the natural texture perception. PMID:25535362

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.

Cascaded Amplitude Modulations in Sound Texture Perception.

PubMed

McWalter, Richard; Dau, Torsten

2017-01-01

Sound textures, such as crackling fire or chirping crickets, represent a broad class of sounds defined by their homogeneous temporal structure. It has been suggested that the perception of texture is mediated by time-averaged summary statistics measured from early auditory representations. In this study, we investigated the perception of sound textures that contain rhythmic structure, specifically second-order amplitude modulations that arise from the interaction of different modulation rates, previously described as "beating" in the envelope-frequency domain. We developed an auditory texture model that utilizes a cascade of modulation filterbanks that capture the structure of simple rhythmic patterns. The model was examined in a series of psychophysical listening experiments using synthetic sound textures-stimuli generated using time-averaged statistics measured from real-world textures. In a texture identification task, our results indicated that second-order amplitude modulation sensitivity enhanced recognition. Next, we examined the contribution of the second-order modulation analysis in a preference task, where the proposed auditory texture model was preferred over a range of model deviants that lacked second-order modulation rate sensitivity. Lastly, the discriminability of textures that included second-order amplitude modulations appeared to be perceived using a time-averaging process. Overall, our results demonstrate that the inclusion of second-order modulation analysis generates improvements in the perceived quality of synthetic textures compared to the first-order modulation analysis considered in previous approaches.

Deep neural networks for texture classification-A theoretical analysis.

PubMed

Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of texture analysis method for mammogram density classification

NASA Astrophysics Data System (ADS)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

Texture analysis of apparent diffusion coefficient maps for treatment response assessment in prostate cancer bone metastases-A pilot study.

PubMed

Reischauer, Carolin; Patzwahl, René; Koh, Dow-Mu; Froehlich, Johannes M; Gutzeit, Andreas

2018-04-01

To evaluate whole-lesion volumetric texture analysis of apparent diffusion coefficient (ADC) maps for assessing treatment response in prostate cancer bone metastases. Texture analysis is performed in 12 treatment-naïve patients with 34 metastases before treatment and at one, two, and three months after the initiation of androgen deprivation therapy. Four first-order and 19 second-order statistical texture features are computed on the ADC maps in each lesion at every time point. Repeatability, inter-patient variability, and changes in the feature values under therapy are investigated. Spearman rank's correlation coefficients are calculated across time to demonstrate the relationship between the texture features and the serum prostate specific antigen (PSA) levels. With few exceptions, the texture features exhibited moderate to high precision. At the same time, Friedman's tests revealed that all first-order and second-order statistical texture features changed significantly in response to therapy. Thereby, the majority of texture features showed significant changes in their values at all post-treatment time points relative to baseline. Bivariate analysis detected significant correlations between the great majority of texture features and the serum PSA levels. Thereby, three first-order and six second-order statistical features showed strong correlations with the serum PSA levels across time. The findings in the present work indicate that whole-tumor volumetric texture analysis may be utilized for response assessment in prostate cancer bone metastases. The approach may be used as a complementary measure for treatment monitoring in conjunction with averaged ADC values. Copyright © 2018 Elsevier B.V. All rights reserved.

Application of Texture Analysis to Study Small Vessel Disease and Blood-Brain Barrier Integrity.

PubMed

Valdés Hernández, Maria Del C; González-Castro, Victor; Chappell, Francesca M; Sakka, Eleni; Makin, Stephen; Armitage, Paul A; Nailon, William H; Wardlaw, Joanna M

2017-01-01

We evaluate the alternative use of texture analysis for evaluating the role of blood-brain barrier (BBB) in small vessel disease (SVD). We used brain magnetic resonance imaging from 204 stroke patients, acquired before and 20 min after intravenous gadolinium administration. We segmented tissues, white matter hyperintensities (WMH) and applied validated visual scores. We measured textural features in all tissues pre- and post-contrast and used ANCOVA to evaluate the effect of SVD indicators on the pre-/post-contrast change, Kruskal-Wallis for significance between patient groups and linear mixed models for pre-/post-contrast variations in cerebrospinal fluid (CSF) with Fazekas scores. Textural "homogeneity" increase in normal tissues with higher presence of SVD indicators was consistently more overt than in abnormal tissues. Textural "homogeneity" increased with age, basal ganglia perivascular spaces scores ( p  < 0.01) and SVD scores ( p  < 0.05) and was significantly higher in hypertensive patients ( p  < 0.002) and lacunar stroke ( p  = 0.04). Hypertension (74% patients), WMH load (median = 1.5 ± 1.6% of intracranial volume), and age (mean = 65.6 years, SD = 11.3) predicted the pre/post-contrast change in normal white matter, WMH, and index stroke lesion. CSF signal increased with increasing SVD post-contrast. A consistent general pattern of increasing textural "homogeneity" with increasing SVD and post-contrast change in CSF with increasing WMH suggest that texture analysis may be useful for the study of BBB integrity.

Feasibility of opportunistic osteoporosis screening in routine contrast-enhanced multi detector computed tomography (MDCT) using texture analysis.

PubMed

Mookiah, M R K; Rohrmeier, A; Dieckmeyer, M; Mei, K; Kopp, F K; Noel, P B; Kirschke, J S; Baum, T; Subburaj, K

2018-04-01

This study investigated the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis. The results showed an acceptable reproducibility of texture features, and these features could discriminate healthy/osteoporotic fracture cohort with an accuracy of 83%. This aim of this study is to investigate the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis. We performed texture analysis at the spine in routine MDCT exams and investigated the effect of intravenous contrast medium (IVCM) (n = 7), slice thickness (n = 7), the long-term reproducibility (n = 9), and the ability to differentiate healthy/osteoporotic fracture cohort (n = 9 age and gender matched pairs). Eight texture features were extracted using gray level co-occurrence matrix (GLCM). The independent sample t test was used to rank the features of healthy/fracture cohort and classification was performed using support vector machine (SVM). The results revealed significant correlations between texture parameters derived from MDCT scans with and without IVCM (r up to 0.91) slice thickness of 1 mm versus 2 and 3 mm (r up to 0.96) and scan-rescan (r up to 0.59). The performance of the SVM classifier was evaluated using 10-fold cross-validation and revealed an average classification accuracy of 83%. Opportunistic osteoporosis screening at the spine using specific texture parameters (energy, entropy, and homogeneity) and SVM can be performed in routine contrast-enhanced MDCT exams.

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.

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.

Texture and phase analysis of deformed SUS304 by using HIPPO

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

Takajo, Shigehiro; Vogel, Sven C.

2016-11-15

These slides represent the author's research activity at Los Alamos National Laboratory (LANL), which is about texture and phase analysis of deformed SUS304 by using HIPPO. The following topics are covered: diffraction histogram at each sample position, diffraction histogram (all bank data averaged), possiblity of ε-phase, MAUD analysis with including ε-phase.

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.

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

Characterization of PET/CT images using texture analysis: the past, the present… any future?

PubMed

Hatt, Mathieu; Tixier, Florent; Pierce, Larry; Kinahan, Paul E; Le Rest, Catherine Cheze; Visvikis, Dimitris

2017-01-01

After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.

Automated Texture Analysis and Determination of Fibre Orientation of Heart Tissue: A Morphometric Study.

PubMed

Zach, Bernhard; Hofer, Ernst; Asslaber, Martin; Ahammer, Helmut

2016-01-01

The human heart has a heterogeneous structure, which is characterized by different cell types and their spatial configurations. The physical structure, especially the fibre orientation and the interstitial fibrosis, determines the electrical excitation and in further consequence the contractility in macroscopic as well as in microscopic areas. Modern image processing methods and parameters could be used to describe the image content and image texture. In most cases the description of the texture is not satisfying because the fibre orientation, detected with common algorithms, is biased by elements such as fibrocytes or endothelial nuclei. The goal of this work is to figure out if cardiac tissue can be analysed and classified on a microscopic level by automated image processing methods with a focus on an accurate detection of the fibre orientation. Quantitative parameters for identification of textures of different complexity or pathological attributes inside the heart were determined. The focus was set on the detection of the fibre orientation, which was calculated on the basis of the cardiomyocytes' nuclei. It turned out that the orientation of these nuclei corresponded with a high precision to the fibre orientation in the image plane. Additionally, these nuclei also indicated very well the inclination of the fibre.

Texture analysis for survival prediction of pancreatic ductal adenocarcinoma patients with neoadjuvant chemotherapy

NASA Astrophysics Data System (ADS)

Chakraborty, Jayasree; Langdon-Embry, Liana; Escalon, Joanna G.; Allen, Peter J.; Lowery, Maeve A.; O'Reilly, Eileen M.; Do, Richard K. G.; Simpson, Amber L.

2016-03-01

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the United States. The five-year survival rate for all stages is approximately 6%, and approximately 2% when presenting with distant disease.1 Only 10-20% of all patients present with resectable disease, but recurrence rates are high with only 5 to 15% remaining free of disease at 5 years. At this time, we are unable to distinguish between resectable PDAC patients with occult metastatic disease from those with potentially curable disease. Early classification of these tumor types may eventually lead to changes in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant treatments. Texture analysis is an emerging methodology in oncologic imaging for quantitatively assessing tumor heterogeneity that could potentially aid in the stratification of these patients. The present study derives several texture-based features from CT images of PDAC patients, acquired prior to neoadjuvant chemotherapy, and analyzes their performance, individually as well as in combination, as prognostic markers. A fuzzy minimum redundancy maximum relevance method with leave-one-image-out technique is included to select discriminating features from the set of extracted features. With a naive Bayes classifier, the proposed method predicts the 5-year overall survival of PDAC patients prior to neoadjuvant therapy and achieves the best results in terms of the area under the receiver operating characteristic curve of 0:858 and accuracy of 83:0% with four-fold cross-validation techniques.

Quantification of tissue texture with photoacoustic spectrum analysis

NASA Astrophysics Data System (ADS)

Wang, Xueding; Xu, Guan; Meng, Zhuo-Xian; Lin, Jiandie; Carson, Paul

2014-05-01

Photoacoustic (PA) imaging is an emerging technology that could map the functional contrasts in deep biological tissues in high resolution by "listening" to the laser induced thermoelastic waves. Almost all of the current studies in PA imaging are focused on the intensity of the PA signals as an indication of the optical absorbance of the biological tissues. Our group has for the first time demonstrated that the frequency domain power distribution of the broadband PA signals encode the texture information within the regions-of-interest (ROI). Following the similar method of ultrasound spectral analysis (USSA), photoacoustic spectrum analysis (PASA) could evaluate the relative concentrations and, more importantly, the dimensions of microstructures of the optically absorbing materials in biological tissues, including lipid, collagen, water and hemoglobin. By providing valuable insights into tissue pathology, PASA should benefit basic research and clinical management of many diseases, and may help achieve eventual "noninvasive biopsy". In this work, taking advantage of the optical absorption contrasts contributed by lipid and hemoglobin at 1200-nm and 532-nm wavelengths respectively, we investigated the capability of PASA in identifying histological changes corresponding to fat accumulation livers through the study on ex vivo and in situ mouse models. The PA signals from the mouse livers were acquired using our PA and US dual-modality imaging system, and analyzed in the frequency domain. After quantifying the power spectrum by fitting it to a first order model, three spectral parameters, including the intercept, the midband fit and the slope, were extracted and used to differentiate fatty livers from normal livers. The comparison between the PASA parameters from the normal and the fatty livers supports our hypotheses that PASA can quantitatively identify the microstructure changes in liver tissues for differentiating normal and fatty livers.

Classification of interstitial lung disease patterns with topological texture features

NASA Astrophysics Data System (ADS)

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

2010-03-01

Topological texture features 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 honey-combing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. A set of 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 three Minkowski Functionals (MFs, e.g. MF.euler). 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 and the significance thresholds were adjusted for multiple comparisons by the Bonferroni correction. The best classification results were obtained by the MF features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers. The highest accuracy was found for MF.euler (97.5%, 96.6%; 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 topological texture features can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

Cascaded Amplitude Modulations in Sound Texture Perception

PubMed Central

McWalter, Richard; Dau, Torsten

2017-01-01

Sound textures, such as crackling fire or chirping crickets, represent a broad class of sounds defined by their homogeneous temporal structure. It has been suggested that the perception of texture is mediated by time-averaged summary statistics measured from early auditory representations. In this study, we investigated the perception of sound textures that contain rhythmic structure, specifically second-order amplitude modulations that arise from the interaction of different modulation rates, previously described as “beating” in the envelope-frequency domain. We developed an auditory texture model that utilizes a cascade of modulation filterbanks that capture the structure of simple rhythmic patterns. The model was examined in a series of psychophysical listening experiments using synthetic sound textures—stimuli generated using time-averaged statistics measured from real-world textures. In a texture identification task, our results indicated that second-order amplitude modulation sensitivity enhanced recognition. Next, we examined the contribution of the second-order modulation analysis in a preference task, where the proposed auditory texture model was preferred over a range of model deviants that lacked second-order modulation rate sensitivity. Lastly, the discriminability of textures that included second-order amplitude modulations appeared to be perceived using a time-averaging process. Overall, our results demonstrate that the inclusion of second-order modulation analysis generates improvements in the perceived quality of synthetic textures compared to the first-order modulation analysis considered in previous approaches. PMID:28955191

The effect of texture granularity on texture synthesis quality

NASA Astrophysics Data System (ADS)

Golestaneh, S. Alireza; Subedar, Mahesh M.; Karam, Lina J.

2015-09-01

Natural and artificial textures occur frequently in images and in video sequences. Image/video coding systems based on texture synthesis can make use of a reliable texture synthesis quality assessment method in order to improve the compression performance in terms of perceived quality and bit-rate. Existing objective visual quality assessment methods do not perform satisfactorily when predicting the synthesized texture quality. In our previous work, we showed that texture regularity can be used as an attribute for estimating the quality of synthesized textures. In this paper, we study the effect of another texture attribute, namely texture granularity, on the quality of synthesized textures. For this purpose, subjective studies are conducted to assess the quality of synthesized textures with different levels (low, medium, high) of perceived texture granularity using different types of texture synthesis methods.

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

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

Textural signatures for wetland vegetation

NASA Technical Reports Server (NTRS)

Whitman, R. I.; Marcellus, K. L.

1973-01-01

This investigation indicates that unique textural signatures do exist for specific wetland communities at certain times in the growing season. When photographs with the proper resolution are obtained, the textural features can identify the spectral features of the vegetation community seen with lower resolution mapping data. The development of a matrix of optimum textural signatures is the goal of this research. Seasonal variations of spectral and textural features are particularly important when performing a vegetations analysis of fresh water marshes. This matrix will aid in flight planning, since expected seasonal variations and resolution requirements can be established prior to a given flight mission.

Mapping lava flow textures using three-dimensional measures of surface roughness

NASA Astrophysics Data System (ADS)

Mallonee, H. C.; Kobs-Nawotniak, S. E.; McGregor, M.; Hughes, S. S.; Neish, C.; Downs, M.; Delparte, D.; Lim, D. S. S.; Heldmann, J. L.

2016-12-01

Lava flow emplacement conditions are reflected in the surface textures of a lava flow; unravelling these conditions is crucial to understanding the eruptive history and characteristics of basaltic volcanoes. Mapping lava flow textures using visual imagery alone is an inherently subjective process, as these images generally lack the resolution needed to make these determinations. Our team has begun mapping lava flow textures using visual spectrum imagery, which is an inherently subjective process involving the challenge of identifying transitional textures such as rubbly and slabby pāhoehoe, as these textures are similar in appearance and defined qualitatively. This is particularly problematic for interpreting planetary lava flow textures, where we have more limited data. We present a tool to objectively classify lava flow textures based on quantitative measures of roughness, including the 2D Hurst exponent, RMS height, and 2D:3D surface area ratio. We collected aerial images at Craters of the Moon National Monument (COTM) using Unmanned Aerial Vehicles (UAVs) in 2015 and 2016 as part of the FINESSE (Field Investigations to Enable Solar System Science and Exploration) and BASALT (Biologic Analog Science Associated with Lava Terrains) research projects. The aerial images were stitched together to create Digital Terrain Models (DTMs) with resolutions on the order of centimeters. The DTMs were evaluated by the classification tool described above, with output compared against field assessment of the texture. Further, the DTMs were downsampled and reevaluated to assess the efficacy of the classification tool at data resolutions similar to current datasets from other planetary bodies. This tool allows objective classification of lava flow texture, which enables more accurate interpretations of flow characteristics. This work also gives context for interpretations of flows with comparatively low data resolutions, such as those on the Moon and Mars. Textural maps based on

Differentiating pheochromocytoma from lipid-poor adrenocortical adenoma by CT texture analysis: feasibility study.

PubMed

Zhang, Gu-Mu-Yang; Shi, Bing; Sun, Hao; Jin, Zheng-Yu; Xue, Hua-Dan

2017-09-01

To investigate the feasibility of using CT texture analysis (CTTA) to differentiate pheochromocytoma from lipid-poor adrenocortical adenoma (lp-ACA). Ninety-eight pheochromocytomas and 66 lp-ACAs were included in this retrospective study. CTTA was performed on unenhanced and enhanced images. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different for the objective. Diagnostic accuracies were evaluated using the cutoff values of texture parameters with the highest AUCs. Compared to lp-ACAs, pheochromocytomas had significantly higher mean gray-level intensity (Mean), entropy, and mean of positive pixels (MPP), but lower skewness and kurtosis on unenhanced images (P < 0.001). On enhanced images, these texture-quantifiers followed a similar trend where Mean, entropy, and MPP were higher, but skewness and kurtosis were lower in pheochromocytomas. Standard deviation (SD) was also significantly higher in pheochromocytomas on enhanced images. Mean and MPP quantified from no filtration on unenhanced CT images yielded the highest AUC of 0.86 ± 0.03 (95% CI 0.81-0.91) at a cutoff value of 34.0 for Mean and MPP, respectively (sensitivity = 79.6%, specificity = 83.3%, accuracy = 81.1%). It was feasible to use CTTA to differentiate pheochromocytoma from lp-ACA.

Quantitative Mapping of Surface Texture on the Northern Polar Residual Cap of Mars

NASA Astrophysics Data System (ADS)

Milkovich, S. M.; Byrne, S.; Russell, P. S.

2010-12-01

The northern polar residual cap (NPRC) of Mars is a water ice deposit with a rough surface made up of pits, knobs, and linear depressions on scales of tens of meters [1]. This roughness manifests as a series of bright and dark patches in visible images. Spectral data indicate that the surface of the NPRC is composed of large-grained (and therefore old) water ice. Due to the presence of this old ice, it is thought that the NPRC is in a current state of net loss of material [2]. The NPRC provides a link between the current martian climate and the historical climate recorded within the layers of the underlying north polar layered deposits. By characterizing and mapping the variations in surface texture of the NPRC, we seek to understand what factors (distance from the pole, GCM and mesoscale wind direction predictions, etc) are currently at work in resurfacing the deposit, and may have been at work in shaping the layers below. Maps of NPRC texture wavelength and orientation are being produced from HiRISE images. Two-dimensional Fourier analysis is performed upon a 256 meter x 256 meter region (corresponding to 512 x 512 pixels in 0.5 cm/pxl images, or 1024 x 1024 pixels in 0.25 cm/pxl images) within each image analyzed. The dominant wavelength of the resulting peak power spectrum corresponds to the average size of a pit-knob pair in the image, and so is a proxy for the scale of the surface roughness. The orientation of the surface roughness (i.e., the orientation of a chain of pits and mounds) is measured from a narrow range of wavelengths encompassing the dominant wavelength. We will report on how the dominant wavelengths and orientations of this surface texture vary with location and what that implies for the processes currently shaping this landscape. [1] P. C. Thomas et al, Nature 404, 161-164, 2000 [2]Y. Langevin et al, Science 307, 5715, 1581-1584, 2005.

Accurate object tracking system by integrating texture and depth cues

NASA Astrophysics Data System (ADS)

Chen, Ju-Chin; Lin, Yu-Hang

2016-03-01

A robust object tracking system that is invariant to object appearance variations and background clutter is proposed. Multiple instance learning with a boosting algorithm is applied to select discriminant texture information between the object and background data. Additionally, depth information, which is important to distinguish the object from a complicated background, is integrated. We propose two depth-based models that can compensate texture information to cope with both appearance variants and background clutter. Moreover, in order to reduce the risk of drifting problem increased for the textureless depth templates, an update mechanism is proposed to select more precise tracking results to avoid incorrect model updates. In the experiments, the robustness of the proposed system is evaluated and quantitative results are provided for performance analysis. Experimental results show that the proposed system can provide the best success rate and has more accurate tracking results than other well-known algorithms.

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

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�

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.

Computer-assisted liver graft steatosis assessment via learning-based texture analysis.

PubMed

Moccia, Sara; Mattos, Leonardo S; Patrini, Ilaria; Ruperti, Michela; Poté, Nicolas; Dondero, Federica; Cauchy, François; Sepulveda, Ailton; Soubrane, Olivier; De Momi, Elena; Diaspro, Alberto; Cesaretti, Manuela

2018-05-23

Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process. Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was [Formula: see text]. This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern ([Formula: see text]), and gray-level co-occurrence matrix ([Formula: see text]) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance. With the best-performing feature set ([Formula: see text]) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively. This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone

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.

Prediction of Chemoresistance in Women Undergoing Neo-Adjuvant Chemotherapy for Locally Advanced Breast Cancer: Volumetric Analysis of First-Order Textural Features Extracted from Multiparametric MRI.

PubMed

Panzeri, M M; Losio, C; Della Corte, A; Venturini, E; Ambrosi, A; Panizza, P; De Cobelli, F

2018-01-01

To assess correlations between volumetric first-order texture parameters on baseline MRI and pathological response after neoadjuvant chemotherapy (NAC) for locally advanced breast cancer (BC). 69 patients with locally advanced BC candidate to neoadjuvant chemotherapy underwent MRI within 4 weeks from the start of therapeutic regimen. T2, DWI, and DCE sequences were analyzed and maps were generated for Apparent Diffusion Coefficient (ADC), T2 signal intensity, and the following dynamic parameters: k -trans, peak enhancement, area under curve (AUC), time to maximal enhancement (TME), wash-in rate, and washout rate. Volumetric analysis of these parameters was performed, yielding a histogram analysis including first-order texture kinetics (percentiles, maximum value, minimum value, range, standard deviation, mean, median, mode, skewness, and kurtosis). Finally, correlations between these values and response to NAC (evaluated on the surgical specimen according to RECIST 1.1 criteria) were assessed. Out of 69 tumors, 33 (47.8%) achieved complete pathological response, 26 (37.7%) partial response, and 10 (14.5%) no response. Higher levels of AUCmax ( p value = 0.0338), AUCrange ( p value = 0.0311), and TME 75 ( p value = 0.0452) and lower levels of washout 10 ( p value = 0.0417), washout 20 ( p value = 0.0138), washout 25 ( p value = 0.0114), and washout 30 ( p value = 0.05) were predictive of noncomplete response. Histogram-derived texture analysis of MRI images allows finding quantitative parameters predictive of nonresponse to NAC in women affected by locally advanced BC.

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.

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

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...

Soil texture and climatc conditions for biocrust growth limitation: a meta analysis

NASA Astrophysics Data System (ADS)

Fischer, Thomas; Subbotina, Mariia

2015-04-01

Along with afforestation, attempts have been made to combat desertification by managing soil crusts, and is has been reported that recovery rates of biocrusts are dependent on many factors, including the type, severity, and extent of disturbance; structure of the vascular plant community; conditions of adjoining substrates; availability of inoculation material; and climate during and after disturbance (Belnap & Eldridge 2001). Because biological soil crusts are known to be more stable on and to prefer fine substrates (Belnap 2001), the question arises as to how successful crust management practices can be applied to coarser soil. In previous studies we observed similar crust biomasses on finer soils under arid and on coarser soils under temperate conditions. We hypothesized that the higher water holding capacity of finer substrates would favor crust development, and that the amount of silt and clay in the substrate that is required for enhanced crust development would vary with changes in climatic conditions. In a global meta study, climatic and soil texture threshold values promoting BSC growth were derived. While examining literature sources, it became evident that the amount of studies to be incorporated into this meta analysis was reversely related to the amount of common environmental parameters they share. We selected annual mean precipitaion, mean temperature and the amount of silt and clay as driving variables for crust growth. Response variable was the "relative crust biomass", which was computed per literature source as the ratio between each individual crust biomass value of the given study to the study maximum value reported. We distinguished lichen, green algal, cyanobacterial and moss crusts. To quantify threshold conditions at which crust biomass responded to differences in texture and climate, we (I) determined correlations between bioclimatic variables, (II) calculated linear models to determine the effect of typical climatic variables with soil

A Study of Feature Extraction Using Divergence Analysis of Texture Features

NASA Technical Reports Server (NTRS)

Hallada, W. A.; Bly, B. G.; Boyd, R. K.; Cox, S.

1982-01-01

An empirical study of texture analysis for feature extraction and classification of high spatial resolution remotely sensed imagery (10 meters) is presented in terms of specific land cover types. The principal method examined is the use of spatial gray tone dependence (SGTD). The SGTD method reduces the gray levels within a moving window into a two-dimensional spatial gray tone dependence matrix which can be interpreted as a probability matrix of gray tone pairs. Haralick et al (1973) used a number of information theory measures to extract texture features from these matrices, including angular second moment (inertia), correlation, entropy, homogeneity, and energy. The derivation of the SGTD matrix is a function of: (1) the number of gray tones in an image; (2) the angle along which the frequency of SGTD is calculated; (3) the size of the moving window; and (4) the distance between gray tone pairs. The first three parameters were varied and tested on a 10 meter resolution panchromatic image of Maryville, Tennessee using the five SGTD measures. A transformed divergence measure was used to determine the statistical separability between four land cover categories forest, new residential, old residential, and industrial for each variation in texture parameters.

Quantitative characterization of material surface — Application to Ni + Mo electrolytic composite coatings

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

Kubisztal, J., E-mail: julian.kubisztal@us.edu.pl

A new approach to numerical analysis of maps of material surface has been proposed and discussed in detail. It was concluded that the roughness factor RF and the root mean square roughness S{sub q} show a saturation effect with increasing size of the analysed maps what allows determining the optimal map dimension representative of the examined material. A quantitative method of determining predominant direction of the surface texture based on the power spectral density function is also proposed and discussed. The elaborated method was applied in surface analysis of Ni + Mo composite coatings. It was shown that co-deposition ofmore » molybdenum particles in nickel matrix leads to an increase in surface roughness. In addition, a decrease in size of the embedded Mo particles in Ni matrix causes an increase of both the surface roughness and the surface texture. It was also stated that the relation between the roughness factor and the double layer capacitance C{sub dl} of the studied coatings is linear and allows determining the double layer capacitance of the smooth nickel electrode. - Highlights: •Optimization of the procedure for the scanning of the material surface •Quantitative determination of the surface roughness and texture intensity •Proposition of the parameter describing privileged direction of the surface texture •Determination of the double layer capacitance of the smooth electrode.« less

Textured catalysts and methods of making textured catalysts

DOEpatents

Werpy, Todd [West Richland, WA; Frye, Jr., John G.; Wang, Yong [Richland, WA; Zacher, Alan H [Kennewick, WA

2007-03-06

A textured catalyst having a hydrothermally-stable support, a metal oxide and a catalyst component is described. Methods of conducting aqueous phase reactions that are catalyzed by a textured catalyst are also described. The invention also provides methods of making textured catalysts and methods of making chemical products using a textured catalyst.

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.

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

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.

PubMed

Hu, Leland S; Ning, Shuluo; Eschbacher, Jennifer M; Gaw, Nathan; Dueck, Amylou C; Smith, Kris A; Nakaji, Peter; Plasencia, Jonathan; Ranjbar, Sara; Price, Stephen J; Tran, Nhan; Loftus, Joseph; Jenkins, Robert; O'Neill, Brian P; Elmquist, William; Baxter, Leslie C; Gao, Fei; Frakes, David; Karis, John P; Zwart, Christine; Swanson, Kristin R; Sarkaria, Jann; Wu, Teresa; Mitchell, J Ross; Li, Jing

2015-01-01

Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set. We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients). Multi-parametric MRI and texture analysis can help characterize and visualize GBM's spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Efficient rolling texture predictions and texture-sensitive properties of α-uranium foils

DOE PAGES

Steiner, Matthew A.; Klein, Robert W.; Calhoun, Christopher A.; ...

2017-01-01

Here, finite element (FE) analysis was used to simulate the strain history of an α-uranium foil during cold-rolling, with the sheet modeled as an isotropic elastoplastic continuum. The resulting strain history was then used as input for a viscoplastic self-consistent (VPSC) polycrystal plasticity model to simulate crystallographic texture evolution. Mid-plane textures predicted via the combined FE→VPSC approach show alignment of the (010) poles along the rolling direction (RD), and the (001) poles along the normal direction (ND) with a symmetric splitting along RD. The surface texture is similar to that of the mid-plane, but with a shear-induced asymmetry that favorsmore » one of the RD split features of the (001) pole figure. Both the mid-plane and surface textures predicted by the FE→VPSC approach agree with published experimental results for cold-rolled α-uranium plates, as well as predictions made by a more computationally intensive full-field crystal plasticity based finite element model. α-uranium foils produced by cold-rolling must typically undergo a final recrystallization anneal to restore ductility prior to their final application, resulting in significant texture evolution from the cold-rolled plate deformation texture. Using the texture measured from a foil in the final recrystallized state, coefficients of the thermal expansion and elastic stiffness tensors were calculated using a thermo-elastic self-consistent model, and the anisotropic yield loci and flow curves along the RD, TD, and ND were predicted using the VPSC code.« less

Efficient rolling texture predictions and texture-sensitive properties of α-uranium foils

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

Steiner, Matthew A.; Klein, Robert W.; Calhoun, Christopher A.

Here, finite element (FE) analysis was used to simulate the strain history of an α-uranium foil during cold-rolling, with the sheet modeled as an isotropic elastoplastic continuum. The resulting strain history was then used as input for a viscoplastic self-consistent (VPSC) polycrystal plasticity model to simulate crystallographic texture evolution. Mid-plane textures predicted via the combined FE→VPSC approach show alignment of the (010) poles along the rolling direction (RD), and the (001) poles along the normal direction (ND) with a symmetric splitting along RD. The surface texture is similar to that of the mid-plane, but with a shear-induced asymmetry that favorsmore » one of the RD split features of the (001) pole figure. Both the mid-plane and surface textures predicted by the FE→VPSC approach agree with published experimental results for cold-rolled α-uranium plates, as well as predictions made by a more computationally intensive full-field crystal plasticity based finite element model. α-uranium foils produced by cold-rolling must typically undergo a final recrystallization anneal to restore ductility prior to their final application, resulting in significant texture evolution from the cold-rolled plate deformation texture. Using the texture measured from a foil in the final recrystallized state, coefficients of the thermal expansion and elastic stiffness tensors were calculated using a thermo-elastic self-consistent model, and the anisotropic yield loci and flow curves along the RD, TD, and ND were predicted using the VPSC code.« less

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.

Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.

PubMed

Zheng, Yuanjie; Keller, Brad M; Ray, Shonket; Wang, Yan; Conant, Emily F; Gee, James C; Kontos, Despina

2015-07-01

performance. The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05). The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors.

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

Towards true 3D textural analysis; using your crystal mush wisely.

NASA Astrophysics Data System (ADS)

Jerram, D. A.; Morgan, D. J.; Pankhurst, M. J.

2014-12-01

The crystal cargo that is found in volcanic and plutonic rocks contains a wealth of information about magmatic mush processes, crystallisation history, crystal entrainment and recycling. Phenocryst populations predominantly record episodes of growth/nucleation and bulk geochemical changes within an evolving crystal-melt body. Ante- and xeno-crysts provide useful clues to the nature of mush interaction with wall rock and with principal magma(s). Furthermore, crystal evolutions (core to rim) record pathways through pressure, temperature and compositional space. These can often illustrate complex recycling within systems, describing the plumbing architecture. Understanding this architecture underpins our knowledge of how igneous systems can interact with the crust, grow, freeze, re-mobilise and prime for eruption. Initially, 2D studies produced corrected 3D crystal size distributions to help provide information about nucleation and residence times. It immediately became clear that crystal shape is an important factor in determining the confidence placed upon 3D reconstructions of 2D data. Additionally studies utilised serial sections of medium- to coarse-grain-size populations which allowed 3D reconstruction using modelling software to be improved, since size and shape etc. can be directly constrained. Finally the advent of textural studies using X-ray tomography has revolutionised the way in which we can inspect the crystal cargo in mushy systems, allowing us to image in great detail crystal packing arrangements, 3D CSDs, shapes and orientations etc. The latest most innovative studies use X-ray micro-computed tomography to rapidly characterise chemical populations within the crystal cargo, adding a further dimension to this approach, and implies the ability to untangle magmatic chemical components to better understand their individual and combined evolution. In this contribution key examples of the different types of textural analysis techniques in 2D and 3D

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.

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.

Texture-based segmentation and analysis of emphysema depicted on CT images

NASA Astrophysics Data System (ADS)

Tan, Jun; Zheng, Bin; Wang, Xingwei; Lederman, Dror; Pu, Jiantao; Sciurba, Frank C.; Gur, David; Leader, J. Ken

2011-03-01

In this study we present a texture-based method of emphysema segmentation depicted on CT examination consisting of two steps. Step 1, a fractal dimension based texture feature extraction is used to initially detect base regions of emphysema. A threshold is applied to the texture result image to obtain initial base regions. Step 2, the base regions are evaluated pixel-by-pixel using a method that considers the variance change incurred by adding a pixel to the base in an effort to refine the boundary of the base regions. Visual inspection revealed a reasonable segmentation of the emphysema regions. There was a strong correlation between lung function (FEV1%, FEV1/FVC, and DLCO%) and fraction of emphysema computed using the texture based method, which were -0.433, -.629, and -0.527, respectively. The texture-based method produced more homogeneous emphysematous regions compared to simple thresholding, especially for large bulla, which can appear as speckled regions in the threshold approach. In the texture-based method, single isolated pixels may be considered as emphysema only if neighboring pixels meet certain criteria, which support the idea that single isolated pixels may not be sufficient evidence that emphysema is present. One of the strength of our complex texture-based approach to emphysema segmentation is that it goes beyond existing approaches that typically extract a single or groups texture features and individually analyze the features. We focus on first identifying potential regions of emphysema and then refining the boundary of the detected regions based on texture patterns.

Efficient rolling texture predictions and texture-sensitive thermomechanical properties of α-uranium foils

NASA Astrophysics Data System (ADS)

Steiner, Matthew A.; Klein, Robert W.; Calhoun, Christopher A.; Knezevic, Marko; Garlea, Elena; Agnew, Sean R.

2017-11-01

Finite element (FE) analysis was used to simulate the strain history of an α-uranium foil during cold straight-rolling, with the sheet modeled as an isotropic elastoplastic continuum. The resulting strain history was then used as input for a viscoplastic self-consistent (VPSC) polycrystal plasticity model to simulate crystallographic texture evolution. Mid-plane textures predicted via the combined FE→VPSC approach show alignment of the (010) poles along the rolling direction (RD), and the (001) poles along the normal direction (ND) with a symmetric splitting along RD. The surface texture is similar to that of the mid-plane, but with a shear-induced asymmetry that favors one of the RD split features of the (001) pole figure. Both the mid-plane and surface textures predicted by the FE→VPSC approach agree with published experimental results for cold straight-rolled α-uranium plates, as well as predictions made by a more computationally intensive full-field crystal plasticity based finite element model. α-uranium foils produced by cold-rolling must typically undergo a recrystallization anneal to restore ductility prior to their final application, resulting in significant texture evolution from the cold-rolled plate deformation texture. Using the texture measured from a foil in the final recrystallized state, coefficients of thermal expansion and the elastic stiffness tensors were calculated using a thermo-elastic self-consistent model, and the anisotropic yield loci and flow curves along the RD, TD, and ND were predicted using the VPSC code.

An Analysis of the Max-Min Texture Measure.

DTIC Science & Technology

1982-01-01

PANC 33 D2 Confusion Matrices for Scene A, IR 34 D3 Confusion Matrices for Scene B, PANC 35 D4 Confusion Matrices for Scene B, IR 36 D5 Confusion...Matrices for Scene C, PANC 37 D6 Confusion Matrices for Scene C, IR 38 D7 Confusion Matrices for Scene E, PANC 39 D8 Confusion Matrices for Scene E, IR 40...D9 Confusion Matrices for Scene H, PANC 41 DIO Confusion Matrices for Scene H, JR 42 3 .D 10CnuinMtie o cn ,IR4 AN ANALYSIS OF THE MAX-MIN TEXTURE

Coexistence of colossal stress and texture gradients in sputter deposited nanocrystalline ultra-thin metal films

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

Kuru, Yener; Welzel, Udo; Mittemeijer, Eric J.

2014-12-01

This paper demonstrates experimentally that ultra-thin, nanocrystalline films can exhibit coexisting colossal stress and texture depth gradients. Their quantitative determination is possible by X-ray diffraction experiments. Whereas a uniform texture by itself is known to generally cause curvature in so-called sin{sup 2}ψ plots, it is shown that the combined action of texture and stress gradients provides a separate source of curvature in sin{sup 2}ψ plots (i.e., even in cases where a uniform texture does not induce such curvature). On this basis, the texture and stress depth profiles of a nanocrystalline, ultra-thin (50 nm) tungsten film could be determined.

Prediction of Chemoresistance in Women Undergoing Neo-Adjuvant Chemotherapy for Locally Advanced Breast Cancer: Volumetric Analysis of First-Order Textural Features Extracted from Multiparametric MRI

PubMed Central

Losio, C.; Della Corte, A.; Venturini, E.; Ambrosi, A.; Panizza, P.; De Cobelli, F.

2018-01-01

Purpose To assess correlations between volumetric first-order texture parameters on baseline MRI and pathological response after neoadjuvant chemotherapy (NAC) for locally advanced breast cancer (BC). Materials and Methods 69 patients with locally advanced BC candidate to neoadjuvant chemotherapy underwent MRI within 4 weeks from the start of therapeutic regimen. T2, DWI, and DCE sequences were analyzed and maps were generated for Apparent Diffusion Coefficient (ADC), T2 signal intensity, and the following dynamic parameters: k-trans, peak enhancement, area under curve (AUC), time to maximal enhancement (TME), wash-in rate, and washout rate. Volumetric analysis of these parameters was performed, yielding a histogram analysis including first-order texture kinetics (percentiles, maximum value, minimum value, range, standard deviation, mean, median, mode, skewness, and kurtosis). Finally, correlations between these values and response to NAC (evaluated on the surgical specimen according to RECIST 1.1 criteria) were assessed. Results Out of 69 tumors, 33 (47.8%) achieved complete pathological response, 26 (37.7%) partial response, and 10 (14.5%) no response. Higher levels of AUCmax (p value = 0.0338), AUCrange (p value = 0.0311), and TME75 (p value = 0.0452) and lower levels of washout10 (p value = 0.0417), washout20 (p value = 0.0138), washout25 (p value = 0.0114), and washout30 (p value = 0.05) were predictive of noncomplete response. Conclusion Histogram-derived texture analysis of MRI images allows finding quantitative parameters predictive of nonresponse to NAC in women affected by locally advanced BC. PMID:29853811

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.

Mechanical Model Analysis for Quantitative Evaluation of Liver Fibrosis Based on Ultrasound Tissue Elasticity Imaging

NASA Astrophysics Data System (ADS)

Shiina, Tsuyoshi; Maki, Tomonori; Yamakawa, Makoto; Mitake, Tsuyoshi; Kudo, Masatoshi; Fujimoto, Kenji

2012-07-01

Precise evaluation of the stage of chronic hepatitis C with respect to fibrosis has become an important issue to prevent the occurrence of cirrhosis and to initiate appropriate therapeutic intervention such as viral eradication using interferon. Ultrasound tissue elasticity imaging, i.e., elastography can visualize tissue hardness/softness, and its clinical usefulness has been studied to detect and evaluate tumors. We have recently reported that the texture of elasticity image changes as fibrosis progresses. To evaluate fibrosis progression quantitatively on the basis of ultrasound tissue elasticity imaging, we introduced a mechanical model of fibrosis progression and simulated the process by which hepatic fibrosis affects elasticity images and compared the results with those clinical data analysis. As a result, it was confirmed that even in diffuse diseases like chronic hepatitis, the patterns of elasticity images are related to fibrous structural changes caused by hepatic disease and can be used to derive features for quantitative evaluation of fibrosis stage.

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.

Textural analysis of optical coherence tomography skin images: quantitative differentiation between healthy and cancerous tissues

NASA Astrophysics Data System (ADS)

Adabi, Saba; Conforto, Silvia; Hosseinzadeh, Matin; Noe, Shahryar; Daveluy, Steven; Mehregan, Darius; Nasiriavanaki, Mohammadreza

2017-02-01

Optical Coherence Tomography (OCT) offers real-time high-resolution three-dimensional images of tissue microstructures. In this study, we used OCT skin images acquired from ten volunteers, neither of whom had any skin conditions addressing the features of their anatomic location. OCT segmented images are analyzed based on their optical properties (attenuation coefficient) and textural image features e.g., contrast, correlation, homogeneity, energy, entropy, etc. Utilizing the information and referring to their clinical insight, we aim to make a comprehensive computational model for the healthy skin. The derived parameters represent the OCT microstructural morphology and might provide biological information for generating an atlas of normal skin from different anatomic sites of human skin and may allow for identification of cell microstructural changes in cancer patients. We then compared the parameters of healthy samples with those of abnormal skin and classified them using a linear Support Vector Machines (SVM) with 82% accuracy.

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

Hybrid texture generator

NASA Astrophysics Data System (ADS)

Miyata, Kazunori; Nakajima, Masayuki

1995-04-01

A method is given for synthesizing a texture by using the interface of a conventional drawing tool. The majority of conventional texture generation methods are based on the procedural approach, and can generate a variety of textures that are adequate for generating a realistic image. But it is hard for a user to imagine what kind of texture will be generated simply by looking at its parameters. Furthermore, it is difficult to design a new texture freely without a knowledge of all the procedures for texture generation. Our method offers a solution to these problems, and has the following four merits: First, a variety of textures can be obtained by combining a set of feature lines and attribute functions. Second, data definitions are flexible. Third, the user can preview a texture together with its feature lines. Fourth, people can design their own textures interactively and freely by using the interface of a conventional drawing tool. For users who want to build this texture generation method into their own programs, we also give the language specifications for generating a texture. This method can interactively provide a variety of textures, and can also be used for typographic design.

Two-dimensional numerical simulation of boron diffusion for pyramidally textured silicon

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

Ma, Fa-Jun, E-mail: Fajun.Ma@nus.edu.sg; Duttagupta, Shubham; Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576

2014-11-14

Multidimensional numerical simulation of boron diffusion is of great relevance for the improvement of industrial n-type crystalline silicon wafer solar cells. However, surface passivation of boron diffused area is typically studied in one dimension on planar lifetime samples. This approach neglects the effects of the solar cell pyramidal texture on the boron doping process and resulting doping profile. In this work, we present a theoretical study using a two-dimensional surface morphology for pyramidally textured samples. The boron diffusivity and segregation coefficient between oxide and silicon in simulation are determined by reproducing measured one-dimensional boron depth profiles prepared using different boronmore » diffusion recipes on planar samples. The established parameters are subsequently used to simulate the boron diffusion process on textured samples. The simulated junction depth is found to agree quantitatively well with electron beam induced current measurements. Finally, chemical passivation on planar and textured samples is compared in device simulation. Particularly, a two-dimensional approach is adopted for textured samples to evaluate chemical passivation. The intrinsic emitter saturation current density, which is only related to Auger and radiative recombination, is also simulated for both planar and textured samples. The differences between planar and textured samples are discussed.« less

A procedure for classifying textural facies in gravel‐bed rivers

USGS Publications Warehouse

Buffington, John M.; Montgomery, David R.

1999-01-01

Textural patches (i.e., grain‐size facies) are commonly observed in gravel‐bed channels and are of significance for both physical and biological processes at subreach scales. We present a general framework for classifying textural patches that allows modification for particular study goals, while maintaining a basic degree of standardization. Textures are classified using a two‐tier system of ternary diagrams that identifies the relative abundance of major size classes and subcategories of the dominant size. An iterative procedure of visual identification and quantitative grain‐size measurement is used. A field test of our classification indicates that it affords reasonable statistical discrimination of median grain size and variance of bed‐surface textures. We also explore the compromise between classification simplicity and accuracy. We find that statistically meaningful textural discrimination requires use of both tiers of our classification. Furthermore, we find that simplified variants of the two‐tier scheme are less accurate but may be more practical for field studies which do not require a high level of textural discrimination or detailed description of grain‐size distributions. Facies maps provide a natural template for stratifying other physical and biological measurements and produce a retrievable and versatile database that can be used as a component of channel monitoring efforts.

Scanning electron microscopy combined with image processing technique: Analysis of microstructure, texture and tenderness in Semitendinous and Gluteus Medius bovine muscles.

PubMed

Pieniazek, Facundo; Messina, Valeria

2016-11-01

In this study the effect of freeze drying on the microstructure, texture, and tenderness of Semitendinous and Gluteus Medius bovine muscles were analyzed applying Scanning Electron Microscopy combined with image analysis. Samples were analyzed by Scanning Electron Microscopy at different magnifications (250, 500, and 1,000×). Texture parameters were analyzed by Texture analyzer and by image analysis. Tenderness by Warner-Bratzler shear force. Significant differences (p < 0.05) were obtained for image and instrumental texture features. A linear trend with a linear correlation was applied for instrumental and image features. Image texture features calculated from Gray Level Co-occurrence Matrix (homogeneity, contrast, entropy, correlation and energy) at 1,000× in both muscles had high correlations with instrumental features (chewiness, hardness, cohesiveness, and springiness). Tenderness showed a positive correlation in both muscles with image features (energy and homogeneity). Combing Scanning Electron Microscopy with image analysis can be a useful tool to analyze quality parameters in meat.Summary SCANNING 38:727-734, 2016. © 2016 Wiley Periodicals, Inc. © Wiley Periodicals, Inc.

POWTEX - A new High-Intensity Powder and Texture Diffractometer at FRM II, Garching Germany

NASA Astrophysics Data System (ADS)

Walter, J. M.; Brückel, T.; Dronskowski, R.; Hansen, B. T.; Houben, A.; Klein, H.; Leiss, B.; Vollbrecht, A.; Sowa, H.

2009-05-01

In recent years, neutron diffraction has become a routine tool in Geoscience for experimental high-field (HP/HT/HH) powder diffraction and for the quantitative analysis of the crystallographic preferred orientation (CPO). Quantitative texture analysis is e.g. involved in the research fields of fabric development in mono- and polyphase rocks, deformation histories and kinematics during mountain building processes and the characterization of flow kinematics in lava flows. Secondly the quantitative characterization of anisotropic physical properties of both rock and analogue materials is conducted by bulk texture measurements of sometimes larger sample volumes. This is easily achievable by neutron diffraction due to the high penetration capabilities of the neutrons. The resulting geoscientific need for increased measuring time at neutron diffraction facilities with the corresponding technical characteristics and equipment will in future be satisfied by this high-intensity diffractometer at the neutron research reactor FRM II in Garching, Germany. It will be built by a consortium of groups from the RWTH Aachen, Forschungszentrum Jülich and the University of Göttingen, who will also operate the instrument. The diffractometer will be optimized to high intensities (flux) with an equivalent sufficient resolution for polyphase rocks. Furthermore a broad range of d-values (0.5 to 15 Å) will be measurable. The uniqueness of this instrument is the geoscientific focus on different sample environments for in situ-static and deformation experiments (stress, strain and annealing/recrystallisation) and (U)HP/(U)HT experiments. A LP/LT or atmospheric-P deformation rig for in situ-deformation experiments on ice, halite or rock analogue materials is planned, to allow in situ-measurements of the texture development during deformation and annealing. Additionally a uniaxial HT/MP deformation apparatus for salt deformation experiments and an adapted Griggs- type deformation rig are

SU-F-R-36: Validating Quantitative Radiomic Texture Features for Oncologic PET: A Digital Phantom Study

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

Yang, F; Yang, Y; Young, L

Purpose: Radiomic texture features derived from the oncologic PET have recently been brought under intense investigation within the context of patient stratification and treatment outcome prediction in a variety of cancer types; however, their validity has not yet been examined. This work is aimed to validate radiomic PET texture metrics through the use of realistic simulations in the ground truth setting. Methods: Simulation of FDG-PET was conducted by applying the Zubal phantom as an attenuation map to the SimSET software package that employs Monte Carlo techniques to model the physical process of emission imaging. A total of 15 irregularly-shaped lesionsmore » featuring heterogeneous activity distribution were simulated. For each simulated lesion, 28 texture features in relation to the intensity histograms (GLIH), grey-level co-occurrence matrices (GLCOM), neighborhood difference matrices (GLNDM), and zone size matrices (GLZSM) were evaluated and compared with their respective values extracted from the ground truth activity map. Results: In reference to the values from the ground truth images, texture parameters appearing on the simulated data varied with a range of 0.73–3026.2% for GLIH-based, 0.02–100.1% for GLCOM-based, 1.11–173.8% for GLNDM-based, and 0.35–66.3% for GLZSM-based. For majority of the examined texture metrics (16/28), their values on the simulated data differed significantly from those from the ground truth images (P-value ranges from <0.0001 to 0.04). Features not exhibiting significant difference comprised of GLIH-based standard deviation, GLCO-based energy and entropy, GLND-based coarseness and contrast, and GLZS-based low gray-level zone emphasis, high gray-level zone emphasis, short zone low gray-level emphasis, long zone low gray-level emphasis, long zone high gray-level emphasis, and zone size nonuniformity. Conclusion: The extent to which PET imaging disturbs texture appearance is feature-dependent and could be substantial. It

Combining multiple features for color texture classification

NASA Astrophysics Data System (ADS)

Cusano, Claudio; Napoletano, Paolo; Schettini, Raimondo

2016-11-01

The analysis of color and texture has a long history in image analysis and computer vision. These two properties are often considered as independent, even though they are strongly related in images of natural objects and materials. Correlation between color and texture information is especially relevant in the case of variable illumination, a condition that has a crucial impact on the effectiveness of most visual descriptors. We propose an ensemble of hand-crafted image descriptors designed to capture different aspects of color textures. We show that the use of these descriptors in a multiple classifiers framework makes it possible to achieve a very high classification accuracy in classifying texture images acquired under different lighting conditions. A powerful alternative to hand-crafted descriptors is represented by features obtained with deep learning methods. We also show how the proposed combining strategy hand-crafted and convolutional neural networks features can be used together to further improve the classification accuracy. Experimental results on a food database (raw food texture) demonstrate the effectiveness of the proposed strategy.

Descriptive sensory and instrumental texture profile analysis of woody breast in marinated chicken.

PubMed

Aguirre, M E; Owens, C M; Miller, R K; Alvarado, C Z

2018-04-01

The broiler industry is currently experiencing a muscle anomaly referred to as "woody breast," and the effect of different cooking methods on the marination properties of severe woody breast (SWB) has not yet been reported. This study compared the texture attributes of marinated (injected) normal (NOR) and SWB using a convection oven and a flat-top grill. The objectives were 1) to develop and validate a descriptive texture attribute panel with 6 trained panelists using a 16-point scale and 2) to evaluate the instrumental texture profile analysis (TPA) using a texture analyzer. Sixty-four NOR and SWB were obtained from a commercial facility. Fillet color (L*, a*, b*) and pH were measured before marination. In each of 2 trials, the breast muscles were injected in bulk with 15% brine (0.48 STPP, 0.55% NaCl, final concentration), and marinade retention was determined after 20 minutes. The meat was vacuum packaged, stored at -20°C (7 d sensory; 29 d TPA) and then thawed (4°C, 24 h). Fillets were cooked to 73°C on a flat-top grill (176°C) or in an oven (176°C), and cook loss % was determined. Panelist samples (2 × 2 cm) and TPA samples (4 × 2 cm) were cut into 3 pieces. Color and pH were higher for SWB than NOR fillets (P < 0.05). Marinade retention was 83.21% for NOR meat and 59.23% for SWB meat. The flat-top grill method resulted in higher cook loss than oven (P < 0.05). SWB had higher cook loss when compared to NOR (P < 0.05). Sensory texture descriptors springiness, hardness, denseness, cohesiveness, fracturability, fibrousness, crunchiness, and chewiness were higher for SWB than NOR fillets (P < 0.05). TPA attributes also showed higher values for SWB compared to NOR (P < 0.05). No differences in texture were found between the grill and oven for sensory and TPA attributes. In summary, marinated SWB has significant texture differences when compared to NOR, regardless of cooking method.

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.

Validation of CBCT for the computation of textural biomarkers

NASA Astrophysics Data System (ADS)

Paniagua, Beatriz; Ruellas, Antonio C.; Benavides, Erika; Marron, Steve; Wolford, Larry; Cevidanes, Lucia

2015-03-01

Osteoarthritis (OA) is associated with significant pain and 42.6% of patients with TMJ disorders present with evidence of TMJ OA. However, OA diagnosis and treatment remain controversial, since there are no clear symptoms of the disease. The subchondral bone in the TMJ is believed to play a major role in the progression of OA. We hypothesize that the textural imaging biomarkers computed in high resolution Conebeam CT (hr- CBCT) and μCT scans are comparable. The purpose of this study is to test the feasibility of computing textural imaging biomarkers in-vivo using hr-CBCT, compared to those computed in μCT scans as our Gold Standard. Specimens of condylar bones obtained from condylectomies were scanned using μCT and hr- CBCT. Nine different textural imaging biomarkers (four co-occurrence features and five run-length features) from each pair of μCT and hr-CBCT were computed and compared. Pearson correlation coefficients were computed to compare textural biomarkers values of μCT and hr-CBCT. Four of the nine computed textural biomarkers showed a strong positive correlation between biomarkers computed in μCT and hr-CBCT. Higher correlations in Energy and Contrast, and in GLN (grey-level non-uniformity) and RLN (run length non-uniformity) indicate quantitative texture features can be computed reliably in hr-CBCT, when compared with μCT. The textural imaging biomarkers computed in-vivo hr-CBCT have captured the structure, patterns, contrast between neighboring regions and uniformity of healthy and/or pathologic subchondral bone. The ability to quantify bone texture non-invasively now makes it possible to evaluate the progression of subchondral bone alterations, in TMJ OA.

Validation of CBCT for the computation of textural biomarkers

PubMed Central

Paniagua, Beatriz; Ruellas, Antonio Carlos; Benavides, Erika; Marron, Steve; Woldford, Larry; Cevidanes, Lucia

2015-01-01

Osteoarthritis (OA) is associated with significant pain and 42.6% of patients with TMJ disorders present with evidence of TMJ OA. However, OA diagnosis and treatment remain controversial, since there are no clear symptoms of the disease. The subchondral bone in the TMJ is believed to play a major role in the progression of OA. We hypothesize that the textural imaging biomarkers computed in high resolution Conebeam CT (hr-CBCT) and μCT scans are comparable. The purpose of this study is to test the feasibility of computing textural imaging biomarkers in-vivo using hr-CBCT, compared to those computed in μCT scans as our Gold Standard. Specimens of condylar bones obtained from condylectomies were scanned using μCT and hr-CBCT. Nine different textural imaging biomarkers (four co-occurrence features and five run-length features) from each pair of μCT and hr-CBCT were computed and compared. Pearson correlation coefficients were computed to compare textural biomarkers values of μCT and hr-CBCT. Four of the nine computed textural biomarkers showed a strong positive correlation between biomarkers computed in μCT and hr-CBCT. Higher correlations in Energy and Contrast, and in GLN (grey-level non-uniformity) and RLN (run length non-uniformity) indicate quantitative texture features can be computed reliably in hr-CBCT, when compared with μCT. The textural imaging biomarkers computed in-vivo hr-CBCT have captured the structure, patterns, contrast between neighboring regions and uniformity of healthy and/or pathologic subchondral bone. The ability to quantify bone texture non-invasively now makes it possible to evaluate the progression of subchondral bone alterations, in TMJ OA. PMID:26085710

Validation of CBCT for the computation of textural biomarkers.

PubMed

Paniagua, Beatriz; Ruellas, Antonio Carlos; Benavides, Erika; Marron, Steve; Woldford, Larry; Cevidanes, Lucia

2015-03-17

Osteoarthritis (OA) is associated with significant pain and 42.6% of patients with TMJ disorders present with evidence of TMJ OA. However, OA diagnosis and treatment remain controversial, since there are no clear symptoms of the disease. The subchondral bone in the TMJ is believed to play a major role in the progression of OA. We hypothesize that the textural imaging biomarkers computed in high resolution Conebeam CT (hr-CBCT) and μCT scans are comparable. The purpose of this study is to test the feasibility of computing textural imaging biomarkers in-vivo using hr-CBCT, compared to those computed in μCT scans as our Gold Standard. Specimens of condylar bones obtained from condylectomies were scanned using μCT and hr-CBCT. Nine different textural imaging biomarkers (four co-occurrence features and five run-length features) from each pair of μCT and hr-CBCT were computed and compared. Pearson correlation coefficients were computed to compare textural biomarkers values of μCT and hr-CBCT. Four of the nine computed textural biomarkers showed a strong positive correlation between biomarkers computed in μCT and hr-CBCT. Higher correlations in Energy and Contrast, and in GLN (grey-level non-uniformity) and RLN (run length non-uniformity) indicate quantitative texture features can be computed reliably in hr-CBCT, when compared with μCT. The textural imaging biomarkers computed in-vivo hr-CBCT have captured the structure, patterns, contrast between neighboring regions and uniformity of healthy and/or pathologic subchondral bone. The ability to quantify bone texture non-invasively now makes it possible to evaluate the progression of subchondral bone alterations, in TMJ OA.

Catalogue of X-Ray Texture Data for Al-Cu-Li Alloy 1460, 2090, 2096 and 2195 Near-Net-Shape Extrusions, Sheet and Plate

NASA Technical Reports Server (NTRS)

Hales, Stephen J.; Hafley, Robert A.; Alexa, Joel A.

1998-01-01

The effect of crystallographic texture on the mechanical properties of near-net-shape extrusions is of major interest ff these products are to find application in launch vehicle or aircraft structures. The objective of this research was to produce a catalogue containing quantitative texture information for extruded product, sheet and plate. The material characterized was extracted from wide, integrally stiffened panels fabricated from the Al-Cu-Li alloys 1460, 2090, 2096 and 2195. The textural characteristics of sheet and plate products of the same alloys were determined for comparison purposes. The approach involved using X-ray diffraction to generate pole figures in combination with orientation distribution function analysis. The data were compiled as a function of location in the extruded cross-sections and the variation in the major deformation- and recrystallization-related texture components was identified.

Model for understanding consumer textural food choice

PubMed Central

Jeltema, Melissa; Beckley, Jacqueline; Vahalik, Jennifer

2015-01-01

The current paradigm for developing products that will match the marketing messaging is flawed because the drivers of product choice and satisfaction based on texture are misunderstood. Qualitative research across 10 years has led to the thesis explored in this research that individuals have a preferred way to manipulate food in their mouths (i.e., mouth behavior) and that this behavior is a major driver of food choice, satisfaction, and the desire to repurchase. Texture, which is currently thought to be a major driver of product choice, is a secondary factor, and is important only in that it supports the primary driver—mouth behavior. A model for mouth behavior is proposed and the qualitative research supporting the identification of different mouth behaviors is presented. The development of a trademarked typing tool for characterizing mouth behavior is described along with quantitative substantiation of the tool's ability to group individuals by mouth behavior. The use of these four groups to understand textural preferences and the implications for a variety of areas including product design and weight management are explored. PMID:25987995

Model for understanding consumer textural food choice.

PubMed

Jeltema, Melissa; Beckley, Jacqueline; Vahalik, Jennifer

2015-05-01

The current paradigm for developing products that will match the marketing messaging is flawed because the drivers of product choice and satisfaction based on texture are misunderstood. Qualitative research across 10 years has led to the thesis explored in this research that individuals have a preferred way to manipulate food in their mouths (i.e., mouth behavior) and that this behavior is a major driver of food choice, satisfaction, and the desire to repurchase. Texture, which is currently thought to be a major driver of product choice, is a secondary factor, and is important only in that it supports the primary driver-mouth behavior. A model for mouth behavior is proposed and the qualitative research supporting the identification of different mouth behaviors is presented. The development of a trademarked typing tool for characterizing mouth behavior is described along with quantitative substantiation of the tool's ability to group individuals by mouth behavior. The use of these four groups to understand textural preferences and the implications for a variety of areas including product design and weight management are explored.

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.

[Study on objectively evaluating skin aging according to areas of skin texture].

PubMed

Shan, Gaixin; Gan, Ping; He, Ling; Sun, Lu; Li, Qiannan; Jiang, Zheng; He, Xiangqian

2015-02-01

Skin aging principles play important roles in skin disease diagnosis, the evaluation of skin cosmetic effect, forensic identification and age identification in sports competition, etc. This paper proposes a new method to evaluate the skin aging objectively and quantitatively by skin texture area. Firstly, the enlarged skin image was acquired. Then, the skin texture image was segmented by using the iterative threshold method, and the skin ridge image was extracted according to the watershed algorithm. Finally, the skin ridge areas of the skin texture were extracted. The experiment data showed that the average areas of skin ridges, of both men and women, had a good correlation with age (the correlation coefficient r of male was 0.938, and the correlation coefficient r of female was 0.922), and skin texture area and age regression curve showed that the skin texture area increased with age. Therefore, it is effective to evaluate skin aging objectively by the new method presented in this paper.

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.

Elasticity study of textured barium strontium titanate thin films by X-ray diffraction and laser acoustic waves

NASA Astrophysics Data System (ADS)

Chaabani, Anouar; Njeh, Anouar; Donner, Wolfgang; Klein, Andreas; Hédi Ben Ghozlen, Mohamed

2017-05-01

Ba0.65Sr0.35TiO3 (BST) thin films of 300 nm were deposited on Pt(111)/TiO2/SiO2/Si(001) substrates by radio frequency magnetron sputtering. Two thin films with different (111) and (001) fiber textures were prepared. X-ray diffraction was applied to measure texture. The raw pole figure data were further processed using the MTEX quantitative texture analysis software for plotting pole figures and calculating elastic constants and Young’s modulus from the orientation distribution function (ODF) for each type of textured fiber. The calculated elastic constants were used in the theoretical studies of surface acoustics waves (SAW) propagating in two types of multilayered BST systems. Theoretical dispersion curves were plotted by the application of the ordinary differential equation (ODE) and the stiffness matrix methods (SMM). A laser acoustic waves (LAW) technique was applied to generate surface acoustic waves (SAW) propagating in the BST films, and from a recursive process, the effective Young’s modulus are determined for the two samples. These methods are used to extract and compare elastic properties of two types of BST films, and quantify the influence of texture on the direction-dependent Young’s modulus.

[18]Fluorodeoxyglucose Positron Emission Tomography for the Textural Features of Cervical Cancer Associated with Lymph Node Metastasis and Histological Type.

PubMed

Shen, Wei-Chih; Chen, Shang-Wen; Liang, Ji-An; Hsieh, Te-Chun; Yen, Kuo-Yang; Kao, Chia-Hung

2017-09-01

In this study, we investigated the correlation between the lymph node (LN) status or histological types and textural features of cervical cancers on 18 F-fluorodeoxyglucose positron emission tomography/computed tomography. We retrospectively reviewed the imaging records of 170 patients with International Federation of Gynecology and Obstetrics stage IB-IVA cervical cancer. Four groups of textural features were studied in addition to the maximum standardized uptake value (SUV max ), metabolic tumor volume, and total lesion glycolysis (TLG). Moreover, we studied the associations between the indices and clinical parameters, including the LN status, clinical stage, and histology. Receiver operating characteristic curves were constructed to evaluate the optimal predictive performance among the various textural indices. Quantitative differences were determined using the Mann-Whitney U test. Multivariate logistic regression analysis was performed to determine the independent factors, among all the variables, for predicting LN metastasis. Among all the significant indices related to pelvic LN metastasis, homogeneity derived from the gray-level co-occurrence matrix (GLCM) was the sole independent predictor. By combining SUV max , the risk of pelvic LN metastasis can be scored accordingly. The TLG mean was the independent feature of positive para-aortic LNs. Quantitative differences between squamous and nonsquamous histology can be determined using short-zone emphasis (SZE) from the gray-level size zone matrix (GLSZM). This study revealed that in patients with cervical cancer, pelvic or para-aortic LN metastases can be predicted by using textural feature of homogeneity from the GLCM and TLG mean, respectively. SZE from the GLSZM is the sole feature associated with quantitative differences between squamous and nonsquamous histology.

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.

Surface scaling analysis of textured MgO thin films fabricated by energetic particle self-assisted deposition

NASA Astrophysics Data System (ADS)

Feng, Feng; Zhang, Xiangsong; Qu, Timing; Liu, Binbin; Huang, Junlong; Li, Jun; Xiao, Shaozhu; Han, Zhenghe; Feng, Pingfa

2018-04-01

In the fabrication of a high-temperature superconducting coated conductor, the surface roughness and texture of buffer layers can significantly affect the epitaxially grown superconductor layer. A biaxially textured MgO buffer layer fabricated by ion beam assisted deposition (IBAD) is widely used in the coated conductor manufacture due to its low thickness requirement. In our previous study, a new method called energetic particle self-assisted deposition (EPSAD), which employed only a sputtering deposition apparatus without an ion source, was proposed for fabricating biaxially textured MgO films on non-textured substrates. In this study, our aim was to investigate the deposition mechanism of EPSAD-MgO thin films. The behavior of the surface roughness (evaluated by Rq) was studied using atomic force microscopy (AFM) measurements with three scan scales, while the in-plane and out-of-plane textures were measured using X-ray diffraction (XRD). It was found that the variations of surface roughness and textures along with the increase in the thickness of EPSAD-MgO samples were very similar to those of IBAD-MgO reported in the literature, revealing the similarity of their deposition mechanisms. Moreover, fractal geometry was utilized to conduct the scaling analysis of EPSAD-MgO film's surface. Different scaling behaviors were found in two scale ranges, and the indications of the fractal properties in different scale ranges were discussed.

Texture analysis of computed tomographic images in osteoporotic patients with sinus lift bone graft reconstruction.

PubMed

Marchand-Libouban, Hélène; Guillaume, Bernard; Bellaiche, Norbert; Chappard, Daniel

2013-05-01

Bone implants are now widely used to replace missing teeth. Bone grafting (sinus lift) is a very useful way to increase the bone volume of the maxilla in patients with bone atrophy. There is a 6- to 9-month delay for the receiver grafted site to heal before the implants can be placed. Computed tomography is a useful method to measure the amount of remaining bone before implantation and to evaluate the quality of the receiver bone at the end of the healing period. Texture analysis is a non-invasive method useful to characterize bone microarchitecture on X-ray images. Ten patients in which a sinus lift surgery was necessary before implantation were analyzed in the present study. All had a bone reconstruction with a combination of a biomaterial (beta tricalcium phosphate) and autograft bone harvested at the chin. Computed tomographic images were obtained before grafting (t0), at mid-interval (t1, 4.2 ± 0.7 months) and before implant placement (t2, 9.2 ± 0.6 months). Texture analysis was done with the run-length method. A significant increase of texture parameters at t1 reflected a gain of homogeneity due to the graft and the beginning of bone remodeling. At t2, some parameters remained high and corresponded to the persistence of bone trabeculae while the resorption of biomaterials was identified by other parameters which tended to return to pregraft values. Texture analysis identified changes during the healing of the receiver site. The method is known to correlate with microarchitectural changes in bone and could be a useful approach to characterized osseointegrated grafts.

Quinoa Starch Characteristics and Their Correlations with the Texture Profile Analysis (TPA) of Cooked Quinoa.

PubMed

Wu, Geyang; Morris, Craig F; Murphy, Kevin M

2017-10-01

Starch characteristics significantly influence the functionality and end-use quality of cereals and pseudo-cereals. This study examined the composition and properties of starch from 11 pure varieties and 2 commercial samples of quinoa in relationship to the texture of cooked quinoa. Nearly all starch properties and characteristics differed among these samples. Results showed that total starch content of seeds ranged from 53.2 to 75.1 g/100 g apparent amylose content ranged from 2.7% to 16.9%; total amylose ranged from 4.7% to 17.3%; and the degree of amylose-lipid complex ranged from 3.4% to 43.3%. Amylose leaching ranged from 31 mg/100 g starch in "Japanese Strain" to 862 mg/100 g starch in "49ALC." "Japanese Strain" starch also exhibited the highest water solubility (4.5%) and the lowest swelling power (17). α-Amylase activity in "1ESP," "Col.#6197," "Japanese Strain," "QQ63," "Yellow Commercial," and "Red Commercial" (0.03 to 0.09 CU) were significantly lower than the levels of the other quinoa samples (0.20 to 1.16 CU). Additionally, gel texture, thermal properties, and pasting properties of quinoa starches were investigated. Lastly, correlation analysis showed that the quinoa samples with higher amylose content tended to yield harder, stickier, more cohesive, more gummy, and more chewy texture after cooking. A higher degree of amylose-lipid complex and amylose leaching were associated with softer and less chewy cooked quinoa TPA texture. Higher starch enthalpy correlated with firmer, more adhesive, more cohesive, and chewier texture. In sum, starch plays a significant role in the texture of cooked quinoa. The research determined starch characteristics among a diverse set of pure quinoa varieties and commercial samples, and identified the relationships between starch properties and cooked quinoa texture. The results can help breeders and food manufacturers to understand better the relationships among quinoa starch characteristics, cooked quinoa texture, and

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.

Mining textural knowledge in biological images: Applications, methods and trends.

PubMed

Di Cataldo, Santa; Ficarra, Elisa

2017-01-01

Texture analysis is a major task in many areas of computer vision and pattern recognition, including biological imaging. Indeed, visual textures can be exploited to distinguish specific tissues or cells in a biological sample, to highlight chemical reactions between molecules, as well as to detect subcellular patterns that can be evidence of certain pathologies. This makes automated texture analysis fundamental in many applications of biomedicine, such as the accurate detection and grading of multiple types of cancer, the differential diagnosis of autoimmune diseases, or the study of physiological processes. Due to their specific characteristics and challenges, the design of texture analysis systems for biological images has attracted ever-growing attention in the last few years. In this paper, we perform a critical review of this important topic. First, we provide a general definition of texture analysis and discuss its role in the context of bioimaging, with examples of applications from the recent literature. Then, we review the main approaches to automated texture analysis, with special attention to the methods of feature extraction and encoding that can be successfully applied to microscopy images of cells or tissues. Our aim is to provide an overview of the state of the art, as well as a glimpse into the latest and future trends of research in this area.

PREFACE: 17th International Conference on Textures of Materials (ICOTOM 17)

NASA Astrophysics Data System (ADS)

Skrotzki, Werner; Oertel, Carl-Georg

2015-04-01

related research on microstructures 9. Texture-induced anisotropy 10. Insight through new experimental methods 11. Technological applications of texture studies 12. Other new developments and future trends related to the field While there was large interest in the topics 2, 3 and 8, contributions to topic 7 were much less than expected. ICOTOM 17 attracted 266 scientists from 34 countries with about one third of the participants being students. This is a very good ratio showing that we could attract the young generation. There have been 216 oral and 76 poster presentations, three of which received a poster award. It is our pleasure to thank the members of the International ICOTOM Committee for their valuable help, especially for proposing and choosing the 15 plenary speakers as well as the distinguished scientist of the texture community for the "Bunge Award". 130 papers were submitted for publication in the proceedings, 116 were accepted after reviewing. We would like to express our thanks to all referees for their efficient and prompt efforts. We acknowledge particularly support from the German Research Society (DFG) and the City of Dresden. We are also grateful for industrial support from Bruker Nano GmbH, Oxford Instruments GmbH, Ametek GmbH / EDAX, Labosoft S.C., PANalytical GmbH and IOP Publishing. Finally we thank all members of the National Organizing Committee, Intercom Dresden and Conwerk / Laboratory Ten for the excellent organization of ICOTOM 17 and the very pleasant collaboration. On the first day of the conference three tutorials have been offered. Each of them has been attended by about 30 participants. 1. Texture-aided residual stress identification system (TARSIuS) (organized by Prof. Dr. J. Bonarski and Mr. B. Kania) 2. MTEX - MATLAB toolbox for quantitative texture analysis (organized by Dr. R. Hielscher and Mr. F. Bachmann) 3. Grain boundary engineering (organized by Prof. N. Bozzolo and Prof. Dr. A.D. Rollett) A highlight of ICOTOM 17 was the

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.

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

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.

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

Texture classification of lung computed tomography images

NASA Astrophysics Data System (ADS)

Pheng, Hang See; Shamsuddin, Siti M.

2013-03-01

Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.

Ion beam texturing

NASA Technical Reports Server (NTRS)

Hudson, W. R.

1976-01-01

A microscopic surface texture is created by sputter etching a surface while simultaneously sputter depositing a lower sputter yield material onto the surface. A xenon ion beam source has been used to perform this texturing process on samples as large as three centimeters in diameter. Ion beam textured surface structures have been characterized with SEM photomicrographs for a large number of materials including Cu, Al, Si, Ti, Ni, Fe, Stainless steel, Au, and Ag. Surfaces have been textured using a variety of low sputter yield materials - Ta, Mo, Nb, and Ti. The initial stages of the texture creation have been documented, and the technique of ion beam sputter removal of any remaining deposited material has been studied. A number of other texturing parameters have been studied such as the variation of the texture with ion beam power, surface temperature, and the rate of texture growth with sputter etching time.

Coupled X-ray computed tomography and grey level co-occurrence matrices as a method for quantification of mineralogy and texture in 3D

NASA Astrophysics Data System (ADS)

Jardine, M. A.; Miller, J. A.; Becker, M.

2018-02-01

Texture is one of the most basic descriptors used in the geological sciences. The value derived from textural characterisation extends into engineering applications associated with mining, mineral processing and metal extraction where quantitative textural information is required for models predicting the response of the ore through a particular process. This study extends the well-known 2D grey level co-occurrence matrices methodology into 3D as a method for image analysis of 3D x-ray computed tomography grey scale volumes of drill core. Subsequent interrogation of the information embedded within the grey level occurrence matrices (GLCM) indicates they are sensitive to changes in mineralogy and texture of samples derived from a magmatic nickel sulfide ore. The position of the peaks in the GLCM is an indication of the relative density (specific gravity, SG) of the minerals and when interpreted using a working knowledge of the mineralogy of the ore presented a means to determine the relative abundance of the sulfide minerals (SG > 4), dense silicate minerals (SG > 3), and lighter silicate minerals (SG < 3). The spread of the peaks in the GLCM away from the diagonal is an indication of the degree of grain boundary interaction with wide peaks representing fine grain sizes and narrow peaks representing coarse grain sizes. The method lends itself to application as part of a generic methodology for routine use on large XCT volumes providing quantitative, timely, meaningful and automated information on mineralogy and texture in 3D.

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.

Measurement of kinaesthetic properties of in-brine table olives by microstructure of fracture surface, sensory evaluation and texture profile analysis (TPA).

PubMed

Lanza, Barbara; Amoruso, Filomena

2018-02-02

A series of transformations occur in olive fruit both during ripening and processing. In particular, significant changes in the microstructural composition affect the flavour, texture, nutrients and overall quality of the end product. Texture is one of the sensory quality attributes of greatest importance to consumer acceptance. In the present work, kinaesthetic properties of in-brine table olives of three cultivars of Olea europaea L. (Bella di Cerignola, Peranzana and Taggiasca cvs) were provided by several measurements of olive tissue texture by sensory, rheological and microstructural approaches. Olives at the same stage of ripening and processed with the same technology, but belonging to different cultivars, showed significant differences at microstructural, sensorial and rheological levels. To describe the relationship between the three variables, multiple regression analysis and principal component analysis were chosen. Differences in microstructure were closely related both in terms of hardness measured by texture profile analysis and hardness measured by sensory analysis. The information provided could be an aid for screening and training of a sensory panel. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

A comparison between Warner-Bratzler shear force measurement and texture profile analysis of meat and meat products: a review

NASA Astrophysics Data System (ADS)

Novaković, S.; Tomašević, I.

2017-09-01

Texture is one of the most important characteristics of meat and we can explain it as the human physiological-psychological awareness of a number of rheological and other properties of foods and their relations. In this paper, we discuss instrumental measurement of texture by Warner-Bratzler shear force (WBSF) and texture profile analysis (TPA). The conditions for using the device are detailed in WBSF measurements, and the influence of different parameters on the execution of the method and final results are shown. After that, the main disadvantages are reflected in the non-standardized method. Also, we introduce basic texture parameters which connect and separate TPA and WBSF methods and mention contemporary methods with their main advantage.

Spectral analysis of the stick-slip phenomenon in "oral" tribological texture evaluation.

PubMed

Sanahuja, Solange; Upadhyay, Rutuja; Briesen, Heiko; Chen, Jianshe

2017-08-01

"Oral" tribology has become a new paradigm in food texture studies to understand complex texture attributes, such as creaminess, oiliness, and astringency, which could not be successfully characterized by traditional texture analysis nor by rheology. Stick-slip effects resulting from intermittent sliding motion during kinetic friction of oral mucosa could constitute an additional determining factor of sensory perception where traditional friction coefficient values and their Stribeck regimes fail in predicting different lubricant (food bolus and saliva) behaviors. It was hypothesized that the observed jagged behavior of most sliding force curves are due to stick-slip effects and depend on test velocity, normal load, surface roughness as well as lubricant type. Therefore, different measurement set-ups were investigated: sliding velocities from 0.01 to 40 mm/s, loads of 0.5 and 2.5 N as well as a smooth and a textured silicone contact surface. Moreover, dry contact measurements were compared to model food systems, such as water, oil, and oil-in-water emulsions. Spectral analysis permitted to extract the distribution of stick-slip magnitudes for specific wave numbers, characterizing the occurrence of jagged force peaks per unit sliding distance, similar to frequencies per unit time. The spectral features were affected by all the above mentioned tested factors. Stick-slip created vibration frequencies in the range of those detected by oral mechanoreceptors (0.3-400 Hz). The study thus provides a new insight into the use of tribology in food psychophysics. Dynamic spectral analysis has been applied for the first time to the force-displacement curves in "oral" tribology. Analyzing the stick-slip phenomenon in the dynamic friction provides new information that is generally overlooked or confused with machine noise and which may help to understand friction-related sensory attributes. This approach allows us to differentiate samples that have similar friction coefficient

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.

Diagnostic performance of semi-quantitative and quantitative stress CMR perfusion analysis: a meta-analysis.

PubMed

van Dijk, R; van Assen, M; Vliegenthart, R; de Bock, G H; van der Harst, P; Oudkerk, M

2017-11-27

Stress cardiovascular magnetic resonance (CMR) perfusion imaging is a promising modality for the evaluation of coronary artery disease (CAD) due to high spatial resolution and absence of radiation. Semi-quantitative and quantitative analysis of CMR perfusion are based on signal-intensity curves produced during the first-pass of gadolinium contrast. Multiple semi-quantitative and quantitative parameters have been introduced. Diagnostic performance of these parameters varies extensively among studies and standardized protocols are lacking. This study aims to determine the diagnostic accuracy of semi- quantitative and quantitative CMR perfusion parameters, compared to multiple reference standards. Pubmed, WebOfScience, and Embase were systematically searched using predefined criteria (3272 articles). A check for duplicates was performed (1967 articles). Eligibility and relevance of the articles was determined by two reviewers using pre-defined criteria. The primary data extraction was performed independently by two researchers with the use of a predefined template. Differences in extracted data were resolved by discussion between the two researchers. The quality of the included studies was assessed using the 'Quality Assessment of Diagnostic Accuracy Studies Tool' (QUADAS-2). True positives, false positives, true negatives, and false negatives were subtracted/calculated from the articles. The principal summary measures used to assess diagnostic accuracy were sensitivity, specificity, andarea under the receiver operating curve (AUC). Data was pooled according to analysis territory, reference standard and perfusion parameter. Twenty-two articles were eligible based on the predefined study eligibility criteria. The pooled diagnostic accuracy for segment-, territory- and patient-based analyses showed good diagnostic performance with sensitivity of 0.88, 0.82, and 0.83, specificity of 0.72, 0.83, and 0.76 and AUC of 0.90, 0.84, and 0.87, respectively. In per territory

Automated Quantitative Rare Earth Elements Mineralogy by Scanning Electron Microscopy

NASA Astrophysics Data System (ADS)

Sindern, Sven; Meyer, F. Michael

2016-09-01

Increasing industrial demand of rare earth elements (REEs) stems from the central role they play for advanced technologies and the accelerating move away from carbon-based fuels. However, REE production is often hampered by the chemical, mineralogical as well as textural complexity of the ores with a need for better understanding of their salient properties. This is not only essential for in-depth genetic interpretations but also for a robust assessment of ore quality and economic viability. The design of energy and cost-efficient processing of REE ores depends heavily on information about REE element deportment that can be made available employing automated quantitative process mineralogy. Quantitative mineralogy assigns numeric values to compositional and textural properties of mineral matter. Scanning electron microscopy (SEM) combined with a suitable software package for acquisition of backscatter electron and X-ray signals, phase assignment and image analysis is one of the most efficient tools for quantitative mineralogy. The four different SEM-based automated quantitative mineralogy systems, i.e. FEI QEMSCAN and MLA, Tescan TIMA and Zeiss Mineralogic Mining, which are commercially available, are briefly characterized. Using examples of quantitative REE mineralogy, this chapter illustrates capabilities and limitations of automated SEM-based systems. Chemical variability of REE minerals and analytical uncertainty can reduce performance of phase assignment. This is shown for the REE phases parisite and synchysite. In another example from a monazite REE deposit, the quantitative mineralogical parameters surface roughness and mineral association derived from image analysis are applied for automated discrimination of apatite formed in a breakdown reaction of monazite and apatite formed by metamorphism prior to monazite breakdown. SEM-based automated mineralogy fulfils all requirements for characterization of complex unconventional REE ores that will become

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

Texture functions in image analysis: A computationally efficient solution

NASA Technical Reports Server (NTRS)

Cox, S. C.; Rose, J. F.

1983-01-01

A computationally efficient means for calculating texture measurements from digital images by use of the co-occurrence technique is presented. The calculation of the statistical descriptors of image texture and a solution that circumvents the need for calculating and storing a co-occurrence matrix are discussed. The results show that existing efficient algorithms for calculating sums, sums of squares, and cross products can be used to compute complex co-occurrence relationships directly from the digital image input.

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.

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

Uric acid versus non-uric acid urinary stones: differentiation with single energy CT texture analysis.

PubMed

Zhang, G-M-Y; Sun, H; Shi, B; Xu, M; Xue, H-D; Jin, Z-Y

2018-05-21

To evaluate the accuracy of computed tomography (CT) texture analysis (TA) to differentiate uric acid (UA) stones from non-UA stones on unenhanced CT in patients with urinary calculi with ex vivo Fourier transform infrared spectroscopy (FTIR) as the reference standard. Fourteen patients with 18 UA stones and 31 patients with 32 non-UA stones were included. All the patients had preoperative CT evaluation and subsequent surgical removal of the stones. CTTA was performed on CT images using commercially available research software. Each texture feature was evaluated using the non-parametric Mann-Whitney test. Receiver operating characteristic (ROC) curves were created and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different. The features were used to train support vector machine (SVM) classifiers. Diagnostic accuracy was evaluated. Compared to non-UA stones, UA stones had significantly lower mean, standard deviation and mean of positive pixels but higher kurtosis (p<0.001) on both unfiltered and filtered texture scales. There were no significant differences in entropy or skewness between UA and non-UA stones. The average SVM accuracy of texture features for differentiating UA from non-UA stones ranged from 88% to 92% (after 10-fold cross validation). A model incorporating standard deviation, skewness, and kurtosis from unfiltered texture scale images resulted in an AUC of 0.965±00.029 with a sensitivity of 94.4% and specificity of 93.7%. CTTA can be used to accurately differentiate UA stones from non-UA stones in vivo using unenhanced CT images. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

SU-F-R-32: Evaluation of MRI Acquisition Parameter Variations On Texture Feature Extraction Using ACR Phantom

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

Xie, Y; Wang, J; Wang, C

Purpose: To investigate the sensitivity of classic texture features to variations of MRI acquisition parameters. Methods: This study was performed on American College of Radiology (ACR) MRI Accreditation Program Phantom. MR imaging was acquired on a GE 750 3T scanner with XRM explain gradient, employing a T1-weighted images (TR/TE=500/20ms) with the following parameters as the reference standard: number of signal average (NEX) = 1, matrix size = 256×256, flip angle = 90°, slice thickness = 5mm. The effect of the acquisition parameters on texture features with and without non-uniformity correction were investigated respectively, while all the other parameters were keptmore » as reference standard. Protocol parameters were set as follows: (a). NEX = 0.5, 2 and 4; (b).Phase encoding steps = 128, 160 and 192; (c). Matrix size = 128×128, 192×192 and 512×512. 32 classic texture features were generated using the classic gray level run length matrix (GLRLM) and gray level co-occurrence matrix (GLCOM) from each image data set. Normalized range ((maximum-minimum)/mean) was calculated to determine variation among the scans with different protocol parameters. Results: For different NEX, 31 out of 32 texture features’ range are within 10%. For different phase encoding steps, 31 out of 32 texture features’ range are within 10%. For different acquisition matrix size without non-uniformity correction, 14 out of 32 texture features’ range are within 10%; for different acquisition matrix size with non-uniformity correction, 16 out of 32 texture features’ range are within 10%. Conclusion: Initial results indicated that those texture features that range within 10% are less sensitive to variations in T1-weighted MRI acquisition parameters. This might suggest that certain texture features might be more reliable to be used as potential biomarkers in MR quantitative image analysis.« less

Texture-Based Analysis of 100 MR Examinations of Head and Neck Tumors - Is It Possible to Discriminate Between Benign and Malignant Masses in a Multicenter Trial?

PubMed

Fruehwald-Pallamar, J; Hesselink, J R; Mafee, M F; Holzer-Fruehwald, L; Czerny, C; Mayerhoefer, M E

2016-02-01

To evaluate whether texture-based analysis of standard MRI sequences can help in the discrimination between benign and malignant head and neck tumors. The MR images of 100 patients with a histologically clarified head or neck mass, from two different institutions, were analyzed. Texture-based analysis was performed using texture analysis software, with region of interest measurements for 2 D and 3 D evaluation independently for all axial sequences. COC, RUN, GRA, ARM, and WAV features were calculated for all ROIs. 10 texture feature subsets were used for a linear discriminant analysis, in combination with k-nearest-neighbor classification. Benign and malignant tumors were compared with regard to texture-based values. There were differences in the images from different field-strength scanners, as well as from different vendors. For the differentiation of benign and malignant tumors, we found differences on STIR and T2-weighted images for 2 D, and on contrast-enhanced T1-TSE with fat saturation for 3 D evaluation. In a separate analysis of the subgroups 1.5 and 3 Tesla, more discriminating features were found. Texture-based analysis is a useful tool in the discrimination of benign and malignant tumors when performed on one scanner with the same protocol. We cannot recommend this technique for the use of multicenter studies with clinical data. 2 D/3 D texture-based analysis can be performed in head and neck tumors. Texture-based analysis can differentiate between benign and malignant masses. Analyzed MR images should originate from one scanner with an identical protocol. © Georg Thieme Verlag KG Stuttgart · New York.

Texture Fish

ERIC Educational Resources Information Center

Stone, Julie

2007-01-01

In an effort to provide an opportunity for her first graders to explore texture through an engaging subject, the author developed a three-part lesson that features fish in a mixed-media artwork: (1) Exploring Textured Paint; (2) Creating the Fish; and (3) Role Playing. In this lesson, students effectively explore texture through painting, drawing,…

Idiopathic Pulmonary Fibrosis: Data-driven Textural Analysis of Extent of Fibrosis at Baseline and 15-Month Follow-up.

PubMed

Humphries, Stephen M; Yagihashi, Kunihiro; Huckleberry, Jason; Rho, Byung-Hak; Schroeder, Joyce D; Strand, Matthew; Schwarz, Marvin I; Flaherty, Kevin R; Kazerooni, Ella A; van Beek, Edwin J R; Lynch, David A

2017-10-01

Purpose To evaluate associations between pulmonary function and both quantitative analysis and visual assessment of thin-section computed tomography (CT) images at baseline and at 15-month follow-up in subjects with idiopathic pulmonary fibrosis (IPF). Materials and Methods This retrospective analysis of preexisting anonymized data, collected prospectively between 2007 and 2013 in a HIPAA-compliant study, was exempt from additional institutional review board approval. The extent of lung fibrosis at baseline inspiratory chest CT in 280 subjects enrolled in the IPF Network was evaluated. Visual analysis was performed by using a semiquantitative scoring system. Computer-based quantitative analysis included CT histogram-based measurements and a data-driven textural analysis (DTA). Follow-up CT images in 72 of these subjects were also analyzed. Univariate comparisons were performed by using Spearman rank correlation. Multivariate and longitudinal analyses were performed by using a linear mixed model approach, in which models were compared by using asymptotic χ 2 tests. Results At baseline, all CT-derived measures showed moderate significant correlation (P < .001) with pulmonary function. At follow-up CT, changes in DTA scores showed significant correlation with changes in both forced vital capacity percentage predicted (ρ = -0.41, P < .001) and diffusing capacity for carbon monoxide percentage predicted (ρ = -0.40, P < .001). Asymptotic χ 2 tests showed that inclusion of DTA score significantly improved fit of both baseline and longitudinal linear mixed models in the prediction of pulmonary function (P < .001 for both). Conclusion When compared with semiquantitative visual assessment and CT histogram-based measurements, DTA score provides additional information that can be used to predict diminished function. Automatic quantification of lung fibrosis at CT yields an index of severity that correlates with visual assessment and functional change in subjects with IPF

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.

TU-F-CAMPUS-J-05: Effect of Uncorrelated Noise Texture On Computed Tomography Quantitative Image Features

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

Oliver, J; Budzevich, M; Moros, E

Purpose: To investigate the relationship between quantitative image features (i.e. radiomics) and statistical fluctuations (i.e. electronic noise) in clinical Computed Tomography (CT) using the standardized American College of Radiology (ACR) CT accreditation phantom and patient images. Methods: Three levels of uncorrelated Gaussian noise were added to CT images of phantom and patients (20) acquired in static mode and respiratory tracking mode. We calculated the noise-power spectrum (NPS) of the original CT images of the phantom, and of the phantom images with added Gaussian noise with means of 50, 80, and 120 HU. Concurrently, on patient images (original and noise-added images),more » image features were calculated: 14 shape, 19 intensity (1st order statistics from intensity volume histograms), 18 GLCM features (2nd order statistics from grey level co-occurrence matrices) and 11 RLM features (2nd order statistics from run-length matrices). These features provide the underlying structural information of the images. GLCM (size 128x128) was calculated with a step size of 1 voxel in 13 directions and averaged. RLM feature calculation was performed in 13 directions with grey levels binning into 128 levels. Results: Adding the electronic noise to the images modified the quality of the NPS, shifting the noise from mostly correlated to mostly uncorrelated voxels. The dramatic increase in noise texture did not affect image structure/contours significantly for patient images. However, it did affect the image features and textures significantly as demonstrated by GLCM differences. Conclusion: Image features are sensitive to acquisition factors (simulated by adding uncorrelated Gaussian noise). We speculate that image features will be more difficult to detect in the presence of electronic noise (an uncorrelated noise contributor) or, for that matter, any other highly correlated image noise. This work focuses on the effect of electronic, uncorrelated, noise and future work

Texture in steel plates revealed by laser ultrasonic surface acoustic waves velocity dispersion analysis.

PubMed

Yin, Anmin; Wang, Xiaochen; Glorieux, Christ; Yang, Quan; Dong, Feng; He, Fei; Wang, Yanlong; Sermeus, Jan; Van der Donck, Tom; Shu, Xuedao

2017-07-01

A photoacoustic, laser ultrasonics based approach in an Impulsive Stimulated Scattering (ISS) implementation was used to investigate the texture in polycrystalline metal plates. The angular dependence of the 'polycrystalline' surface acoustic wave (SAW) velocity measured along regions containing many grains was experimentally determined and compared with simulated results that were based on the angular dependence of the 'single grain' SAW velocity within single grains and the grain orientation distribution. The polycrystalline SAW velocities turn out to vary with texture. The SAW velocities and their angular variations for {110} texture were found to be larger than that the ones for {111} texture or the strong γ fiber texture. The SAW velocities for {001} texture were larger than for {111} texture, but with almost the same angular dependence. The results infer the feasibility to apply angular SAW angular dispersion measurements by laser ultrasonics for on-line texture monitoring. Copyright © 2017 Elsevier B.V. All rights reserved.

Statistical-techniques-based computer-aided diagnosis (CAD) using texture feature analysis: application in computed tomography (CT) imaging to fatty liver disease

NASA Astrophysics Data System (ADS)

Chung, Woon-Kwan; Park, Hyong-Hu; Im, In-Chul; Lee, Jae-Seung; Goo, Eun-Hoe; Dong, Kyung-Rae

2012-09-01

This paper proposes a computer-aided diagnosis (CAD) system based on texture feature analysis and statistical wavelet transformation technology to diagnose fatty liver disease with computed tomography (CT) imaging. In the target image, a wavelet transformation was performed for each lesion area to set the region of analysis (ROA, window size: 50 × 50 pixels) and define the texture feature of a pixel. Based on the extracted texture feature values, six parameters (average gray level, average contrast, relative smoothness, skewness, uniformity, and entropy) were determined to calculate the recognition rate for a fatty liver. In addition, a multivariate analysis of the variance (MANOVA) method was used to perform a discriminant analysis to verify the significance of the extracted texture feature values and the recognition rate for a fatty liver. According to the results, each texture feature value was significant for a comparison of the recognition rate for a fatty liver ( p < 0.05). Furthermore, the F-value, which was used as a scale for the difference in recognition rates, was highest in the average gray level, relatively high in the skewness and the entropy, and relatively low in the uniformity, the relative smoothness and the average contrast. The recognition rate for a fatty liver had the same scale as that for the F-value, showing 100% (average gray level) at the maximum and 80% (average contrast) at the minimum. Therefore, the recognition rate is believed to be a useful clinical value for the automatic detection and computer-aided diagnosis (CAD) using the texture feature value. Nevertheless, further study on various diseases and singular diseases will be needed in the future.

Origin of texture development in orthorhombic uranium

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

Zecevic, Miroslav; Knezevic, Marko; Beyerlein, Irene Jane

We study texture evolution of alpha-uranium (α-U) during plane strain compression and uniaxial compression to high strains at different temperatures. We combine a multiscale polycrystal constitutive model and detailed analysis of texture data to uncover the slip and twinning modes responsible for the formation of individual texture components. The analysis indicates that during plane strain compression, floor slip (001)[100] results in the formation of two pronounced {001}{001} texture peaks tilted 10–15° away from the normal toward the rolling direction. During both high-temperature (573 K) through-thickness compression and plane strain compression, the active slip modes are floor slip (001)[100] and chimneymore » slip 1/2{110} <11¯0> with slightly different ratios. {130} <31¯0> deformation twinning is profuse during rolling and in-plane compression and decreases with increasing temperature, but is not as active for through-thickness compression. Lastly, we comment on some similarities between rolling textures of α-U, which has a c/a ratio of 1.734, and those that develop in hexagonal close packed metals with similarly high c/a ratios like Zn (1.856) and Cd (1.885) and are dominated by basal slip.« less

Origin of texture development in orthorhombic uranium

DOE PAGES

Zecevic, Miroslav; Knezevic, Marko; Beyerlein, Irene Jane; ...

2016-04-09

We study texture evolution of alpha-uranium (α-U) during plane strain compression and uniaxial compression to high strains at different temperatures. We combine a multiscale polycrystal constitutive model and detailed analysis of texture data to uncover the slip and twinning modes responsible for the formation of individual texture components. The analysis indicates that during plane strain compression, floor slip (001)[100] results in the formation of two pronounced {001}{001} texture peaks tilted 10–15° away from the normal toward the rolling direction. During both high-temperature (573 K) through-thickness compression and plane strain compression, the active slip modes are floor slip (001)[100] and chimneymore » slip 1/2{110} <11¯0> with slightly different ratios. {130} <31¯0> deformation twinning is profuse during rolling and in-plane compression and decreases with increasing temperature, but is not as active for through-thickness compression. Lastly, we comment on some similarities between rolling textures of α-U, which has a c/a ratio of 1.734, and those that develop in hexagonal close packed metals with similarly high c/a ratios like Zn (1.856) and Cd (1.885) and are dominated by basal slip.« less

Potential Performance Criteria for Combat Ration Packs - Texture Profile Analysis

DTIC Science & Technology

2014-11-01

12 3.3.1 Apricot & coconut muesli bar...Figure 5 Texture vs aw of canned puddings stored at 30 °C for up to 730 days. 3.3 Muesli Bar The three muesli bars (apricot and coconut , tropical...Apricot & coconut muesli bar No significant changes were observed during storage for texture attributes, except at 40 °C for break strength and

Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT.

PubMed

Nardone, Valerio; Tini, Paolo; Nioche, Christophe; Mazzei, Maria Antonietta; Carfagno, Tommaso; Battaglia, Giuseppe; Pastina, Pierpaolo; Grassi, Roberta; Sebaste, Lucio; Pirtoli, Luigi

2018-06-01

Image texture analysis (TA) is a heterogeneity quantifying approach that cannot be appreciated by the naked eye, and early evidence suggests that TA has great potential in the field of oncology. The aim of this study is to evaluate parotid gland texture analysis (TA) combined with formal dosimetry as a factor for predicting severe late xerostomia in patients undergoing radiation therapy for head and neck cancers. We performed a retrospective analysis of patients treated at our Radiation Oncology Unit between January 2010 and December 2015, and selected the patients whose normal dose constraints for the parotid gland (mean dose < 26 Gy for the bilateral gland) could not be satisfied due to the presence of positive nodes close to the parotid glands. The parotid gland that showed the higher V30 was contoured on CT simulation and analysed with LifeX Software©. TA parameters included features of grey-level co-occurrence matrix (GLCM), neighbourhood grey-level dependence matrix (NGLDM), grey-level run length matrix (GLRLM), grey-level zone length matrix (GLZLM), sphericity, and indices from the grey-level histogram. We performed a univariate and multivariate analysis between all the texture parameters, the volume of the gland, the normal dose parameters (V30 and Mean Dose), and the development of severe chronic xerostomia. Seventy-eight patients were included and 25 (31%) developed chronic xerostomia. The TA parameters correlated with severe chronic xerostomia included V30 (OR 5.63), Dmean (OR 5.71), Kurtosis (OR 0.78), GLCM Correlation (OR 1.34), and RLNU (OR 2.12). The multivariate logistic regression showed a significant correlation between V30 (0.001), GLCM correlation (p: 0.026), RLNU (p: 0.011), and chronic xerostomia (p < 0.001, R2:0.664). Xerostomia represents an important cause of morbidity for head and neck cancer survivors after radiation therapy, and in certain cases normal dose constraints cannot be satisfied. Our results seem promising as texture

Dynamic Leidenfrost temperature on micro-textured surfaces: Acoustic wave absorption into thin vapor layer

NASA Astrophysics Data System (ADS)

Jerng, Dong Wook; Kim, Dong Eok

2018-01-01

The dynamic Leidenfrost phenomenon is governed by three types of pressure potentials induced via vapor hydrodynamics, liquid dynamic pressure, and the water hammer effect resulting from the generation of acoustic waves at the liquid-vapor interface. The prediction of the Leidenfrost temperature for a dynamic droplet needs quantitative evaluation and definition for each of the pressure fields. In particular, the textures on a heated surface can significantly affect the vapor hydrodynamics and the water hammer pressure. We present a quantitative model for evaluating the water hammer pressure on micro-textured surfaces taking into account the absorption of acoustic waves into the thin vapor layer. The model demonstrates that the strength of the acoustic flow into the liquid droplet, which directly contributes to the water hammer pressure, depends on the magnitude of the acoustic resistance (impedance) in the droplet and the vapor region. In consequence, the micro-textures of the surface and the increased spacing between them reduce the water hammer coefficient ( kh ) defined as the ratio of the acoustic flow into the droplet to total generated flow. Aided by numerical calculations that solve the laminar Navier-Stokes equation for the vapor flow, we also predict the dynamic Leidenfrost temperature on a micro-textured surface with reliable accuracy consistent with the experimental data.

Multi-fractal detrended texture feature for brain tumor classification

NASA Astrophysics Data System (ADS)

Reza, Syed M. S.; Mays, Randall; Iftekharuddin, Khan M.

2015-03-01

We propose a novel non-invasive brain tumor type classification using Multi-fractal Detrended Fluctuation Analysis (MFDFA) [1] in structural magnetic resonance (MR) images. This preliminary work investigates the efficacy of the MFDFA features along with our novel texture feature known as multifractional Brownian motion (mBm) [2] in classifying (grading) brain tumors as High Grade (HG) and Low Grade (LG). Based on prior performance, Random Forest (RF) [3] is employed for tumor grading using two different datasets such as BRATS-2013 [4] and BRATS-2014 [5]. Quantitative scores such as precision, recall, accuracy are obtained using the confusion matrix. On an average 90% precision and 85% recall from the inter-dataset cross-validation confirm the efficacy of the proposed method.

MRI-Based Texture Analysis to Differentiate Sinonasal Squamous Cell Carcinoma from Inverted Papilloma.

PubMed

Ramkumar, S; Ranjbar, S; Ning, S; Lal, D; Zwart, C M; Wood, C P; Weindling, S M; Wu, T; Mitchell, J R; Li, J; Hoxworth, J M

2017-05-01

Because sinonasal inverted papilloma can harbor squamous cell carcinoma, differentiating these tumors is relevant. The objectives of this study were to determine whether MR imaging-based texture analysis can accurately classify cases of noncoexistent squamous cell carcinoma and inverted papilloma and to compare this classification performance with neuroradiologists' review. Adult patients who had inverted papilloma or squamous cell carcinoma resected were eligible (coexistent inverted papilloma and squamous cell carcinoma were excluded). Inclusion required tumor size of >1.5 cm and preoperative MR imaging with axial T1, axial T2, and axial T1 postcontrast sequences. Five well-established texture analysis algorithms were applied to an ROI from the largest tumor cross-section. For a training dataset, machine-learning algorithms were used to identify the most accurate model, and performance was also evaluated in a validation dataset. On the basis of 3 separate blinded reviews of the ROI, isolated tumor, and entire images, 2 neuroradiologists predicted tumor type in consensus. The inverted papilloma ( n = 24) and squamous cell carcinoma ( n = 22) cohorts were matched for age and sex, while squamous cell carcinoma tumor volume was larger ( P = .001). The best classification model achieved similar accuracies for training (17 squamous cell carcinomas, 16 inverted papillomas) and validation (7 squamous cell carcinomas, 6 inverted papillomas) datasets of 90.9% and 84.6%, respectively ( P = .537). For the combined training and validation cohorts, the machine-learning accuracy (89.1%) was better than that of the neuroradiologists' ROI review (56.5%, P = .0004) but not significantly different from the neuroradiologists' review of the tumors (73.9%, P = .060) or entire images (87.0%, P = .748). MR imaging-based texture analysis has the potential to differentiate squamous cell carcinoma from inverted papilloma and may, in the future, provide incremental information to the

Multi-spectral texture analysis for IED detection

NASA Astrophysics Data System (ADS)

Petersson, Henrik; Gustafsson, David

2016-10-01

The use of Improvised Explosive Devices (IEDs) has increased significantly over the world and is a globally widespread phenomenon. Although measures can be taken to anticipate and prevent the opponent's ability to deploy IEDs, detection of IEDs will always be a central activity. There is a wide range of different sensors that are useful but also simple means, such as a pair of binoculars, can be crucial to detect IEDs in time. Disturbed earth (disturbed soil), such as freshly dug areas, dumps of clay on top of smooth sand or depressions in the ground, could be an indication of a buried IED. This paper brie y describes how a field trial was set-up to provide a realistic data set on a road section containing areas with disturbed soil due to buried IEDs. The road section was imaged using a forward looking land-based sensor platform consisting of visual imaging sensors together with long-, mid-, and shortwave infrared imaging sensors. The paper investigates the presence of discriminatory information in surface texture comparing areas with disturbed against undisturbed soil. The investigation is conducted for the different wavelength bands available. To extract features that describe texture, image processing tools such as 'Histogram of Oriented Gradients', 'Local Binary Patterns', 'Lacunarity', 'Gabor Filtering' and 'Co-Occurence' is used. It is found that texture as characterized here may provide discriminatory information to detect disturbed soil, but the signatures we found are weak and can not be used alone in e.g. a detector system.

Some distinguishing characteristics of contour and texture phenomena in images

NASA Technical Reports Server (NTRS)

Jobson, Daniel J.

1992-01-01

The development of generalized contour/texture discrimination techniques is a central element necessary for machine vision recognition and interpretation of arbitrary images. Here, the visual perception of texture, selected studies of texture analysis in machine vision, and diverse small samples of contour and texture are all used to provide insights into the fundamental characteristics of contour and texture. From these, an experimental discrimination scheme is developed and tested on a battery of natural images. The visual perception of texture defined fine texture as a subclass which is interpreted as shading and is distinct from coarse figural similarity textures. Also, perception defined the smallest scale for contour/texture discrimination as eight to nine visual acuity units. Three contour/texture discrimination parameters were found to be moderately successful for this scale discrimination: (1) lightness change in a blurred version of the image, (2) change in lightness change in the original image, and (3) percent change in edge counts relative to local maximum.

Tensile properties of cooked meat sausages and their correlation with texture profile analysis (TPA) parameters and physico-chemical characteristics.

PubMed

Herrero, A M; de la Hoz, L; Ordóñez, J A; Herranz, B; Romero de Ávila, M D; Cambero, M I

2008-11-01

The possibilities of using breaking strength (BS) and energy to fracture (EF) for monitoring textural properties of some cooked meat sausages (chopped, mortadella and galantines) were studied. Texture profile analysis (TPA), folding test and physico-chemical measurements were also performed. Principal component analysis enabled these meat products to be grouped into three textural profiles which showed significant (p<0.05) differences mainly for BS, hardness, adhesiveness and cohesiveness. Multivariate analysis indicated that BS, EF and TPA parameters were correlated (p<0.05) for every individual meat product (chopped, mortadella and galantines) and all products together. On the basis of these results, TPA parameters could be used for constructing regression models to predict BS. The resulting regression model for all cooked meat products was BS=-0.160+6.600∗cohesiveness-1.255∗adhesiveness+0.048∗hardness-506.31∗springiness (R(2)=0.745, p<0.00005). Simple linear regression analysis showed significant coefficients of determination between BS (R(2)=0.586, p<0.0001) versus folding test grade (FG) and EF versus FG (R(2)=0.564, p<0.0001).

Probing Majorana neutrino textures at DUNE

NASA Astrophysics Data System (ADS)

Bora, Kalpana; Borah, Debasish; Dutta, Debajyoti

2017-10-01

We study the possibility of probing different texture zero neutrino mass matrices at the long baseline neutrino experiment DUNE, particularly focusing on its sensitivity to the octant of atmospheric mixing angle θ23 and leptonic Dirac C P phase δcp. Assuming a diagonal charged lepton basis and Majorana nature of light neutrinos, we first classify the possible light neutrino mass matrices with one and two texture zeros and then numerically evaluate the parameter space which satisfies the texture zero conditions. Apart from using the latest global fit 3 σ values of neutrino oscillation parameters, we also use the latest bound on the sum of absolute neutrino masses (∑i |mi|) from the Planck mission data and the updated bound on effective neutrino mass Me e from neutrinoless double beta decay (0 ν β β ) experiments to find the allowed Majorana texture zero mass matrices. For the allowed texture zero mass matrices from all these constraints, we then feed the corresponding light neutrino parameter values satisfying the texture zero conditions into the numerical analysis in order to study the capability of DUNE to allow or exclude them once it starts taking data. We find that DUNE will be able to exclude some of these texture zero mass matrices which restrict (θ23-δcp) to a very specific range of values, depending on the values of the parameters that nature has chosen.

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.

Effect of high hydrostatic pressure on the color and texture parameters of refrigerated Caiman (Caiman crocodilus yacare) tail meat.

PubMed

Canto, A C V C S; Lima, B R C C; Cruz, A G; Lázaro, C A; Freitas, D G C; Faria, Jose A F; Torrezan, R; Freitas, M Q; Silva, T P J

2012-07-01

The effect of applying high hydrostatic pressure (HHP) on the instrumental parameters of color and texture and sensory characteristics of alligator meat were evaluated. Samples of alligator tail meat were sliced, vacuum-packed, pressurized and distributed into four groups: control, treated with 200 MPa/10 min, 300 MPa/10 min and 400 MPa/10 min, then stored at 4°C±1°C for 45 days. Instrumental color, texture profile and a sensory profiling using quantitative descriptive analysis were carried out on the 1st, 15th, 30th and 45th days of storage. HHP was shown to affect the color and texture of the product, and the sensory descriptors (p<0.05). The results suggest that high pressure is a promising technology for the processing of alligator meat, especially low pressures (200 MPa) which can have positive effects on the quality of the product. Copyright © 2012 Elsevier Ltd. All rights reserved.

TU-AB-BRA-04: Quantitative Radiomics: Sensitivity of PET Textural Features to Image Acquisition and Reconstruction Parameters Implies the Need for Standards

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

Nyflot, MJ; Yang, F; Byrd, D

Purpose: Despite increased use of heterogeneity metrics for PET imaging, standards for metrics such as textural features have yet to be developed. We evaluated the quantitative variability caused by image acquisition and reconstruction parameters on PET textural features. Methods: PET images of the NEMA IQ phantom were simulated with realistic image acquisition noise. 35 features based on intensity histograms (IH), co-occurrence matrices (COM), neighborhood-difference matrices (NDM), and zone-size matrices (ZSM) were evaluated within lesions (13, 17, 22, 28, 33 mm diameter). Variability in metrics across 50 independent images was evaluated as percent difference from mean for three phantom girths (850,more » 1030, 1200 mm) and two OSEM reconstructions (2 iterations, 28 subsets, 5 mm FWHM filtration vs 6 iterations, 28 subsets, 8.6 mm FWHM filtration). Also, patient sample size to detect a clinical effect of 30% with Bonferroni-corrected α=0.001 and 95% power was estimated. Results: As a class, NDM features demonstrated greatest sensitivity in means (5–50% difference for medium girth and reconstruction comparisons and 10–100% for large girth comparisons). Some IH features (standard deviation, energy, entropy) had variability below 10% for all sensitivity studies, while others (kurtosis, skewness) had variability above 30%. COM and ZSM features had complex sensitivities; correlation, energy, entropy (COM) and zone percentage, short-zone emphasis, zone-size non-uniformity (ZSM) had variability less than 5% while other metrics had differences up to 30%. Trends were similar for sample size estimation; for example, coarseness, contrast, and strength required 12, 38, and 52 patients to detect a 30% effect for the small girth case but 38, 88, and 128 patients in the large girth case. Conclusion: The sensitivity of PET textural features to image acquisition and reconstruction parameters is large and feature-dependent. Standards are needed to ensure that prospective

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.

Quantification of texture match of the skin graft: function and morphology of the stratum corneum.

PubMed

Inoue, K; Matsumoto, K

1986-01-01

In an attempt to analyze the "texture match" of grafted skin, functional and morphological aspects of the stratum corneum were studied using the Skin Surface Hydrometer (IBS Inc.) and the scanning electron microscope. The results showed that hygroscopicity and water holding capacity of the stratum corneum played a crucial role in making the skin surface soft and smooth. Morphologically there were regional differences in the surface pattern and the mean area of corneocytes, suggesting that these differences affect skin texture. It is suggested that the present functional and morphological studies of the stratum corneum can provide a quantitative measure of the "texture match".

Conjoint representation of texture ensemble and location in the parahippocampal place area.

PubMed

Park, Jeongho; Park, Soojin

2017-04-01

Texture provides crucial information about the category or identity of a scene. Nonetheless, not much is known about how the texture information in a scene is represented in the brain. Previous studies have shown that the parahippocampal place area (PPA), a scene-selective part of visual cortex, responds to simple patches of texture ensemble. However, in natural scenes textures exist in spatial context within a scene. Here we tested two hypotheses that make different predictions on how textures within a scene context are represented in the PPA. The Texture-Only hypothesis suggests that the PPA represents texture ensemble (i.e., the kind of texture) as is, irrespective of its location in the scene. On the other hand, the Texture and Location hypothesis suggests that the PPA represents texture and its location within a scene (e.g., ceiling or wall) conjointly. We tested these two hypotheses across two experiments, using different but complementary methods. In experiment 1 , by using multivoxel pattern analysis (MVPA) and representational similarity analysis, we found that the representational similarity of the PPA activation patterns was significantly explained by the Texture-Only hypothesis but not by the Texture and Location hypothesis. In experiment 2 , using a repetition suppression paradigm, we found no repetition suppression for scenes that had the same texture ensemble but differed in location (supporting the Texture and Location hypothesis). On the basis of these results, we propose a framework that reconciles contrasting results from MVPA and repetition suppression and draw conclusions about how texture is represented in the PPA. NEW & NOTEWORTHY This study investigates how the parahippocampal place area (PPA) represents texture information within a scene context. We claim that texture is represented in the PPA at multiple levels: the texture ensemble information at the across-voxel level and the conjoint information of texture and its location at the within

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.

Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT.

PubMed

Meng, Jie; Zhu, Lijing; Zhu, Li; Xie, Li; Wang, Huanhuan; Liu, Song; Yan, Jing; Liu, Baorui; Guan, Yue; He, Jian; Ge, Yun; Zhou, Zhengyang; Yang, Xiaofeng

2017-11-03

To explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram and texture analysis in predicting tumor recurrence of advanced cervical cancer treated with concurrent chemo-radiotherapy (CCRT). 36 women with pathologically confirmed advanced cervical squamous carcinomas were enrolled in this prospective study. 3.0 T pelvic MR examinations including diffusion weighted imaging (b = 0, 800 s/mm 2 ) were performed before CCRT (pre-CCRT) and at the end of 2nd week of CCRT (mid-CCRT). ADC histogram and texture features were derived from the whole volume of cervical cancers. With a mean follow-up of 25 months (range, 11 ∼ 43), 10/36 (27.8%) patients ended with recurrence. Pre-CCRT 75th, 90th, correlation, autocorrelation and mid-CCRT ADC mean , 10th, 25th, 50th, 75th, 90th, autocorrelation can effectively differentiate the recurrence from nonrecurrence group with area under the curve ranging from 0.742 to 0.850 (P values range, 0.001 ∼ 0.038). Pre- and mid-treatment whole-lesion ADC histogram and texture analysis hold great potential in predicting tumor recurrence of advanced cervical cancer treated with CCRT.

Quantitative Hydrocarbon Surface Analysis

NASA Technical Reports Server (NTRS)

Douglas, Vonnie M.

2000-01-01

The elimination of ozone depleting substances, such as carbon tetrachloride, has resulted in the use of new analytical techniques for cleanliness verification and contamination sampling. The last remaining application at Rocketdyne which required a replacement technique was the quantitative analysis of hydrocarbons by infrared spectrometry. This application, which previously utilized carbon tetrachloride, was successfully modified using the SOC-400, a compact portable FTIR manufactured by Surface Optics Corporation. This instrument can quantitatively measure and identify hydrocarbons from solvent flush of hardware as well as directly analyze the surface of metallic components without the use of ozone depleting chemicals. Several sampling accessories are utilized to perform analysis for various applications.

Multivariate Quantitative Chemical Analysis

NASA Technical Reports Server (NTRS)

Kinchen, David G.; Capezza, Mary

1995-01-01

Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

Cooking Methods for a Soft Diet Using Chicken Based on Food Texture Analysis.

PubMed

Watanabe, Emi; Maeno, Masami; Kayashita, Jun; Miyamoto, Ken-Ichi; Kogirima, Miho

2017-01-01

Undernutrition caused by difficulties in masticating is of growing concern among the elderly. Soft diets are often served at nursing homes; however, the styles differ with nursing homes. Improperly modified food texture and consistency may lead to further loss of nutritive value. Therefore, we developed a method to produce a soft diet using chicken. The texture-modified chicken was prepared by boiling a mixture of minced chicken and additive foodstuff that softened the meat. The best food additive was determined through testing cooking process, size after modification and texture. The optimum proportions of each component in the mixture were determined measuring food texture using a creep meter. Teriyaki chicken was cooked using the texture-modified chicken, and provided to a nursing home. The amount of food intake by elderly residents was subsequently surveyed. This study involved 22 residents (1 man and 21 women; mean age 91.4±5.3 y). Consequently, yakifu, which was made from wheat gluten, was the most suitable additive foodstuff. The hardness of the texture-modified chicken, with proportions of minced chicken, yakifu, and water being 50%, 10%, and 40% respectively, was under 40,000 N/m 2 . The intake amount of the texture-modified chicken of subjects whose intake amount of conventional chicken using chicken thigh was not 100% was significantly higher. These findings suggest that properly modified food textures could contribute to improve the quality of meals by preventing undernutrition among the elderly with mastication difficulties.

South Polar Textures

NASA Image and Video Library

2014-10-07

While yesterday image showed a texture of oval depressions swiss cheese, this image from NASA 2001 Mars Odyssey spacecraft shows a linear surface texture of the south polar cap. This texture is described as looking like a thumbprint.

Surface Textural Analysis of Quartz Grains from Modern Point Bar Deposits in Lower Reaches of the Yellow River

NASA Astrophysics Data System (ADS)

Cheng, Yong; Liu, Cong; Lu, Ping; Zhang, Yu; Nie, Qi; Wen, Yiming

2018-01-01

The surfaces of quartz grains contain characteristic textures formed during the process of transport, due to their stable physical and chemical properties. The surface textures include the information about source area, transporting force, sedimentary environment and evolution history of sediment. Surface textures of quartz grains from modern point bar deposits in the lower reaches of the Yellow River are observed and studied by scanning electron microscopy (SEM). Results indicate that there are 22 kinds of surface textures. The overall surface morphology of quartz grains shows short transporting time and distance and weak abrasive action of the river water. The combined surface textures caused by mechanical action indicate that quartz grains are transporting in a high-energy hydrodynamic condition and suffer a strong mechanical impact and abrasion. The common solution pits prove that the chemical property of transportation medium is very active and quartz grains receive an obvious chemical action. The combination of these surface textures can be an identification mark of fluvial environment, and that is: quartz grains are main subangular outline, whose roundness is higher with the farther motion distance; Surface fluctuation degree of quartz grains is relatively high, and gives priority to high and medium relief; V-shaped percussion marks are very abundant caused by mechanical action; The conchoidal of different sizes and steps are common-developed with paragenesis relationship; Solution pits are common-developed as well. The study makes up for the blank of surface textures analysis of quartz grains from modern fluvial deposits in China. It provides new ideas and evidence for studies of the sedimentary process and environmental significance, although the deep meanings of these micro textures remain to be further researched.

Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population.

PubMed

Areeckal, A S; Jayasheelan, N; Kamath, J; Zawadynski, S; Kocher, M; David S, S

2018-03-01

We propose an automated low cost tool for early diagnosis of onset of osteoporosis using cortical radiogrammetry and cancellous texture analysis from hand and wrist radiographs. The trained classifier model gives a good performance accuracy in classifying between healthy and low bone mass subjects. We propose a low cost automated diagnostic tool for early diagnosis of reduction in bone mass using cortical radiogrammetry and cancellous texture analysis of hand and wrist radiographs. Reduction in bone mass could lead to osteoporosis, a disease observed to be increasingly occurring at a younger age in recent times. Dual X-ray absorptiometry (DXA), currently used in clinical practice, is expensive and available only in urban areas in India. Therefore, there is a need to develop a low cost diagnostic tool in order to facilitate large-scale screening of people for early diagnosis of osteoporosis at primary health centers. Cortical radiogrammetry from third metacarpal bone shaft and cancellous texture analysis from distal radius are used to detect low bone mass. Cortical bone indices and cancellous features using Gray Level Run Length Matrices and Laws' masks are extracted. A neural network classifier is trained using these features to classify healthy subjects and subjects having low bone mass. In our pilot study, the proposed segmentation method shows 89.9 and 93.5% accuracy in detecting third metacarpal bone shaft and distal radius ROI, respectively. The trained classifier shows training accuracy of 94.3% and test accuracy of 88.5%. An automated diagnostic technique for early diagnosis of onset of osteoporosis is developed using cortical radiogrammetric measurements and cancellous texture analysis of hand and wrist radiographs. The work shows that a combination of cortical and cancellous features improves the diagnostic ability and is a promising low cost tool for early diagnosis of increased risk of osteoporosis.

On soil textural classifications and soil-texture-based estimations

NASA Astrophysics Data System (ADS)

Ángel Martín, Miguel; Pachepsky, Yakov A.; García-Gutiérrez, Carlos; Reyes, Miguel

2018-02-01

The soil texture representation with the standard textural fraction triplet sand-silt-clay is commonly used to estimate soil properties. The objective of this work was to test the hypothesis that other fraction sizes in the triplets may provide a better representation of soil texture for estimating some soil parameters. We estimated the cumulative particle size distribution and bulk density from an entropy-based representation of the textural triplet with experimental data for 6240 soil samples. The results supported the hypothesis. For example, simulated distributions were not significantly different from the original ones in 25 and 85 % of cases when the sand-silt-clay and very coarse+coarse + medium sand - fine + very fine sand - silt+clay were used, respectively. When the same standard and modified triplets were used to estimate the average bulk density, the coefficients of determination were 0.001 and 0.967, respectively. Overall, the textural triplet selection appears to be application and data specific.

Texture-dependent motion signals in primate middle temporal area

PubMed Central

Gharaei, Saba; Tailby, Chris; Solomon, Selina S; Solomon, Samuel G

2013-01-01

Neurons in the middle temporal (MT) area of primate cortex provide an important stage in the analysis of visual motion. For simple stimuli such as bars and plaids some neurons in area MT – pattern cells – seem to signal motion independent of contour orientation, but many neurons – component cells – do not. Why area MT supports both types of receptive field is unclear. To address this we made extracellular recordings from single units in area MT of anaesthetised marmoset monkeys and examined responses to two-dimensional images with a large range of orientations and spatial frequencies. Component and pattern cell response remained distinct during presentation of these complex spatial textures. Direction tuning curves were sharpest in component cells when a texture contained a narrow range of orientations, but were similar across all neurons for textures containing all orientations. Response magnitude of pattern cells, but not component cells, increased with the spatial bandwidth of the texture. In addition, response variability in all neurons was reduced when the stimulus was rich in spatial texture. Fisher information analysis showed that component cells provide more informative responses than pattern cells when a texture contains a narrow range of orientations, but pattern cells had more informative responses for broadband textures. Component cells and pattern cells may therefore coexist because they provide complementary and parallel motion signals. PMID:24000175

Textured silicon nitride: processing and anisotropic properties

PubMed Central

Zhu, Xinwen; Sakka, Yoshio

2008-01-01

Textured silicon nitride (Si3N4) has been intensively studied over the past 15 years because of its use for achieving its superthermal and mechanical properties. In this review we present the fundamental aspects of the processing and anisotropic properties of textured Si3N4, with emphasis on the anisotropic and abnormal grain growth of β-Si3N4, texture structure and texture analysis, processing methods and anisotropic properties. On the basis of the texturing mechanisms, the processing methods described in this article have been classified into two types: hot-working (HW) and templated grain growth (TGG). The HW method includes the hot-pressing, hot-forging and sinter-forging techniques, and the TGG method includes the cold-pressing, extrusion, tape-casting and strong magnetic field alignment techniques for β-Si3N4 seed crystals. Each processing technique is thoroughly discussed in terms of theoretical models and experimental data, including the texturing mechanisms and the factors affecting texture development. Also, methods of synthesizing the rodlike β-Si3N4 single crystals are presented. Various anisotropic properties of textured Si3N4 and their origins are thoroughly described and discussed, such as hardness, elastic modulus, bending strength, fracture toughness, fracture energy, creep behavior, tribological and wear behavior, erosion behavior, contact damage behavior and thermal conductivity. Models are analyzed to determine the thermal anisotropy by considering the intrinsic thermal anisotropy, degree of orientation and various microstructure factors. Textured porous Si3N4 with a unique microstructure composed of oriented elongated β-Si3N4 and anisotropic pores is also described for the first time, with emphasis on its unique mechanical and thermal-mechanical properties. Moreover, as an important related material, textured α-Sialon is also reviewed, because the presence of elongated α-Sialon grains allows the production of textured α-Sialon using the

The effectiveness of texture analysis for mapping forest land using the panchromatic bands of Landsat 7, SPOT, and IRS imagery

Treesearch

Michael L. Hoppus; Rachel I. Riemann; Andrew J. Lister; Mark V. Finco

2002-01-01

The panchromatic bands of Landsat 7, SPOT, and IRS satellite imagery provide an opportunity to evaluate the effectiveness of texture analysis of satellite imagery for mapping of land use/cover, especially forest cover. A variety of texture algorithms, including standard deviation, Ryherd-Woodcock minimum variance adaptive window, low pass etc., were applied to moving...

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.

Predicting Ki67% expression from DCE-MR images of breast tumors using textural kinetic features in tumor habitats

NASA Astrophysics Data System (ADS)

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

2016-03-01

The use of Ki67% expression, a cell proliferation marker, as a predictive and prognostic factor has been widely studied in the literature. Yet its usefulness is limited due to inconsistent cut off scores for Ki67% expression, subjective differences in its assessment in various studies, and spatial variation in expression, which makes it difficult to reproduce as a reliable independent prognostic factor. Previous studies have shown that there are significant spatial variations in Ki67% expression, which may limit its clinical prognostic utility after core biopsy. These variations are most evident when examining the periphery of the tumor vs. the core. To date, prediction of Ki67% expression from quantitative image analysis of DCE-MRI is very limited. This work presents a novel computer aided diagnosis framework to use textural kinetics to (i) predict the ratio of periphery Ki67% expression to core Ki67% expression, and (ii) predict Ki67% expression from individual tumor habitats. The pilot cohort consists of T1 weighted fat saturated DCE-MR images from 17 patients. Support vector regression with a radial basis function was used for predicting the Ki67% expression and ratios. The initial results show that texture features from individual tumor habitats are more predictive of the Ki67% expression ratio and spatial Ki67% expression than features from the whole tumor. The Ki67% expression ratio could be predicted with a root mean square error (RMSE) of 1.67%. Quantitative image analysis of DCE-MRI using textural kinetic habitats, has the potential to be used as a non-invasive method for predicting Ki67 percentage and ratio, thus more accurately reporting high KI-67 expression for patient prognosis.

Viscous anisotropy of textured olivine aggregates: 2. Micromechanical model

NASA Astrophysics Data System (ADS)

Hansen, Lars N.; Conrad, Clinton P.; Boneh, Yuval; Skemer, Philip; Warren, Jessica M.; Kohlstedt, David L.

2016-10-01

The significant viscous anisotropy that results from crystallographic alignment (texture) of olivine grains in deformed upper mantle rocks strongly influences a large variety of geodynamic processes. Our ability to explore the effects of anisotropic viscosity in simulations of these processes requires a mechanical model that can predict the magnitude of anisotropy and its evolution. Unfortunately, existing models of olivine textural evolution and viscous anisotropy are calibrated for relatively small deformations and simple strain paths, making them less general than desired for many large-scale geodynamic scenarios. Here we develop a new set of micromechanical models to describe the mechanical behavior and textural evolution of olivine through a large range of strains and complex strain histories. For the mechanical behavior, we explore two extreme scenarios, one in which each grain experiences the same stress tensor (Sachs model) and one in which each grain undergoes a strain rate as close as possible to the macroscopic strain rate (pseudo-Taylor model). For the textural evolution, we develop a new model in which the director method is used to control the rate of grain rotation and the available slip systems in olivine are used to control the axis of rotation. Only recently has enough laboratory data on the deformation of olivine become available to calibrate these models. We use these new data to conduct inversions for the best parameters to characterize both the mechanical and textural evolution models. These inversions demonstrate that the calibrated pseudo-Taylor model best reproduces the mechanical observations. Additionally, the pseudo-Taylor textural evolution model can reasonably reproduce the observed texture strength, shape, and orientation after large and complex deformations. A quantitative comparison between our calibrated models and previously published models reveals that our new models excel in predicting the magnitude of viscous anisotropy and

Texture evolution and their effects on the mechanical properties of duplex Mg-Li alloy

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

Zou, Yun; Zhang, Lehao; Wang, Hongtao

Texture evolution is strongly dependent on the deformation mode during thermo-mechanical treatments. In this paper, we report the texture evolution in a duplex Mg-Li alloy. The results provide an evidence of deformation mode transition in the hexagonal-close-packed (hcp) alpha phase with various thickness reductions. The activation sequence of deformation modes is basal slip first, and then pyramidal slip during hot-rolling to a thickness reduction of 40%. The relative activity of slip decreases with further thickness reduction. After annealing, basal texture is strengthened and pyramidal component disappears due to static recrystallization and grain growth. The microstructure, specifically texture evolution in bothmore » hcp alpha and body-centered cubic (bcc) beta phase and their effects on mechanical properties are quantitatively analyzed and assessed. (C) 2016 Elsevier B.V. All rights reserved.« less

Texture evolution and their effects on the mechanical properties of duplex Mg-Li alloy

DOE PAGES

Zou, Yun; Zhang, Lehao; Wang, Hongtao; ...

2016-01-27

Texture evolution is strongly dependent on the deformation mode during thermo-mechanical treatments. In this paper, we report the texture evolution in a duplex Mg-Li alloy. The results provide an evidence of deformation mode transition in the hexagonal-close-packed (hcp) alpha phase with various thickness reductions. The activation sequence of deformation modes is basal slip first, and then pyramidal slip during hot-rolling to a thickness reduction of 40%. The relative activity of slip decreases with further thickness reduction. After annealing, basal texture is strengthened and pyramidal component disappears due to static recrystallization and grain growth. The microstructure, specifically texture evolution in bothmore » hcp alpha and body-centered cubic (bcc) beta phase and their effects on mechanical properties are quantitatively analyzed and assessed. (C) 2016 Elsevier B.V. All rights reserved.« less

Wavelet analysis enables system-independent texture analysis of optical coherence tomography images.

PubMed

Lingley-Papadopoulos, Colleen A; Loew, Murray H; Zara, Jason M

2009-01-01

Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.

Wavelet analysis enables system-independent texture analysis of optical coherence tomography images

NASA Astrophysics Data System (ADS)

Lingley-Papadopoulos, Colleen A.; Loew, Murray H.; Zara, Jason M.

2009-07-01

Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

PubMed Central

Bayır, Şafak

2016-01-01

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC. PMID:27110272

Preprocessing with image denoising and histogram equalization for endoscopy image analysis using texture analysis.

PubMed

Hiroyasu, Tomoyuki; Hayashinuma, Katsutoshi; Ichikawa, Hiroshi; Yagi, Nobuaki

2015-08-01

A preprocessing method for endoscopy image analysis using texture analysis is proposed. In a previous study, we proposed a feature value that combines a co-occurrence matrix and a run-length matrix to analyze the extent of early gastric cancer from images taken with narrow-band imaging endoscopy. However, the obtained feature value does not identify lesion zones correctly due to the influence of noise and halation. Therefore, we propose a new preprocessing method with a non-local means filter for de-noising and contrast limited adaptive histogram equalization. We have confirmed that the pattern of gastric mucosa in images can be improved by the proposed method. Furthermore, the lesion zone is shown more correctly by the obtained color map.

Automated Classification of Usual Interstitial Pneumonia using Regional Volumetric Texture Analysis in High-Resolution CT

PubMed Central

Depeursinge, Adrien; Chin, Anne S.; Leung, Ann N.; Terrone, Donato; Bristow, Michael; Rosen, Glenn; Rubin, Daniel L.

2014-01-01

Objectives We propose a novel computational approach for the automated classification of classic versus atypical usual interstitial pneumonia (UIP). Materials and Methods 33 patients with UIP were enrolled in this study. They were classified as classic versus atypical UIP by a consensus of two thoracic radiologists with more than 15 years of experience using the American Thoracic Society evidence–based guidelines for CT diagnosis of UIP. Two cardiothoracic fellows with one year of subspecialty training provided independent readings. The system is based on regional characterization of the morphological tissue properties of lung using volumetric texture analysis of multiple detector CT images. A simple digital atlas with 36 lung subregions is used to locate texture properties, from which the responses of multi-directional Riesz wavelets are obtained. Machine learning is used to aggregate and to map the regional texture attributes to a simple score that can be used to stratify patients with UIP into classic and atypical subtypes. Results We compared the predictions based on regional volumetric texture analysis with the ground truth established by expert consensus. The area under the receiver operating characteristic curve of the proposed score was estimated to be 0.81 using a leave-one-patient-out cross-validation, with high specificity for classic UIP. The performance of our automated method was found to be similar to that of the two fellows and to the agreement between experienced chest radiologists reported in the literature. However, the errors of our method and the fellows occurred on different cases, which suggests that combining human and computerized evaluations may be synergistic. Conclusions Our results are encouraging and suggest that an automated system may be useful in routine clinical practice as a diagnostic aid for identifying patients with complex lung disease such as classic UIP, obviating the need for invasive surgical lung biopsy and its

SU-E-J-260: Quantitative Image Feature Analysis of Multiphase Liver CT for Hepatocellular Carcinoma (HCC) in Radiation Therapy

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

Choi, W; Wang, J; Lu, W

Purpose: To identify the effective quantitative image features (radiomics features) for prediction of response, survival, recurrence and metastasis of hepatocellular carcinoma (HCC) in radiotherapy. Methods: Multiphase contrast enhanced liver CT images were acquired in 16 patients with HCC on pre and post radiation therapy (RT). In this study, arterial phase CT images were selected to analyze the effectiveness of image features for the prediction of treatment outcome of HCC to RT. Response evaluated by RECIST criteria, survival, local recurrence (LR), distant metastasis (DM) and liver metastasis (LM) were examined. A radiation oncologist manually delineated the tumor and normal liver onmore » pre and post CT scans, respectively. Quantitative image features were extracted to characterize the intensity distribution (n=8), spatial patterns (texture, n=36), and shape (n=16) of the tumor and liver, respectively. Moreover, differences between pre and post image features were calculated (n=120). A total of 360 features were extracted and then analyzed by unpaired student’s t-test to rank the effectiveness of features for the prediction of response. Results: The five most effective features were selected for prediction of each outcome. Significant predictors for tumor response and survival are changes in tumor shape (Second Major Axes Length, p= 0.002; Eccentricity, p=0.0002), for LR, liver texture (Standard Deviation (SD) of High Grey Level Run Emphasis and SD of Entropy, both p=0.005) on pre and post CT images, for DM, tumor texture (SD of Entropy, p=0.01) on pre CT image and for LM, liver (Mean of Cluster Shade, p=0.004) and tumor texture (SD of Entropy, p=0.006) on pre CT image. Intensity distribution features were not significant (p>0.09). Conclusion: Quantitative CT image features were found to be potential predictors of the five endpoints of HCC in RT. This work was supported in part by the National Cancer Institute Grant R01CA172638.« less

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.

Quantitative characterisation of sedimentary grains

NASA Astrophysics Data System (ADS)

Tunwal, Mohit; Mulchrone, Kieran F.; Meere, Patrick A.

2016-04-01

Analysis of sedimentary texture helps in determining the formation, transportation and deposition processes of sedimentary rocks. Grain size analysis is traditionally quantitative, whereas grain shape analysis is largely qualitative. A semi-automated approach to quantitatively analyse shape and size of sand sized sedimentary grains is presented. Grain boundaries are manually traced from thin section microphotographs in the case of lithified samples and are automatically identified in the case of loose sediments. Shape and size paramters can then be estimated using a software package written on the Mathematica platform. While automated methodology already exists for loose sediment analysis, the available techniques for the case of lithified samples are limited to cases of high definition thin section microphotographs showing clear contrast between framework grains and matrix. Along with the size of grain, shape parameters such as roundness, angularity, circularity, irregularity and fractal dimension are measured. A new grain shape parameter developed using Fourier descriptors has also been developed. To test this new approach theoretical examples were analysed and produce high quality results supporting the accuracy of the algorithm. Furthermore sandstone samples from known aeolian and fluvial environments from the Dingle Basin, County Kerry, Ireland were collected and analysed. Modern loose sediments from glacial till from County Cork, Ireland and aeolian sediments from Rajasthan, India have also been collected and analysed. A graphical summary of the data is presented and allows for quantitative distinction between samples extracted from different sedimentary environments.

Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

PubMed

Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

2015-09-01

The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

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

Nanofrictional behavior of amorphous, polycrystalline and textured Y-Cr-O films

NASA Astrophysics Data System (ADS)

Gervacio-Arciniega, J. J.; Flores-Ruiz, F. J.; Diliegros-Godines, C. J.; Broitman, E.; Enriquez-Flores, C. I.; Espinoza-Beltrán, F. J.; Siqueiros, J.; Cruz, M. P.

2016-08-01

Differences in friction coefficients (μ) of ferroelectric YCrO3, textured and polycrystalline films, and non-ferroelectric Y-Cr-O films are analyzed. The friction coefficient was evaluated by atomic force microscopy using a simple quantitative procedure where the dependence of friction force with the applied load is obtained in only one topographical image. A simple code was developed with the MATLAB® software to analyze the experimental data. The code includes a correction of the hysteresis in the forward and backward scanning directions. The quantification of load exerted on the sample surface was obtained by finite element analysis of the AFM cantilever starting from its experimental dynamic information. The results show that the ferroelectric YCrO3 film deposited on a Pt(150 nm)/TiO2(30 nm)/SiO2/Si (100) substrate is polycrystalline and has a lower friction coefficient than the deposited on SrTiO3 (110), which is highly textured. From a viewpoint of industrial application in ferroelectric memories, where the writing process is electrical or mechanically achieved by sliding AFM tips on the sample, polycrystalline YCrO3 films seem to be the best candidates due to their lower μ.

Quantitative CT analysis for the preoperative prediction of pathologic grade in pancreatic neuroendocrine tumors

NASA Astrophysics Data System (ADS)

Chakraborty, Jayasree; Pulvirenti, Alessandra; Yamashita, Rikiya; Midya, Abhishek; Gönen, Mithat; Klimstra, David S.; Reidy, Diane L.; Allen, Peter J.; Do, Richard K. G.; Simpson, Amber L.

2018-02-01

Pancreatic neuroendocrine tumors (PanNETs) account for approximately 5% of all pancreatic tumors, affecting one individual per million each year.1 PanNETs are difficult to treat due to biological variability from benign to highly malignant, indolent to very aggressive. The World Health Organization classifies PanNETs into three categories based on cell proliferative rate, usually detected using the Ki67 index and cell morphology: low-grade (G1), intermediate-grade (G2) and high-grade (G3) tumors. Knowledge of grade prior to treatment would select patients for optimal therapy: G1/G2 tumors respond well to somatostatin analogs and targeted or cytotoxic drugs whereas G3 tumors would be targeted with platinum or alkylating agents.2, 3 Grade assessment is based on the pathologic examination of the surgical specimen, biopsy or ne-needle aspiration; however, heterogeneity in the proliferative index can lead to sampling errors.4 Based on studies relating qualitatively assessed shape and enhancement characteristics on CT imaging to tumor grade in PanNET,5 we propose objective classification of PanNET grade with quantitative analysis of CT images. Fifty-five patients were included in our retrospective analysis. A pathologist graded the tumors. Texture and shape-based features were extracted from CT. Random forest and naive Bayes classifiers were compared for the classification of G1/G2 and G3 PanNETs. The best area under the receiver operating characteristic curve (AUC) of 0:74 and accuracy of 71:64% was achieved with texture features. The shape-based features achieved an AUC of 0:70 and accuracy of 78:73%.

Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

PubMed

Karacavus, Seyhan; Yılmaz, Bülent; Tasdemir, Arzu; Kayaaltı, Ömer; Kaya, Eser; İçer, Semra; Ayyıldız, Oguzhan

2018-04-01

We investigated the association between the textural features obtained from 18 F-FDG images, metabolic parameters (SUVmax , SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.

Micromechanical Characterization and Texture Analysis of Direct Cast Titanium Alloys Strips

NASA Technical Reports Server (NTRS)

2000-01-01

This research was conducted to determine a post-processing technique to optimize mechanical and material properties of a number of Titanium based alloys and aluminides processed via Melt Overflow Solidification Technique (MORST). This technique was developed by NASA for the development of thin sheet titanium and titanium aluminides used in high temperature applications. The materials investigated in this study included conventional titanium alloy strips and foils, Ti-1100, Ti-24Al-11Nb (Alpha-2), and Ti-48Al-2Ta (Gamma). The methodology used included micro-characterization, heat-treatment, mechanical processing and mechanical testing. Characterization techniques included optical, electron microscopy, and x-ray texture analysis. The processing included heat-treatment and mechanical deformation through cold rolling. The initial as-cast materials were evaluated for their microstructure and mechanical properties. Different heat-treatment and rolling steps were chosen to process these materials. The properties were evaluated further and a processing relationship was established in order to obtain an optimum processing condition. The results showed that the as-cast material exhibited a Widmanstatten (fine grain) microstructure that developed into a microstructure with larger grains through processing steps. The texture intensity showed little change for all processing performed in this investigation.

Dental microwear texture analysis of late Pliocene Procynocephalus subhimalayanus (Primates: Cercopithecidae) of the Upper Siwaliks, India

NASA Astrophysics Data System (ADS)

Williams, Frank L'Engle; Holmes, Noelle A.

2012-09-01

Late Pliocene Procynocephalus subhimalayanus from the Upper Siwaliks, India is known from only three specimens. The dietary proclivities of this taxon have implications for reconstructing the paleoecology of the Upper Siwaliks. The dental microwear texture properties of Procynocephalus are compared to those from extant tropical forest primates including Alouatta palliata (n = 11), Cebus apella (n = 13), Gorilla gorilla (n = 9), Lophocebus albigena (n = 15) and Trachypithecus cristatus (n = 12). Dental microwear textures are generated by scanning the surface enamel of Facet 9 using white-light confocal microscopy at 100x. Four variables were extracted from scale-sensitive fractal analysis, and the data were ranked before ANOVA with post-hoc tests of significance and multivariate analyses were performed. Procynocephalus clusters closest to Lophocebus, Cebus and some Gorilla specimens suggesting hard-object feeding characterized a portion of its diet. The dental microwear texture of Procynocephalus supports interpretations of widespread grasslands of the Late Pliocene Kansal Formation (Pinjor zone). The extreme enamel complexity characterizing Procynocephalus may derive from consumption of underground storage organs, or other foods with high grit loads. Foods consumed near ground level carry a heavy load of abrasive minerals possibly contributing to greater enamel surface complexity and textural fill volume.

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.

Study of CT image texture using deep learning techniques

NASA Astrophysics Data System (ADS)

Dutta, Sandeep; Fan, Jiahua; Chevalier, David

2018-03-01

For CT imaging, reduction of radiation dose while improving or maintaining image quality (IQ) is currently a very active research and development topic. Iterative Reconstruction (IR) approaches have been suggested to be able to offer better IQ to dose ratio compared to the conventional Filtered Back Projection (FBP) reconstruction. However, it has been widely reported that often CT image texture from IR is different compared to that from FBP. Researchers have proposed different figure of metrics to quantitate the texture from different reconstruction methods. But there is still a lack of practical and robust method in the field for texture description. This work applied deep learning method for CT image texture study. Multiple dose scans of a 20cm diameter cylindrical water phantom was performed on Revolution CT scanner (GE Healthcare, Waukesha) and the images were reconstructed with FBP and four different IR reconstruction settings. The training images generated were randomly allotted (80:20) to a training and validation set. An independent test set of 256-512 images/class were collected with the same scan and reconstruction settings. Multiple deep learning (DL) networks with Convolution, RELU activation, max-pooling, fully-connected, global average pooling and softmax activation layers were investigated. Impact of different image patch size for training was investigated. Original pixel data as well as normalized image data were evaluated. DL models were reliably able to classify CT image texture with accuracy up to 99%. Results show that the deep learning techniques suggest that CT IR techniques may help lower the radiation dose compared to FBP.

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

DTIC Science & Technology

2012-10-24

representative pdf’s via the Kullback - Leibler divergence (KL). Species turnover, or b diversity, is estimated using both this KL divergence and the...multiresolution analysis provides a means for estimating divergence between two textures, specifically the Kullback - Leibler divergence between the pair of ...and open challenges. Ecological Informatics 5: 318–329. 19. Ludovisi A, TaticchiM(2006) Investigating beta diversity by kullback - leibler information

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.

Detection of pigment network in dermatoscopy images using texture analysis

PubMed Central

Anantha, Murali; Moss, Randy H.; Stoecker, William V.

2011-01-01

Dermatoscopy, also known as dermoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. ELM offers a completely new range of visual features. One such prominent feature is the pigment network. Two texture-based algorithms are developed for the detection of pigment network. These methods are applicable to various texture patterns in dermatoscopy images, including patterns that lack fine lines such as cobblestone, follicular, or thickened network patterns. Two texture algorithms, Laws energy masks and the neighborhood gray-level dependence matrix (NGLDM) large number emphasis, were optimized on a set of 155 dermatoscopy images and compared. Results suggest superiority of Laws energy masks for pigment network detection in dermatoscopy images. For both methods, a texel width of 10 pixels or approximately 0.22 mm is found for dermatoscopy images. PMID:15249068

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.

Instrumental texture characteristics of broiler pectoralis major with the woody breast condition

USDA-ARS?s Scientific Manuscript database

The objective was to characterize texture properties of raw and cooked broiler fillets (pectoralis major) with the woody breast condition (WBC) using instrumental texture techniques Meullenet-Owens Razor Shear (MORS) and texture profile analysis (TPA). Deboned (3 h postmortem) broiler fillets were c...

Food texture analysis in the 21st century

USDA-ARS?s Scientific Manuscript database

The study of food texture encompasses sensory, physiological, and structural aspects. Research in this area must be multidisciplinary in nature, accounting for consumer perception and acceptability, rheology, and structural aspects. This brief review of the field covers sensory panels, instrumenta...

Fault rock texture and porosity type in Triassic dolostones

NASA Astrophysics Data System (ADS)

Agosta, Fabrizio; Grieco, Donato; Bardi, Alessandro; Prosser, Giacomo

2015-04-01

Preliminary results of an ongoing project aimed at deciphering the micromechanics and porosity evolution associated to brittle deformation of Triassic dolostones are presented. Samples collected from high-angle, oblique-slip, 10's to 100's m-throw normal faults crosscutting Mesozoic carbonates of the Neo Tethys (Campanian-Lucanian Platform) are investigated by mean of field geological mapping, optical microscopy, SEM and image analyses. The goal is to characterize in detail composition, texture and porosity of cataclastic rocks in order to assess the structural architecture of dolomitic fault cores. Moreover, the present study addresses the time-space control exerted by several micro-mechanisms such as intragranular extensional fracturing, chipping and shear fracturing, which took place during grain rolling and crushing within the evolving faults, on type, amount, dimensions and distribution of micropores present within the cataclastic fault cores. Study samples are representative of well-exposed dolomitic fault cores of oblique-slip normal faults trending either NW-SE or NE-SW. The high-angle normal faults crosscut the Mesozoic carbonates of the Campanian-Lucanian Platform, which overrode the Lagonegro succession by mean of low-angle thrust faults. Fault throws are measured by considering the displaced thrust faults as key markers after large scale field mapping (1:10,000 scale) of the study areas. In the field, hand samples were selected according to their distance from main slip surfaces and, in some case, along secondary slip surfaces. Microscopy analysis of about 100 oriented fault rock samples shows that, mostly, the study cataclastic rocks are made up of dolomite and sparse, minute survivor silicate grains deriving from the Lagonegro succession. In order to quantitatively assess the main textural classes, a great attention is paid to the grain-matrix ratio, grain sphericity, grain roundness, and grain sorting. By employing an automatic box-counting technique

Brain tissue analysis using texture features based on optical coherence tomography images

NASA Astrophysics Data System (ADS)

Lenz, Marcel; Krug, Robin; Dillmann, Christopher; Gerhardt, Nils C.; Welp, Hubert; Schmieder, Kirsten; Hofmann, Martin R.

2018-02-01

Brain tissue differentiation is highly demanded in neurosurgeries, i.e. tumor resection. Exact navigation during the surgery is essential in order to guarantee best life quality afterwards. So far, no suitable method has been found that perfectly covers this demands. With optical coherence tomography (OCT), fast three dimensional images can be obtained in vivo and contactless with a resolution of 1-15 μm. With these specifications OCT is a promising tool to support neurosurgeries. Here, we investigate ex vivo samples of meningioma, healthy white and healthy gray matter in a preliminary study towards in vivo brain tumor removal assistance. Raw OCT images already display structural variations for different tissue types, especially meningioma. But, in order to achieve neurosurgical guidance directly during resection, an automated differentiation approach is desired. For this reason, we employ different texture feature based algorithms, perform a Principal Component Analysis afterwards and then train a Support Vector Machine classifier. In the future we will try different combinations of texture features and perform in vivo measurements in order to validate our findings.

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.

Ultrasound texture analysis: Association with lymph node metastasis of papillary thyroid microcarcinoma.

PubMed

Kim, Soo-Yeon; Lee, Eunjung; Nam, Se Jin; Kim, Eun-Kyung; Moon, Hee Jung; Yoon, Jung Hyun; Han, Kyung Hwa; Kwak, Jin Young

2017-01-01

This retrospective study aimed to evaluate whether ultrasound texture analysis is useful to predict lymph node metastasis in patients with papillary thyroid microcarcinoma (PTMC). This study was approved by the Institutional Review Board, and the need to obtain informed consent was waived. Between May and July 2013, 361 patients (mean age, 43.8 ± 11.3 years; range, 16-72 years) who underwent staging ultrasound (US) and subsequent thyroidectomy for conventional PTMC ≤ 10 mm between May and July 2013 were included. Each PTMC was manually segmented and its histogram parameters (Mean, Standard deviation, Skewness, Kurtosis, and Entropy) were extracted with Matlab software. The mean values of histogram parameters and clinical and US features were compared according to lymph node metastasis using the independent t-test and Chi-square test. Multivariate logistic regression analysis was performed to identify the independent factors associated with lymph node metastasis. Tumors with lymph node metastasis (n = 117) had significantly higher entropy compared to those without lymph node metastasis (n = 244) (mean±standard deviation, 6.268±0.407 vs. 6.171±.0.405; P = .035). No additional histogram parameters showed differences in mean values according to lymph node metastasis. Entropy was not independently associated with lymph node metastasis on multivariate logistic regression analysis (Odds ratio, 0.977 [95% confidence interval (CI), 0.482-1.980]; P = .949). Younger age (Odds ratio, 0.962 [95% CI, 0.940-0.984]; P = .001) and lymph node metastasis on US (Odds ratio, 7.325 [95% CI, 3.573-15.020]; P < .001) were independently associated with lymph node metastasis. Texture analysis was not useful in predicting lymph node metastasis in patients with PTMC.

Texture analysis of MR images of patients with Mild Traumatic Brain Injury

PubMed Central

2010-01-01

Background Our objective was to study the effect of trauma on texture features in cerebral tissue in mild traumatic brain injury (MTBI). Our hypothesis was that a mild trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection but could be detected with texture analysis (TA). Methods We imaged 42 MTBI patients by using 1.5 T MRI within three weeks of onset of trauma. TA was performed on the area of mesencephalon, cerebral white matter at the levels of mesencephalon, corona radiata and centrum semiovale and in different segments of corpus callosum (CC) which have been found to be sensitive to damage. The same procedure was carried out on a control group of ten healthy volunteers. Patients' TA data was compared with the TA results of the control group comparing the amount of statistically significantly differing TA parameters between the left and right sides of the cerebral tissue and comparing the most discriminative parameters. Results There were statistically significant differences especially in several co-occurrence and run-length matrix based parameters between left and right side in the area of mesencephalon, in cerebral white matter at the level of corona radiata and in the segments of CC in patients. Considerably less difference was observed in the healthy controls. Conclusions TA revealed significant changes in texture parameters of cerebral tissue between hemispheres and CC segments in TBI patients. TA may serve as a novel additional tool for detecting the conventionally invisible changes in cerebral tissue in MTBI and help the clinicians to make an early diagnosis. PMID:20462439

Spatially resolved texture and microstructure evolution of additively manufactured and gas gun deformed 304L stainless steel investigated by neutron diffraction and electron backscatter diffraction

DOE PAGES

Takajo, Shigehiro; Brown, Donald William; Clausen, Bjorn; ...

2018-04-30

In this study, we report the characterization of a 304L stainless steel cylindrical projectile produced by additive manufacturing. The projectile was compressively deformed using a Taylor Anvil Gas Gun, leading to a huge strain gradient along the axis of the deformed cylinder. Spatially resolved neutron diffraction measurements on the HIgh Pressure Preferred Orientation time-of-flight diffractometer (HIPPO) and Spectrometer for Materials Research at Temperature and Stress diffractometer (SMARTS) beamlines at the Los Alamos Neutron Science CEnter (LANSCE) with Rietveld and single-peak analysis were used to quantitatively evaluate the volume fractions of the α, γ, and ε phases as well as residualmore » strain and texture. The texture of the γ phase is consistent with uniaxial compression, while the α texture can be explained by the Kurdjumov–Sachs relationship from the γ texture after deformation. This indicates that the material first deformed in the γ phase and subsequently transformed at larger strains. The ε phase was only found in volumes close to the undeformed material with a texture connected to the γ texture by the Shoji–Nishiyama orientation relationship. This allows us to conclude that the ε phase occurs as an intermediate phase at lower strain, and is superseded by the α phase when strain increases further. We found a proportionality between the root-mean-squared microstrain of the γ phase, dominated by the dislocation density, with the α volume fraction, consistent with strain-induced martensite α formation. In conclusion, knowledge of the sample volume with the ε phase from the neutron diffraction analysis allowed us to identify the ε phase by electron back scatter diffraction analysis, complementing the neutron diffraction analysis with characterization on the grain level.« less

Spatially resolved texture and microstructure evolution of additively manufactured and gas gun deformed 304L stainless steel investigated by neutron diffraction and electron backscatter diffraction

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

Takajo, Shigehiro; Brown, Donald William; Clausen, Bjorn

In this study, we report the characterization of a 304L stainless steel cylindrical projectile produced by additive manufacturing. The projectile was compressively deformed using a Taylor Anvil Gas Gun, leading to a huge strain gradient along the axis of the deformed cylinder. Spatially resolved neutron diffraction measurements on the HIgh Pressure Preferred Orientation time-of-flight diffractometer (HIPPO) and Spectrometer for Materials Research at Temperature and Stress diffractometer (SMARTS) beamlines at the Los Alamos Neutron Science CEnter (LANSCE) with Rietveld and single-peak analysis were used to quantitatively evaluate the volume fractions of the α, γ, and ε phases as well as residualmore » strain and texture. The texture of the γ phase is consistent with uniaxial compression, while the α texture can be explained by the Kurdjumov–Sachs relationship from the γ texture after deformation. This indicates that the material first deformed in the γ phase and subsequently transformed at larger strains. The ε phase was only found in volumes close to the undeformed material with a texture connected to the γ texture by the Shoji–Nishiyama orientation relationship. This allows us to conclude that the ε phase occurs as an intermediate phase at lower strain, and is superseded by the α phase when strain increases further. We found a proportionality between the root-mean-squared microstrain of the γ phase, dominated by the dislocation density, with the α volume fraction, consistent with strain-induced martensite α formation. In conclusion, knowledge of the sample volume with the ε phase from the neutron diffraction analysis allowed us to identify the ε phase by electron back scatter diffraction analysis, complementing the neutron diffraction analysis with characterization on the grain level.« less

T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results.

PubMed

Nketiah, Gabriel; Elschot, Mattijs; Kim, Eugene; Teruel, Jose R; Scheenen, Tom W; Bathen, Tone F; Selnæs, Kirsten M

2017-07-01

To evaluate the diagnostic relevance of T2-weighted (T2W) MRI-derived textural features relative to quantitative physiological parameters derived from diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI in Gleason score (GS) 3+4 and 4+3 prostate cancers. 3T multiparametric-MRI was performed on 23 prostate cancer patients prior to prostatectomy. Textural features [angular second moment (ASM), contrast, correlation, entropy], apparent diffusion coefficient (ADC), and DCE pharmacokinetic parameters (K trans and V e ) were calculated from index tumours delineated on the T2W, DW, and DCE images, respectively. The association between the textural features and prostatectomy GS and the MRI-derived parameters, and the utility of the parameters in differentiating between GS 3+4 and 4+3 prostate cancers were assessed statistically. ASM and entropy correlated significantly (p < 0.05) with both GS and median ADC. Contrast correlated moderately with median ADC. The textural features correlated insignificantly with K trans and V e . GS 4+3 cancers had significantly lower ASM and higher entropy than 3+4 cancers, but insignificant differences in median ADC, K trans , and V e . The combined texture-MRI parameters yielded higher classification accuracy (91%) than the individual parameter sets. T2W MRI-derived textural features could serve as potential diagnostic markers, sensitive to the pathological differences in prostate cancers. • T2W MRI-derived textural features correlate significantly with Gleason score and ADC. • T2W MRI-derived textural features differentiate Gleason score 3+4 from 4+3 cancers. • T2W image textural features could augment tumour characterization.

The Wear Behavior of Textured Steel Sliding against Polymers

PubMed Central

Wang, Meiling; Zhang, Changtao; Wang, Xiaolei

2017-01-01

Artificially fabricated surface textures can significantly improve the friction and wear resistance of a tribological contact. Recently, this surface texturing technique has been applied to polymer materials to improve their tribological performance. However, the wear behavior of textured tribo-pairs made of steel and polymer materials has been less thoroughly investigated and is not well understood; thus, it needs further research. The aim of this study is to investigate the wear properties of tribological contacts made of textured stainless steel against polymer surfaces. Three polymer materials were selected in this study, namely, ultrahigh molecular weight polyethylene (UHMWPE), polyoxymethylene (POM) and (polyetheretherketone) PEEK. Wear tests were operated through a ring-on-plane mode. The results revealed that the texture features and material properties affected the wear rates and friction coefficients of the textured tribo-pairs. In general, PEEK/textured steel achieved the lowest wear rate among the three types of tribo-pairs investigated. Energy dispersive x-ray spectroscopy (EDX) analysis revealed that the elements of C and O on the contacting counterfaces varied with texture features and indicated different wear behavior. Experimental and simulated results showed differences in the stress distribution around the dimple edge, which may influence wear performance. Wear debris with different surface morphologies were found for tribo-pairs with varying texture features. This study has increased the understanding of the wear behavior of tribo-pairs between textured stainless steel and polymer materials. PMID:28772688

MRI textures as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma

NASA Astrophysics Data System (ADS)

Langenhuizen, P. P. J. H.; Legters, M. J. W.; Zinger, S.; Verheul, H. B.; Leenstra, S.; de With, P. H. N.

2018-02-01

Vestibular schwannomas (VS) are benign brain tumors that can be treated with high-precision focused radiation with the Gamma Knife in order to stop tumor growth. Outcome prediction of Gamma Knife radiosurgery (GKRS) treatment can help in determining whether GKRS will be effective on an individual patient basis. However, at present, prognostic factors of tumor control after GKRS for VS are largely unknown, and only clinical factors, such as size of the tumor at treatment and pre-treatment growth rate of the tumor, have been considered thus far. This research aims at outcome prediction of GKRS by means of quantitative texture feature analysis on conventional MRI scans. We compute first-order statistics and features based on gray-level co- occurrence (GLCM) and run-length matrices (RLM), and employ support vector machines and decision trees for classification. In a clinical dataset, consisting of 20 tumors showing treatment failure and 20 tumors exhibiting treatment success, we have discovered that the second-order statistical metrics distilled from GLCM and RLM are suitable for describing texture, but are slightly outperformed by simple first-order statistics, like mean, standard deviation and median. The obtained prediction accuracy is about 85%, but a final choice of the best feature can only be made after performing more extensive analyses on larger datasets. In any case, this work provides suitable texture measures for successful prediction of GKRS treatment outcome for VS.

Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

PubMed

Agner, Shannon C; Soman, Salil; Libfeld, Edward; McDonald, Margie; Thomas, Kathleen; Englander, Sarah; Rosen, Mark A; Chin, Deanna; Nosher, John; Madabhushi, Anant

2011-06-01

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91.

Semantic attributes based texture generation

NASA Astrophysics Data System (ADS)

Chi, Huifang; Gan, Yanhai; Qi, Lin; Dong, Junyu; Madessa, Amanuel Hirpa

2018-04-01

Semantic attributes are commonly used for texture description. They can be used to describe the information of a texture, such as patterns, textons, distributions, brightness, and so on. Generally speaking, semantic attributes are more concrete descriptors than perceptual features. Therefore, it is practical to generate texture images from semantic attributes. In this paper, we propose to generate high-quality texture images from semantic attributes. Over the last two decades, several works have been done on texture synthesis and generation. Most of them focusing on example-based texture synthesis and procedural texture generation. Semantic attributes based texture generation still deserves more devotion. Gan et al. proposed a useful joint model for perception driven texture generation. However, perceptual features are nonobjective spatial statistics used by humans to distinguish different textures in pre-attentive situations. To give more describing information about texture appearance, semantic attributes which are more in line with human description habits are desired. In this paper, we use sigmoid cross entropy loss in an auxiliary model to provide enough information for a generator. Consequently, the discriminator is released from the relatively intractable mission of figuring out the joint distribution of condition vectors and samples. To demonstrate the validity of our method, we compare our method to Gan et al.'s method on generating textures by designing experiments on PTD and DTD. All experimental results show that our model can generate textures from semantic attributes.

Interior car noise created by textured pavement surfaces : final report.

DOT National Transportation Integrated Search

1975-01-01

Because of widespread concern about the effect of textured pavement surfaces on interior car noise, sound pressure levels (SPL) were measured inside a test vehicle as it traversed 21 pavements with various textures. A linear regression analysis run o...

Energy Dispersive Spectrometry and Quantitative Analysis Short Course. Introduction to X-ray Energy Dispersive Spectrometry and Quantitative Analysis

NASA Technical Reports Server (NTRS)

Carpenter, Paul; Curreri, Peter A. (Technical Monitor)

2002-01-01

This course will cover practical applications of the energy-dispersive spectrometer (EDS) to x-ray microanalysis. Topics covered will include detector technology, advances in pulse processing, resolution and performance monitoring, detector modeling, peak deconvolution and fitting, qualitative and quantitative analysis, compositional mapping, and standards. An emphasis will be placed on use of the EDS for quantitative analysis, with discussion of typical problems encountered in the analysis of a wide range of materials and sample geometries.

Use of feature extraction techniques for the texture and context information in ERTS imagery: Spectral and textural processing of ERTS imagery. [classification of Kansas land use

NASA Technical Reports Server (NTRS)

Haralick, R. H. (Principal Investigator); Bosley, R. J.

1974-01-01

The author has identified the following significant results. A procedure was developed to extract cross-band textural features from ERTS MSS imagery. Evolving from a single image texture extraction procedure which uses spatial dependence matrices to measure relative co-occurrence of nearest neighbor grey tones, the cross-band texture procedure uses the distribution of neighboring grey tone N-tuple differences to measure the spatial interrelationships, or co-occurrences, of the grey tone N-tuples present in a texture pattern. In both procedures, texture is characterized in such a way as to be invariant under linear grey tone transformations. However, the cross-band procedure complements the single image procedure by extracting texture information and spectral information contained in ERTS multi-images. Classification experiments show that when used alone, without spectral processing, the cross-band texture procedure extracts more information than the single image texture analysis. Results show an improvement in average correct classification from 86.2% to 88.8% for ERTS image no. 1021-16333 with the cross-band texture procedure. However, when used together with spectral features, the single image texture plus spectral features perform better than the cross-band texture plus spectral features, with an average correct classification of 93.8% and 91.6%, respectively.

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

Using Qualitative Hazard Analysis to Guide Quantitative Safety Analysis

NASA Technical Reports Server (NTRS)

Shortle, J. F.; Allocco, M.

2005-01-01

Quantitative methods can be beneficial in many types of safety investigations. However, there are many difficulties in using quantitative m ethods. Far example, there may be little relevant data available. This paper proposes a framework for using quantitative hazard analysis to prioritize hazard scenarios most suitable for quantitative mziysis. The framework first categorizes hazard scenarios by severity and likelihood. We then propose another metric "modeling difficulty" that desc ribes the complexity in modeling a given hazard scenario quantitatively. The combined metrics of severity, likelihood, and modeling difficu lty help to prioritize hazard scenarios for which quantitative analys is should be applied. We have applied this methodology to proposed concepts of operations for reduced wake separation for airplane operatio ns at closely spaced parallel runways.

Breast histopathology image segmentation using spatio-colour-texture based graph partition method.

PubMed

Belsare, A D; Mushrif, M M; Pangarkar, M A; Meshram, N

2016-06-01

This paper proposes a novel integrated spatio-colour-texture based graph partitioning method for segmentation of nuclear arrangement in tubules with a lumen or in solid islands without a lumen from digitized Hematoxylin-Eosin stained breast histology images, in order to automate the process of histology breast image analysis to assist the pathologists. We propose a new similarity based super pixel generation method and integrate it with texton representation to form spatio-colour-texture map of Breast Histology Image. Then a new weighted distance based similarity measure is used for generation of graph and final segmentation using normalized cuts method is obtained. The extensive experiments carried shows that the proposed algorithm can segment nuclear arrangement in normal as well as malignant duct in breast histology tissue image. For evaluation of the proposed method the ground-truth image database of 100 malignant and nonmalignant breast histology images is created with the help of two expert pathologists and the quantitative evaluation of proposed breast histology image segmentation has been performed. It shows that the proposed method outperforms over other methods. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

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

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.

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

Quantitative evaluation of skeletal muscle defects in second harmonic generation images.

PubMed

Liu, Wenhua; Raben, Nina; Ralston, Evelyn

2013-02-01

Skeletal muscle pathologies cause irregularities in the normally periodic organization of the myofibrils. Objective grading of muscle morphology is necessary to assess muscle health, compare biopsies, and evaluate treatments and the evolution of disease. To facilitate such quantitation, we have developed a fast, sensitive, automatic imaging analysis software. It detects major and minor morphological changes by combining texture features and Fourier transform (FT) techniques. We apply this tool to second harmonic generation (SHG) images of muscle fibers which visualize the repeating myosin bands. Texture features are then calculated by using a Haralick gray-level cooccurrence matrix in MATLAB. Two scores are retrieved from the texture correlation plot by using FT and curve-fitting methods. The sensitivity of the technique was tested on SHG images of human adult and infant muscle biopsies and of mouse muscle samples. The scores are strongly correlated to muscle fiber condition. We named the software MARS (muscle assessment and rating scores). It is executed automatically and is highly sensitive even to subtle defects. We propose MARS as a powerful and unbiased tool to assess muscle health.

Quantitative evaluation of skeletal muscle defects in second harmonic generation images

NASA Astrophysics Data System (ADS)

Liu, Wenhua; Raben, Nina; Ralston, Evelyn

2013-02-01

Skeletal muscle pathologies cause irregularities in the normally periodic organization of the myofibrils. Objective grading of muscle morphology is necessary to assess muscle health, compare biopsies, and evaluate treatments and the evolution of disease. To facilitate such quantitation, we have developed a fast, sensitive, automatic imaging analysis software. It detects major and minor morphological changes by combining texture features and Fourier transform (FT) techniques. We apply this tool to second harmonic generation (SHG) images of muscle fibers which visualize the repeating myosin bands. Texture features are then calculated by using a Haralick gray-level cooccurrence matrix in MATLAB. Two scores are retrieved from the texture correlation plot by using FT and curve-fitting methods. The sensitivity of the technique was tested on SHG images of human adult and infant muscle biopsies and of mouse muscle samples. The scores are strongly correlated to muscle fiber condition. We named the software MARS (muscle assessment and rating scores). It is executed automatically and is highly sensitive even to subtle defects. We propose MARS as a powerful and unbiased tool to assess muscle health.

Relation between consumers' perceptions of color and texture of dairy desserts and instrumental measurements using a generalized procrustes analysis.

PubMed

González-Tomás, L; Costell, E

2006-12-01

Consumers' perceptions of the color and texture of 8 commercial vanilla dairy desserts were studied and related to color and rheological measurements. First, the 8 desserts were evaluated by a group of consumers by means of the Free Choice Profile. For both color and texture, a 2-dimensional solution was chosen, with dimension 1 highly related to yellow color intensity in the case of color and to thickness in the case of texture. Second, mechanical spectra, flow behavior, and instrumental color were determined. All the samples showed a time-dependent and shear-thinning flow and a mechanical spectrum typical of a weak gel. Differences were found in the flow index, in the apparent viscosity at 10 s(-1), and in the values of the storage modulus, the loss modulus, the loss angle tangent, and the complex viscosity at 1 Hz, as well as in the color parameters. Finally, sensory and instrumental relationships were investigated by a generalized Procrustes analysis. For both color and texture, a 3-dimensional solution explained a high percentage of the total variance (>80%). In these particular samples, the instrumental color parameters provided more accurate information on consumers' color perceptions than was provided by the rheological parameters of consumers' perceptions of texture.

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

Quantitative Data Analysis--In the Graduate Curriculum

ERIC Educational Resources Information Center

Albers, Michael J.

2017-01-01

A quantitative research study collects numerical data that must be analyzed to help draw the study's conclusions. Teaching quantitative data analysis is not teaching number crunching, but teaching a way of critical thinking for how to analyze the data. The goal of data analysis is to reveal the underlying patterns, trends, and relationships of a…

Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors.

PubMed

Rodriguez Gutierrez, D; Awwad, A; Meijer, L; Manita, M; Jaspan, T; Dineen, R A; Grundy, R G; Auer, D P

2014-05-01

Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features. This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data. ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix. Support vector machine-based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination. These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology. © 2014 by American Journal of Neuroradiology.

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.

Texture analysis of B-mode ultrasound images to stage hepatic lipidosis in the dairy cow: A methodological study.

PubMed

Banzato, Tommaso; Fiore, Enrico; Morgante, Massimo; Manuali, Elisabetta; Zotti, Alessandro

2016-10-01

Hepatic lipidosis is the most diffused hepatic disease in the lactating cow. A new methodology to estimate the degree of fatty infiltration of the liver in lactating cows by means of texture analysis of B-mode ultrasound images is proposed. B-mode ultrasonography of the liver was performed in 48 Holstein Friesian cows using standardized ultrasound parameters. Liver biopsies to determine the triacylglycerol content of the liver (TAGqa) were obtained from each animal. A large number of texture parameters were calculated on the ultrasound images by means of a free software. Based on the TAGqa content of the liver, 29 samples were classified as mild (TAGqa<50mg/g), 6 as moderate (50mg/g100mg/g) and 13 as severe (TAG>100mg/g) in steatosis. Stepwise linear regression analysis was performed to predict the TAGqa content of the liver (TAGpred) from the texture parameters calculated on the ultrasound images. A five-variable model was used to predict the TAG content from the ultrasound images. The regression model explained 83.4% of the variance. An area under the curve (AUC) of 0.949 was calculated for <50mg/g vs >50mg/g of TAGqa; using an optimal cut-off value of 72mg/g TAGpred had a sensitivity of 86.2% and a specificity of 84.2%. An AUC of 0.978 for <100mg/g vs >100mg/g of TAGqa was calculated; using an optimal cut-off value of 89mg/g, TAGpred sensitivity was 92.3% and specificity was 88.6%. Texture analysis of B-mode ultrasound images may therefore be used to accurately predict the TAG content of the liver in lactating cows. Copyright © 2016 Elsevier Ltd. All rights reserved.

Sensory descriptive quantitative analysis of unpasteurized and pasteurized juçara pulp (Euterpe edulis) during long-term storage

PubMed Central

da Silva, Paula Porrelli Moreira; Casemiro, Renata Cristina; Zillo, Rafaela Rebessi; de Camargo, Adriano Costa; Prospero, Evanilda Teresinha Perissinotto; Spoto, Marta Helena Fillet

2014-01-01

This study evaluated the effect of pasteurization followed by storage under different conditions on the sensory attributes of frozen juçara pulp using quantitative descriptive analysis (QDA). Pasteurization of packed frozen pulp was performed by its immersion in stainless steel tank containing water (80°C) for 5 min, followed by storage under refrigerated and frozen conditions. A trained sensory panel evaluated the samples (6°C) on day 1, 15, 30, 45, 60, 75, and 90. Sensory attributes were separated as follows: appearance (foamy, heterogeneous, purple, brown, oily, and creamy), aroma (sweet and fermented), taste (astringent, bitter, and sweet), and texture (oily and consistent), and compared to a reference material. In general, unpasteurized frozen pulp showed the highest score for foamy appearance, and pasteurized samples showed highest scores to creamy appearance. Pasteurized samples remained stable regarding brown color development while unpasteurized counterparts presented increase. Color is an important attribute related to the product identity. All attributes related to taste and texture remained constant during storage for all samples. Pasteurization followed by storage under frozen conditions has shown to be the best conservation method as samples submitted to such process received the best sensory evaluation, described as foamy, slightly heterogeneous, slightly bitter, and slightly astringent. PMID:25473489

Sensory descriptive quantitative analysis of unpasteurized and pasteurized juçara pulp (Euterpe edulis) during long-term storage.

PubMed

da Silva, Paula Porrelli Moreira; Casemiro, Renata Cristina; Zillo, Rafaela Rebessi; de Camargo, Adriano Costa; Prospero, Evanilda Teresinha Perissinotto; Spoto, Marta Helena Fillet

2014-07-01

This study evaluated the effect of pasteurization followed by storage under different conditions on the sensory attributes of frozen juçara pulp using quantitative descriptive analysis (QDA). Pasteurization of packed frozen pulp was performed by its immersion in stainless steel tank containing water (80°C) for 5 min, followed by storage under refrigerated and frozen conditions. A trained sensory panel evaluated the samples (6°C) on day 1, 15, 30, 45, 60, 75, and 90. Sensory attributes were separated as follows: appearance (foamy, heterogeneous, purple, brown, oily, and creamy), aroma (sweet and fermented), taste (astringent, bitter, and sweet), and texture (oily and consistent), and compared to a reference material. In general, unpasteurized frozen pulp showed the highest score for foamy appearance, and pasteurized samples showed highest scores to creamy appearance. Pasteurized samples remained stable regarding brown color development while unpasteurized counterparts presented increase. Color is an important attribute related to the product identity. All attributes related to taste and texture remained constant during storage for all samples. Pasteurization followed by storage under frozen conditions has shown to be the best conservation method as samples submitted to such process received the best sensory evaluation, described as foamy, slightly heterogeneous, slightly bitter, and slightly astringent.

[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.

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

A parametric texture model based on deep convolutional features closely matches texture appearance for humans.

PubMed

Wallis, Thomas S A; Funke, Christina M; Ecker, Alexander S; Gatys, Leon A; Wichmann, Felix A; Bethge, Matthias

2017-10-01

Our visual environment is full of texture-"stuff" like cloth, bark, or gravel as distinct from "things" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parametric model of texture appearance (convolutional neural network [CNN] model) that uses the features encoded by a deep CNN (VGG-19) with two other models: the venerable Portilla and Simoncelli model and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery ("parafoveal") and when observers were able to make eye movements to all three patches ("inspection"). Under parafoveal viewing, observers were unable to discriminate 10 of 12 original images from CNN model images, and remarkably, the simpler Portilla and Simoncelli model performed slightly better than the CNN model (11 textures). Under foveal inspection, matching CNN features captured appearance substantially better than the Portilla and Simoncelli model (nine compared to four textures), and including the power spectrum improved appearance matching for two of the three remaining textures. None of the models we test here could produce indiscriminable images for one of the 12 textures under the inspection condition. While deep CNN (VGG-19) features can often be used to synthesize textures that humans cannot discriminate from natural textures, there is currently no uniformly best model for all textures and viewing conditions.

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

Iris texture traits show associations with iris color and genomic ancestry.

PubMed

Quillen, Ellen E; Guiltinan, Jenna S; Beleza, Sandra; Rocha, Jorge; Pereira, Rinaldo W; Shriver, Mark D

2011-01-01

This study seeks to identify associations among genomic biogeographic ancestry (BGA), quantitative iris color, and iris texture traits contributing to population-level variation in these phenotypes. DNA and iris photographs were collected from 300 individuals across three variably admixed populations (Portugal, Brazil, and Cape Verde). Two raters scored the photos for pigmentation spots, Fuchs' crypts, contraction furrows, and Wolflinn nodes. Iris color was quantified from RGB values. Maximum likelihood estimates of individual BGA were calculated from 176 ancestry informative markers. Pigmentation spots, Fuchs' crypts, contraction furrows, and iris color show significant positive correlation with increasing European BGA. Only contraction furrows are correlated with iris color. The relationship between BGA and iris texture illustrates a genetic contribution to this population-level variation. Copyright © 2011 Wiley-Liss, Inc.

BMI and WHR Are Reflected in Female Facial Shape and Texture: A Geometric Morphometric Image Analysis.

PubMed

Mayer, Christine; Windhager, Sonja; Schaefer, Katrin; Mitteroecker, Philipp

2017-01-01

Facial markers of body composition are frequently studied in evolutionary psychology and are important in computational and forensic face recognition. We assessed the association of body mass index (BMI) and waist-to-hip ratio (WHR) with facial shape and texture (color pattern) in a sample of young Middle European women by a combination of geometric morphometrics and image analysis. Faces of women with high BMI had a wider and rounder facial outline relative to the size of the eyes and lips, and relatively lower eyebrows. Furthermore, women with high BMI had a brighter and more reddish skin color than women with lower BMI. The same facial features were associated with WHR, even though BMI and WHR were only moderately correlated. Yet BMI was better predictable than WHR from facial attributes. After leave-one-out cross-validation, we were able to predict 25% of variation in BMI and 10% of variation in WHR by facial shape. Facial texture predicted only about 3-10% of variation in BMI and WHR. This indicates that facial shape primarily reflects total fat proportion, rather than the distribution of fat within the body. The association of reddish facial texture in high-BMI women may be mediated by increased blood pressure and superficial blood flow as well as diet. Our study elucidates how geometric morphometric image analysis serves to quantify the effect of biological factors such as BMI and WHR to facial shape and color, which in turn contributes to social perception.

Methods of making textured catalysts

DOEpatents

Werpy, Todd [West Richland, WA; Frye, Jr., John G.; Wang, Yong [Richland, WA; Zacher, Alan H [Kennewick, WA

2010-08-17

A textured catalyst having a hydrothermally-stable support, a metal oxide and a catalyst component is described. Methods of conducting aqueous phase reactions that are catalyzed by a textured catalyst are also described. The invention also provides methods of making textured catalysts and methods of making chemical products using a textured catalyst.

Combined radiogrammetry and texture analysis for early diagnosis of osteoporosis using Indian and Swiss data.

PubMed

Areeckal, Anu Shaju; Kamath, Jagannath; Zawadynski, Sophie; Kocher, Michel; S, Sumam David

2018-05-26

Osteoporosis is a bone disorder characterized by bone loss and decreased bone strength. The most widely used technique for detection of osteoporosis is the measurement of bone mineral density (BMD) using dual energy X-ray absorptiometry (DXA). But DXA scans are expensive and not widely available in low-income economies. In this paper, we propose a low cost pre-screening tool for the detection of low bone mass, using cortical radiogrammetry of third metacarpal bone and trabecular texture analysis of distal radius from hand and wrist radiographs. An automatic segmentation algorithm to automatically locate and segment the third metacarpal bone and distal radius region of interest (ROI) is proposed. Cortical measurements such as combined cortical thickness (CCT), cortical area (CA), percent cortical area (PCA) and Barnett Nordin index (BNI) were taken from the shaft of third metacarpal bone. Texture analysis of trabecular network at the distal radius was performed using features obtained from histogram, gray level Co-occurrence matrix (GLCM) and morphological gradient method (MGM). The significant cortical and texture features were selected using independent sample t-test and used to train classifiers to classify healthy subjects and people with low bone mass. The proposed pre-screening tool was validated on two ethnic groups, Indian sample population and Swiss sample population. Data of 134 subjects from Indian sample population and 65 subjects from Swiss sample population were analysed. The proposed automatic segmentation approach shows a detection accuracy of 86% in detecting the third metacarpal bone shaft and 90% in accurately locating the distal radius ROI. Comparison of the automatic radiogrammetry to the ground truth provided by experts show a mean absolute error of 0.04 mm for cortical width of healthy group, 0.12 mm for cortical width of low bone mass group, 0.22 mm for medullary width of healthy group, and 0.26 mm for medullary width of low bone mass

Quantitative Analysis of {sup 18}F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

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

Cui, Yi; Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo; Song, Jie

Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT {sup 18}F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162more » robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e

Model-Based Linkage Analysis of a Quantitative Trait.

PubMed

Song, Yeunjoo E; Song, Sunah; Schnell, Audrey H

2017-01-01

Linkage Analysis is a family-based method of analysis to examine whether any typed genetic markers cosegregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single-marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it also includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single-marker analysis, and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

Real-time color-based texture analysis for sophisticated defect detection on wooden surfaces

NASA Astrophysics Data System (ADS)

Polzleitner, Wolfgang; Schwingshakl, Gert

2004-10-01

We describe a scanning system developed for the classification and grading of surfaces of wooden tiles. The system uses color imaging sensors to analyse the surfaces of either hard- or softwood material in terms of the texture formed by grain lines (orientation, spatial frequency, and color), various types of colorization, and other defects like knots, heart wood, cracks, holes, etc. The analysis requires two major tracks: the assignment of a tile to its texture class (like A, B, C, 1, 2, 3, Waste), and the detection of defects that decrease the commercial value of the tile (heart wood, knots, etc.). The system was initially developed under the international IMS program (Intelligent Manufacturing Systems) by an industry consortium. During the last two years it has been further developed, and several industrial systems have been installed, and are presently used in production of hardwood flooring. The methods implemented reflect some of the latest developments in the field of pattern recognition: genetic feature selection, two-dimensional second order statistics, special color space transforms, and classification by neural networks. In the industrial scenario we describe, many of the features defining a class cannot be described mathematically. Consequently a focus was the design of a learning architecture, where prototype texture samples are presented to the system, which then automatically finds the internal representation necessary for classification. The methods used in this approach have a wide applicability to problems of inspection, sorting, and optimization of high-value material typically used in the furniture, flooring, and related wood manufacturing industries.

Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas.

PubMed

Pyka, Thomas; Gempt, Jens; Hiob, Daniela; Ringel, Florian; Schlegel, Jürgen; Bette, Stefanie; Wester, Hans-Jürgen; Meyer, Bernhard; Förster, Stefan

2016-01-01

Amino acid positron emission tomography (PET) with [18F]-fluoroethyl-L-tyrosine (FET) is well established in the diagnostic work-up of malignant brain tumors. Analysis of FET-PET data using tumor-to-background ratios (TBR) has been shown to be highly valuable for the detection of viable hypermetabolic brain tumor tissue; however, it has not proven equally useful for tumor grading. Recently, textural features in 18-fluorodeoxyglucose-PET have been proposed as a method to quantify the heterogeneity of glucose metabolism in a variety of tumor entities. Herein we evaluate whether textural FET-PET features are of utility for grading and prognostication in patients with high-grade gliomas. One hundred thirteen patients (70 men, 43 women) with histologically proven high-grade gliomas were included in this retrospective study. All patients received static FET-PET scans prior to first-line therapy. TBR (max and mean), volumetric parameters and textural parameters based on gray-level neighborhood difference matrices were derived from static FET-PET images. Receiver operating characteristic (ROC) and discriminant function analyses were used to assess the value for tumor grading. Kaplan-Meier curves and univariate and multivariate Cox regression were employed for analysis of progression-free and overall survival. All FET-PET textural parameters showed the ability to differentiate between World Health Organization (WHO) grade III and IV tumors (p < 0.001; AUC 0.775). Further improvement in discriminatory power was possible through a combination of texture and metabolic tumor volume, classifying 85 % of tumors correctly (AUC 0.830). TBR and volumetric parameters alone were correlated with tumor grade, but showed lower AUC values (0.644 and 0.710, respectively). Furthermore, a correlation of FET-PET texture but not TBR was shown with patient PFS and OS, proving significant in multivariate analysis as well. Volumetric parameters were predictive for OS, but this correlation

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.

Influence of grain size and texture prior to warm rolling on microstructure, texture and magnetic properties of Fe-6.5 wt% Si steel

NASA Astrophysics Data System (ADS)

Xu, H. J.; Xu, Y. B.; Jiao, H. T.; Cheng, S. F.; Misra, R. D. K.; Li, J. P.

2018-05-01

Fe-6.5 wt% Si steel hot bands with different initial grain size and texture were obtained through different annealing treatment. These bands were then warm rolled and annealed. An analysis on the evolution of microstructure and texture, particularly the formation of recrystallization texture was studied. The results indicated that initial grain size and texture had a significant effect on texture evolution and magnetic properties. Large initial grains led to coarse deformed grains with dense and long shear bands after warm rolling. Such long shear bands resulted in growth advantage for {1 1 3} 〈3 6 1〉 oriented grains during recrystallization. On the other hand, sharp {11 h} 〈1, 2, 1/h〉 (α∗-fiber) texture in the coarse-grained sample led to dominant {1 1 2} 〈1 1 0〉 texture after warm rolling. Such {1 1 2} 〈1 1 0〉 deformed grains provided massive nucleation sites for {1 1 3} 〈3 6 1〉 oriented grains during subsequent recrystallization. These {1 1 3} 〈3 6 1〉 grains were confirmed to exhibit an advantage on grain growth compared to γ-fiber grains. As a result, significant {1 1 3} 〈3 6 1〉 texture was developed and unfavorable γ-fiber texture was inhibited in the final annealed sheet. Both these aspects led to superior magnetic properties in the sample with largest initial grain size. The magnetic induction B8 was 1.36 T and the high frequency core loss P10/400 was 17.07 W/kg.

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.

Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain.

PubMed

Latha, Manohar; Kavitha, Ganesan

2018-02-03

Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features. T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ. The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005). A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.

Parameter optimization of parenchymal texture analysis for prediction of false-positive recalls from screening mammography

NASA Astrophysics Data System (ADS)

Ray, Shonket; Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina

2016-03-01

This work details a methodology to obtain optimal parameter values for a locally-adaptive texture analysis algorithm that extracts mammographic texture features representative of breast parenchymal complexity for predicting falsepositive (FP) recalls from breast cancer screening with digital mammography. The algorithm has two components: (1) adaptive selection of localized regions of interest (ROIs) and (2) Haralick texture feature extraction via Gray- Level Co-Occurrence Matrices (GLCM). The following parameters were systematically varied: mammographic views used, upper limit of the ROI window size used for adaptive ROI selection, GLCM distance offsets, and gray levels (binning) used for feature extraction. Each iteration per parameter set had logistic regression with stepwise feature selection performed on a clinical screening cohort of 474 non-recalled women and 68 FP recalled women; FP recall prediction was evaluated using area under the curve (AUC) of the receiver operating characteristic (ROC) and associations between the extracted features and FP recall were assessed via odds ratios (OR). A default instance of mediolateral (MLO) view, upper ROI size limit of 143.36 mm (2048 pixels2), GLCM distance offset combination range of 0.07 to 0.84 mm (1 to 12 pixels) and 16 GLCM gray levels was set. The highest ROC performance value of AUC=0.77 [95% confidence intervals: 0.71-0.83] was obtained at three specific instances: the default instance, upper ROI window equal to 17.92 mm (256 pixels2), and gray levels set to 128. The texture feature of sum average was chosen as a statistically significant (p<0.05) predictor and associated with higher odds of FP recall for 12 out of 14 total instances.

Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging.

PubMed

Banerjee, Imon; Malladi, Sadhika; Lee, Daniela; Depeursinge, Adrien; Telli, Melinda; Lipson, Jafi; Golden, Daniel; Rubin, Daniel L

2018-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is sensitive but not specific to determining treatment response in early stage triple-negative breast cancer (TNBC) patients. We propose an efficient computerized technique for assessing treatment response, specifically the residual tumor (RT) status and pathological complete response (pCR), in response to neoadjuvant chemotherapy. The proposed approach is based on Riesz wavelet analysis of pharmacokinetic maps derived from noninvasive DCE-MRI scans, obtained before and after treatment. We compared the performance of Riesz features with the traditional gray level co-occurrence matrices and a comprehensive characterization of the lesion that includes a wide range of quantitative features (e.g., shape and boundary). We investigated a set of predictive models ([Formula: see text]) incorporating distinct combinations of quantitative characterizations and statistical models at different time points of the treatment and some area under the receiver operating characteristic curve (AUC) values we reported are above 0.8. The most efficient models are based on first-order statistics and Riesz wavelets, which predicted RT with an AUC value of 0.85 and pCR with an AUC value of 0.83, improving results reported in a previous study by [Formula: see text]. Our findings suggest that Riesz texture analysis of TNBC lesions can be considered a potential framework for optimizing TNBC patient care.

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.

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 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

Computational study of textured ferroelectric polycrystals: Dielectric and piezoelectric properties of template-matrix composites

NASA Astrophysics Data System (ADS)

Zhou, Jie E.; Yan, Yongke; Priya, Shashank; Wang, Yu U.

2017-01-01

Quantitative relationships between processing, microstructure, and properties in textured ferroelectric polycrystals and the underlying responsible mechanisms are investigated by phase field modeling and computer simulation. This study focuses on three important aspects of textured ferroelectric ceramics: (i) grain microstructure evolution during templated grain growth processing, (ii) crystallographic texture development as a function of volume fraction and seed size of the templates, and (iii) dielectric and piezoelectric properties of the obtained template-matrix composites of textured polycrystals. Findings on the third aspect are presented here, while an accompanying paper of this work reports findings on the first two aspects. In this paper, the competing effects of crystallographic texture and template seed volume fraction on the dielectric and piezoelectric properties of ferroelectric polycrystals are investigated. The phase field model of ferroelectric composites consisting of template seeds embedded in matrix grains is developed to simulate domain evolution, polarization-electric field (P-E), and strain-electric field (ɛ-E) hysteresis loops. The coercive field, remnant polarization, dielectric permittivity, piezoelectric coefficient, and dissipation factor are studied as a function of grain texture and template seed volume fraction. It is found that, while crystallographic texture significantly improves the polycrystal properties towards those of single crystals, a higher volume fraction of template seeds tends to decrease the electromechanical properties, thus canceling the advantage of ferroelectric polycrystals textured by templated grain growth processing. This competing detrimental effect is shown to arise from the composite effect, where the template phase possesses material properties inferior to the matrix phase, causing mechanical clamping and charge accumulation at inter-phase interfaces between matrix and template inclusions. The computational

Soil Texture Estimates: A Tool to Compare Texture-by-Feel and Lab Data

ERIC Educational Resources Information Center

Franzmeier, D.P.; Owens, P.R.

2008-01-01

Soil texture is a fundamental soil property that impacts agricultural and engineering land-use. Comparing texture estimates-by-feel to laboratory-known values to calibrate fingers is a common practice. As educators, it is difficult to assess this field skill consistently and fairly. The instructor may give full credit for the correct texture class…

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

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.

Microstructure, crystallographic texture and mechanical properties of friction stir welded AA2017A

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

Ahmed, M.M.Z., E-mail: mohamed_ahmed4@s-petrol.suez.edu.eg; Department of Metallurgical and Materials Engineering, Suez Canal University, Suez 43721; Wynne, B.P.

2012-02-15

In this study a thick section (20 mm) friction stir welded AA2017A-T451 has been characterized in terms of microstructure, crystallographic texture and mechanical properties. For microstructural analysis both optical and scanning electron microscopes have been used. A detailed crystallographic texture analysis has been carried out using the electron back scattering diffraction technique. Crystallographic texture has been examined in both shoulder and probe affected regions of the weld NG. An entirely weak texture is observed at the shoulder affected region which is mainly explained by the effect of the sequential multi pass deformation experienced by both tool probe and tool shoulder.more » The texture in the probe dominated region at the AS side of the weld is relatively weak but still assembles the simple shear texture of FCC metals with B/B{sup Macron} and C components existing across the whole map. However, the texture is stronger at the RS than at the AS of the weld, mainly dominated byB/B{sup Macron} components and with C component almost absent across the map. An alternating bands between (B) components and (B{sup Macron }) component are observed only at the AS side of the weld. - Highlights: Black-Right-Pointing-Pointer Detailed investigation of microstructure and crystallographic texture. Black-Right-Pointing-Pointer The grain size is varied from the top to the bottom of the NG. Black-Right-Pointing-Pointer An entirely weak texture is observed at the shoulder affected region. Black-Right-Pointing-Pointer The texture in the probe affected region is dominated by simple shear texture.« less

Transcriptomic analysis of apple fruit ripening and texture attributes

USDA-ARS?s Scientific Manuscript database

Molecular events regulating cultivar-specific apple fruit ripening and sensory quality are largely unknown. Such knowledge is essential for genomic-assisted apple breeding and postharvest quality management. The ripening behavior and texture attributes of two apple cultivars, ‘Pink Lady’ and ‘Honey...

New method for predicting estrogen receptor status utilizing breast MRI texture kinetic analysis

NASA Astrophysics Data System (ADS)

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

2014-03-01

Magnetic Resonance Imaging (MRI) of breast cancer typically shows that tumors are heterogeneous with spatial variations in blood flow and cell density. Here, we examine the potential link between clinical tumor imaging and the underlying evolutionary dynamics behind heterogeneity in the cellular expression of estrogen receptors (ER) in breast cancer. We assume, in an evolutionary environment, that ER expression will only occur in the presence of significant concentrations of estrogen, which is delivered via the blood stream. Thus, we hypothesize, the expression of ER in breast cancer cells will correlate with blood flow on gadolinium enhanced breast MRI. To test this hypothesis, we performed quantitative analysis of blood flow on dynamic contrast enhanced MRI (DCE-MRI) and correlated it with the ER status of the tumor. Here we present our analytic methods, which utilize a novel algorithm to analyze 20 volumetric DCE-MRI breast cancer tumors. The algorithm generates post initial enhancement (PIE) maps from DCE-MRI and then performs texture features extraction from the PIE map, feature selection, and finally classification of tumors into ER positive and ER negative status. The combined gray level co-occurrence matrices, gray level run length matrices and local binary pattern histogram features allow quantification of breast tumor heterogeneity. The algorithm predicted ER expression with an accuracy of 85% using a Naive Bayes classifier in leave-one-out cross-validation. Hence, we conclude that our data supports the hypothesis that imaging characteristics can, through application of evolutionary principles, provide insights into the cellular and molecular properties of cancer cells.

Dislocation structure in textured zirconium tensile-deformed along rolling and transverse directions determined by X-ray diffraction line profile analysis

NASA Astrophysics Data System (ADS)

Fan, Zhijian; Jóni, Bertalan; Xie, Lei; Ribárik, Gábor; Ungár, Tamás

2018-04-01

Specimens of cold-rolled zirconium were tensile-deformed along the rolling (RD) and the transverse (TD) directions. The stress-strain curves revealed a strong texture dependence. High resolution X-ray line profile analysis was used to determine the prevailing active slip-systems in the specimens with different textures. The reflections in the X-ray diffraction patterns were separated into two groups. One group corresponds to the major and the other group to the random texture component, respectively. The dislocation densities, the subgrain size and the prevailing active slip-systems were evaluated by using the convolutional multiple whole profile (CMWP) procedure. These microstructure parameters were evaluated separately in the two groups of reflections corresponding to the two different texture components. Significant differences were found in both, the evolution of dislocation densities and the development of the fractions of and type slip systems in the RD and TD specimens during tensile deformation. The differences between the RD and TD stress-strain curves are discussed in terms of the differences of the microstructure evolution.

Height perception influenced by texture gradient.

PubMed

Tozawa, Junko

2012-01-01

Three experiments were carried out to examine whether a texture gradient influences perception of relative object height. Previous research implicated texture cues in judgments of object width, but similar influences have not been demonstrated for relative height. In this study, I evaluate a hypothesis that the projective ratio of the number of texture elements covered by the objects combined with the ratio of the retinal object heights determines percepts of relative object height. Density of texture background was varied: four density conditions ranged from no-texture to very dense texture. In experiments 1 and 2, participants judged the height of comparison bar compared to the standard bar positioned on no-texture or textured backgrounds. Results showed relative height judgments differed with texture manipulations, consistent with predictions from a hypothesised combination of the number of texture elements with retinal height (experiment 1), or partially consistent with this hypothesis (experiment 2). In experiment 2, variations in the position of a comparison object showed that comparisons located far from the horizon were judged more poorly than in other positions. In experiment 3 I examined distance perception; relative distance judgments were found to be also affected by textured backgrounds. Results are discussed in terms of Gibson's relational theory and distance calibration theory.

Risk stratification of gallbladder polyps larger than 10 mm using high-resolution ultrasonography and texture analysis.

PubMed

Choi, Tae Won; Kim, Jung Hoon; Park, Sang Joon; Ahn, Su Joa; Joo, Ijin; Han, Joon Koo

2018-01-01

To assess important features for risk stratification of gallbladder (GB) polyps >10 mm using high-resolution ultrasonography (HRUS) and texture analysis. We included 136 patients with GB polyps (>10 mm) who underwent both HRUS and cholecystectomy (non-neoplastic, n = 58; adenomatous, n = 32; and carcinoma, n = 46). Two radiologists retrospectively assessed HRUS findings and texture analysis. Multivariate analysis was performed to identify significant predictors for neoplastic polyps and carcinomas. Single polyp (OR, 3.680-3.856) and larger size (OR, 1.450-1.477) were independently associated with neoplastic polyps (p < 0.05). In a single or polyp >14 mm, sensitivity for differentiating neoplastic from non-neoplastic polyps was 92.3%. To differentiate carcinoma from adenoma, sessile shape (OR, 9.485-41.257), larger size (OR, 1.267-1.303), higher skewness (OR, 6.382) and lower grey-level co-occurrence matrices (GLCM) contrast (OR, 0.963) were significant predictors (p < 0.05). In a polyp >22 mm or sessile, sensitivity for differentiating carcinomas from adenomas was 93.5-95.7%. If a polyp demonstrated at least one HRUS finding and at least one texture feature, the specificity for diagnosing carcinoma was increased to 90.6-93.8%. In a GB polyp >10 mm, single and diameter >14 mm were useful for predicting neoplastic polyps. In neoplastic polyps, sessile shape, diameter >22 mm, higher skewness and lower GLCM contrast were useful for predicting carcinoma. • Risk of neoplastic polyp is low in <14 mm and multiple polyps • A sessile polyp or >22 mm has increased risk for GB carcinomas • Higher skewness and lower GLCM contrast are predictors of GB carcinoma • HRUS is useful for risk stratification of GB polyps >1 cm.

Automatic Segmenting Structures in MRI's Based on Texture Analysis and Fuzzy Logic

NASA Astrophysics Data System (ADS)

Kaur, Mandeep; Rattan, Munish; Singh, Pushpinder

2017-12-01

The purpose of this paper is to present the variational method for geometric contours which helps the level set function remain close to the sign distance function, therefor it remove the need of expensive re-initialization procedure and thus, level set method is applied on magnetic resonance images (MRI) to track the irregularities in them as medical imaging plays a substantial part in the treatment, therapy and diagnosis of various organs, tumors and various abnormalities. It favors the patient with more speedy and decisive disease controlling with lesser side effects. The geometrical shape, the tumor's size and tissue's abnormal growth can be calculated by the segmentation of that particular image. It is still a great challenge for the researchers to tackle with an automatic segmentation in the medical imaging. Based on the texture analysis, different images are processed by optimization of level set segmentation. Traditionally, optimization was manual for every image where each parameter is selected one after another. By applying fuzzy logic, the segmentation of image is correlated based on texture features, to make it automatic and more effective. There is no initialization of parameters and it works like an intelligent system. It segments the different MRI images without tuning the level set parameters and give optimized results for all MRI's.

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.

An extensive analysis of various texture feature extractors to detect Diabetes Mellitus using facial specific regions.

PubMed

Shu, Ting; Zhang, Bob; Yan Tang, Yuan

2017-04-01

Researchers have recently discovered that Diabetes Mellitus can be detected through non-invasive computerized method. However, the focus has been on facial block color features. In this paper, we extensively study the effects of texture features extracted from facial specific regions at detecting Diabetes Mellitus using eight texture extractors. The eight methods are from four texture feature families: (1) statistical texture feature family: Image Gray-scale Histogram, Gray-level Co-occurance Matrix, and Local Binary Pattern, (2) structural texture feature family: Voronoi Tessellation, (3) signal processing based texture feature family: Gaussian, Steerable, and Gabor filters, and (4) model based texture feature family: Markov Random Field. In order to determine the most appropriate extractor with optimal parameter(s), various parameter(s) of each extractor are experimented. For each extractor, the same dataset (284 Diabetes Mellitus and 231 Healthy samples), classifiers (k-Nearest Neighbors and Support Vector Machines), and validation method (10-fold cross validation) are used. According to the experiments, the first and third families achieved a better outcome at detecting Diabetes Mellitus than the other two. The best texture feature extractor for Diabetes Mellitus detection is the Image Gray-scale Histogram with bin number=256, obtaining an accuracy of 99.02%, a sensitivity of 99.64%, and a specificity of 98.26% by using SVM. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quantitative molecular analysis in mantle cell lymphoma.

PubMed

Brízová, H; Hilská, I; Mrhalová, M; Kodet, R

2011-07-01

A molecular analysis has three major roles in modern oncopathology--as an aid in the differential diagnosis, in molecular monitoring of diseases, and in estimation of the potential prognosis. In this report we review the application of the molecular analysis in a group of patients with mantle cell lymphoma (MCL). We demonstrate that detection of the cyclin D1 mRNA level is a molecular marker in 98% of patients with MCL. Cyclin D1 quantitative monitoring is specific and sensitive for the differential diagnosis and for the molecular monitoring of the disease in the bone marrow. Moreover, the dynamics of cyclin D1 in bone marrow reflects the disease development and it predicts the clinical course. We employed the molecular analysis for a precise quantitative detection of proliferation markers, Ki-67, topoisomerase IIalpha, and TPX2, that are described as effective prognostic factors. Using the molecular approach it is possible to measure the proliferation rate in a reproducible, standard way which is an essential prerequisite for using the proliferation activity as a routine clinical tool. Comparing with immunophenotyping we may conclude that the quantitative PCR-based analysis is a useful, reliable, rapid, reproducible, sensitive and specific method broadening our diagnostic tools in hematopathology. In comparison to interphase FISH in paraffin sections quantitative PCR is less technically demanding and less time-consuming and furthermore it is more sensitive in detecting small changes in the mRNA level. Moreover, quantitative PCR is the only technology which provides precise and reproducible quantitative information about the expression level. Therefore it may be used to demonstrate the decrease or increase of a tumor-specific marker in bone marrow in comparison with a previously aspirated specimen. Thus, it has a powerful potential to monitor the course of the disease in correlation with clinical data.

BMI and WHR Are Reflected in Female Facial Shape and Texture: A Geometric Morphometric Image Analysis

PubMed Central

Mayer, Christine; Windhager, Sonja; Schaefer, Katrin; Mitteroecker, Philipp

2017-01-01

Facial markers of body composition are frequently studied in evolutionary psychology and are important in computational and forensic face recognition. We assessed the association of body mass index (BMI) and waist-to-hip ratio (WHR) with facial shape and texture (color pattern) in a sample of young Middle European women by a combination of geometric morphometrics and image analysis. Faces of women with high BMI had a wider and rounder facial outline relative to the size of the eyes and lips, and relatively lower eyebrows. Furthermore, women with high BMI had a brighter and more reddish skin color than women with lower BMI. The same facial features were associated with WHR, even though BMI and WHR were only moderately correlated. Yet BMI was better predictable than WHR from facial attributes. After leave-one-out cross-validation, we were able to predict 25% of variation in BMI and 10% of variation in WHR by facial shape. Facial texture predicted only about 3–10% of variation in BMI and WHR. This indicates that facial shape primarily reflects total fat proportion, rather than the distribution of fat within the body. The association of reddish facial texture in high-BMI women may be mediated by increased blood pressure and superficial blood flow as well as diet. Our study elucidates how geometric morphometric image analysis serves to quantify the effect of biological factors such as BMI and WHR to facial shape and color, which in turn contributes to social perception. PMID:28052103

Introduction of High Throughput Magnetic Resonance T2-Weighted Image Texture Analysis for WHO Grade 2 and 3 Gliomas.

PubMed

Kinoshita, Manabu; Sakai, Mio; Arita, Hideyuki; Shofuda, Tomoko; Chiba, Yasuyoshi; Kagawa, Naoki; Watanabe, Yoshiyuki; Hashimoto, Naoya; Fujimoto, Yasunori; Yoshimine, Toshiki; Nakanishi, Katsuyuki; Kanemura, Yonehiro

2016-01-01

Reports have suggested that tumor textures presented on T2-weighted images correlate with the genetic status of glioma. Therefore, development of an image analyzing framework that is capable of objective and high throughput image texture analysis for large scale image data collection is needed. The current study aimed to address the development of such a framework by introducing two novel parameters for image textures on T2-weighted images, i.e., Shannon entropy and Prewitt filtering. Twenty-two WHO grade 2 and 28 grade 3 glioma patients were collected whose pre-surgical MRI and IDH1 mutation status were available. Heterogeneous lesions showed statistically higher Shannon entropy than homogenous lesions (p = 0.006) and ROC curve analysis proved that Shannon entropy on T2WI was a reliable indicator for discrimination of homogenous and heterogeneous lesions (p = 0.015, AUC = 0.73). Lesions with well-defined borders exhibited statistically higher Edge mean and Edge median values using Prewitt filtering than those with vague lesion borders (p = 0.0003 and p = 0.0005 respectively). ROC curve analysis also proved that both Edge mean and median values were promising indicators for discrimination of lesions with vague and well defined borders and both Edge mean and median values performed in a comparable manner (p = 0.0002, AUC = 0.81 and p < 0.0001, AUC = 0.83, respectively). Finally, IDH1 wild type gliomas showed statistically lower Shannon entropy on T2WI than IDH1 mutated gliomas (p = 0.007) but no difference was observed between IDH1 wild type and mutated gliomas in Edge median values using Prewitt filtering. The current study introduced two image metrics that reflect lesion texture described on T2WI. These two metrics were validated by readings of a neuro-radiologist who was blinded to the results. This observation will facilitate further use of this technique in future large scale image analysis of glioma.

Emotional effects of dynamic textures

PubMed Central

Toet, Alexander; Henselmans, Menno; Lucassen, Marcel P; Gevers, Theo

2011-01-01

This study explores the effects of various spatiotemporal dynamic texture characteristics on human emotions. The emotional experience of auditory (eg, music) and haptic repetitive patterns has been studied extensively. In contrast, the emotional experience of visual dynamic textures is still largely unknown, despite their natural ubiquity and increasing use in digital media. Participants watched a set of dynamic textures, representing either water or various different media, and self-reported their emotional experience. Motion complexity was found to have mildly relaxing and nondominant effects. In contrast, motion change complexity was found to be arousing and dominant. The speed of dynamics had arousing, dominant, and unpleasant effects. The amplitude of dynamics was also regarded as unpleasant. The regularity of the dynamics over the textures' area was found to be uninteresting, nondominant, mildly relaxing, and mildly pleasant. The spatial scale of the dynamics had an unpleasant, arousing, and dominant effect, which was larger for textures with diverse content than for water textures. For water textures, the effects of spatial contrast were arousing, dominant, interesting, and mildly unpleasant. None of these effects were observed for textures of diverse content. The current findings are relevant for the design and synthesis of affective multimedia content and for affective scene indexing and retrieval. PMID:23145257

SMA texture and reorientation: simulations and neutron diffraction studies

NASA Astrophysics Data System (ADS)

Gao, Xiujie; Brown, Donald W.; Brinson, L. Catherine

2005-05-01

With increased usage of shape memory alloys (SMA) for applications in various fields, it is important to understand how the material behavior is affected by factors such as texture, stress state and loading history, especially for complex multiaxial loading states. Using the in-situ neutron diffraction loading facility (SMARTS diffractometer) and ex situ inverse pole figure measurement facility (HIPPO diffractometer) at the Los Alamos Neutron Science Center (LANCE), the macroscopic mechanical behavior and texture evolution of Nickel-Titanium (Nitinol) SMAs under sequential compression in alternating directions were studied. The simplified multivariant model developed at Northwestern University was then used to simulate the macroscopic behavior and the microstructural change of Nitinol under this sequential loading. Pole figures were obtained via post-processing of the multivariant results for volume fraction evolution and compared quantitatively well to the experimental results. The experimental results can also be used to test or verify other SMA constitutive models.

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.

Strong texturing of lithium metal in batteries

DOE PAGES

Shi, Feifei; Pei, Allen; Vailionis, Arturas; ...

2017-10-30

Lithium, with its high theoretical specific capacity and lowest electrochemical potential, has been recognized as the ultimate negative electrode material for next-generation lithium-based high-energy-density batteries. However, a key challenge that has yet to be overcome is the inferior reversibility of Li plating and stripping, typically thought to be related to the uncontrollable morphology evolution of the Li anode during cycling. Here we show that Li-metal texturing (preferential crystallographic orientation) occurs during electrochemical deposition, which governs the morphological change of the Li anode. X-ray diffraction pole-figure analysis demonstrates that the texture of Li deposits is primarily dependent on the type ofmore » additive or cross-over molecule from the cathode side. With adsorbed additives, like LiNO 3 and polysulfide, the lithium deposits are strongly textured, with Li (110) planes parallel to the substrate, and thus exhibit uniform, rounded morphology. A growth diagram of lithium deposits is given to connect various texture and morphology scenarios for different battery electrolytes. In conclusion, this understanding of lithium electrocrystallization from the crystallographic point of view provides significant insight for future lithium anode materials design in high-energy-density batteries.« less

Strong texturing of lithium metal in batteries

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

Shi, Feifei; Pei, Allen; Vailionis, Arturas

Lithium, with its high theoretical specific capacity and lowest electrochemical potential, has been recognized as the ultimate negative electrode material for next-generation lithium-based high-energy-density batteries. However, a key challenge that has yet to be overcome is the inferior reversibility of Li plating and stripping, typically thought to be related to the uncontrollable morphology evolution of the Li anode during cycling. Here we show that Li-metal texturing (preferential crystallographic orientation) occurs during electrochemical deposition, which governs the morphological change of the Li anode. X-ray diffraction pole-figure analysis demonstrates that the texture of Li deposits is primarily dependent on the type ofmore » additive or cross-over molecule from the cathode side. With adsorbed additives, like LiNO 3 and polysulfide, the lithium deposits are strongly textured, with Li (110) planes parallel to the substrate, and thus exhibit uniform, rounded morphology. A growth diagram of lithium deposits is given to connect various texture and morphology scenarios for different battery electrolytes. In conclusion, this understanding of lithium electrocrystallization from the crystallographic point of view provides significant insight for future lithium anode materials design in high-energy-density batteries.« less

Mechanical and chemical effects of ion-texturing biomedical polymers

NASA Technical Reports Server (NTRS)

Weigand, A. J.; Cenkus, M. A.

1979-01-01

To determine whether sputter etching may provide substantial polymer surface texturing with insignificant changes in chemical and mechanical properties, an 8 cm beam diameter, electron bombardment, argon ion source was used to sputter etch (ion-texture process) nine biomedical polymers. The materials included silicone rubber, 32% carbon impregnated polyolefin, polyoxymethylene, polytetrafluoroethylene, ultrahigh molecular weight (UHMW) polyethylene, UHMW polyethylene with carbon fibers (10%), and several polyurethanes (bioelectric, segmented, and cross linked). Ion textured microtensile specimens of each material except UHMW polyethylene and UHMW polyethylene with 10% carbon fibers were used to determine the effect of ion texturing on tensile properties. Scanning electron microscopy was used to determine surface morphology changes, and electron spectroscopy for chemical analysis was used to analyze the near surface chemical changes that result from ion texturing. Ion energies of 500 eV with beam current densities ranging from 0.08 to 0.19 mA/sq cm were used to ion texture the various materials. Standard microtensile specimens of seven polymers were exposed to a saline environment for 24 hours prior to and during the tensile testing. The surface chemical changes resulting from sputter etching are minimal in spite of the often significant changes in the surface morphology.

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

Textured catalysts, methods of making textured catalysts, and methods of catalyzing reactions conducted in hydrothermal conditions

DOEpatents

Werpy, Todd [West Richland, WA; Wang, Yong [Richland, WA

2003-12-30

A textured catalyst having a hydrothermally-stable support, a metal oxide and a catalyst component is described. Methods of conducting aqueous phase reactions that are catalyzed by a textured catalyst are also described. The invention also provides methods of making textured catalysts and methods of making chemical products using a textured catalyst.

Extruded snacks from whole wheat supplemented with textured soy flour: Effect on instrumental and sensory textural characteristics.

PubMed

Rodríguez-Vidal, Arturo; Martínez-Flores, Héctor Eduardo; González Jasso, Eva; Velázquez de la Cruz, Gonzalo; Ramírez-Jiménez, Aurea K; Morales-Sánchez, Eduardo

2017-06-01

The quality of extruded snacks can be affected not only by processing conditions, but also by some factors like the concentration and type of ingredients incorporated in their formulation and the working conditions used. Although the process conditions have been established with measurable textural properties, sensory qualities have not been correlated with these responses in expanded extruded snacks made with added functional ingredients. Therefore, in this study the effect of adding textured soy flour (TSF) and whole wheat flour (WWF) to refined wheat flour in the production of extruded snacks and expanded with hot air was evaluated. A response surface design using two levels with five central points was applied to obtain the best combinations of functional ingredients added, holding the parameters of the extrusion process and moisture of treatments. Some texture characteristics and sensory analysis were used as response variables, such as, hardness, fracturability, toughness, crispness, granularity, and chewiness. Likewise, the rate of expansion was evaluated. The results showed that the level of substitution of WWF, especially levels of 15%, had a significant effect on the hardness perceived by the panelist during sensory evaluation. The TSF at concentrations of ≥15%, favored the fracturability and crispness of the samples. It was found that the best expansion index was with the combination of 5% TSF and 15% WWF. Although a correlation between instrumental and sensory tests carried out on the extruded snacks expanded was not found. The physical characteristics of the extruded snacks such as expansion, hardness, and density are important parameters in terms of consumer acceptability of the final product as well as their functional properties. In other words, the appearance and texture are two of the most important attributes that can be seen in snack foods. In particular, the texture can be measured by intrinsic tests: objective (instrumental) and subjective

Adhesive behavior of micro/nano-textured surfaces

NASA Astrophysics Data System (ADS)

Zhang, Yuyan; Wang, Xiaoli; Li, Hanqing; Wang, Ben

2015-02-01

A numerical model of the adhesive contact between a rigid smooth sphere and an elastic textured surface based on the Lennard-Jones interatomic potential law and the Hamaker summation method is established. Textures are considered by introducing the texture height distribution into the gap equation. Simulation results show that the pull-off force on textured surfaces decreases compared to that on smooth surfaces. Furthermore, effects of sphere-shaped textures on reducing adhesion are more obvious than cylinder-shaped or cube-shaped textures when the coverage area ratio, maximum height and interval of textures are fixed. For surfaces with sphere-shaped textures, variation trends of the mean pull-off force with texture density are not monotonous, and there exists a certain range of texture densities in which the mean pull-off force is small and its variation is insignificant. In addition, the pull-off force depends also on the maximum height and radius of textures. On one hand, if the texture radius is fixed, larger maximum height results in smaller pull-off force, and if the maximum height is fixed, the pull-off force tends to increase almost linearly with increases in texture radius. On the other hand, if the height-diameter ratio of textures is fixed, the pull-off force reaches a minimum at an optimum texture radius or maximum height.

Effect of barium sulfate contrast medium on rheology and sensory texture attributes in a model food.

PubMed

Ekberg, O; Bulow, M; Ekman, S; Hall, G; Stading, M; Wendin, K

2009-03-01

The swallowing process can be visualized using videoradiography, by mixing food with contrast medium, e.g., barium sulfate (BaSO(4)), making it radiopaque. The sensory properties of foods may be affected by adding this medium. To evaluate if and to what extent sensory and rheological characteristics of mango purée were altered by adding barium sulfate to the food. This study evaluated four food samples based on mango purée, with no or added barium sulfate contrast medium (0%, 12.5%, 25.0%, and 37.5%), by a radiographic method, and measured sensory texture properties and rheological characteristics. The sensory evaluation was performed by an external trained panel using quantitative descriptive analysis. The ease of swallowing the foods was also evaluated. The sensory texture properties of mango purée were significantly affected by the added barium in all evaluated attributes, as was the perception of particles. Moreover, ease of swallowing was significantly higher in the sample without added contrast medium. All samples decreased in extensional viscosity with increasing extension rate, i.e., all samples were tension thinning. Shear viscosity was not as dependent on the concentration of BaSO(4) as extensional viscosity. Addition of barium sulfate to a model food of mango purée has a major impact on perceived sensory texture attributes as well as on rheological parameters.

A subjective study and an objective metric to quantify the granularity level of textures

NASA Astrophysics Data System (ADS)

Subedar, Mahesh M.; Karam, Lina J.

2015-03-01

Texture granularity is an important visual characteristic that is useful in a variety of applications, including analysis, recognition, and compression, to name a few. A texture granularity measure can be used to quantify the perceived level of texture granularity. The granularity level of the textures is influenced by the size of the texture primitives. A primitive is defined as the smallest recognizable repetitive object in the texture. If the texture has large primitives then the perceived granularity level tends to be lower as compared to a texture with smaller primitives. In this work we are presenting a texture granularity database referred as GranTEX which consists of 30 textures with varying levels of primitive sizes and granularity levels. The GranTEX database consists of both natural and man-made textures. A subjective study is conducted to measure the perceived granularity level of textures present in the GranTEX database. An objective metric that automatically measures the perceived granularity level of textures is also presented as part of this work. It is shown that the proposed granularity metric correlates well with the subjective granularity scores.

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.

Investigations on the change of texture of plant cells due to preservative treatments by digital holographic microscopy

NASA Astrophysics Data System (ADS)

Vora, Priyanka; Anand, Arun

2014-10-01

Texture change is observed in preserved fruits and vegetables. Responsible factors for texture change during preservative treatments are cell morphology, cell wall structure, cell turger, water content and some biochemical components, and also the environmental conditions. Digital Holographic microscopy (DHM) is a quantitative phase contrast imaging technique, which provides three dimensional optical thickness profiles of transparent specimen. Using DHM the morphology of plant cells preserved by refrigeration or stored in vinegar or in sodium chloride can be obtained. This information about the spatio-temporal evolution of optical volume and thickness can be an important tool in area of food processing. Also from the three dimensional images, the texture of the cell can be retrieved and can be investigated under varying conditions.

Quantitative analysis and implications of drainage morphometry of the Agula watershed in the semi-arid northern Ethiopia

NASA Astrophysics Data System (ADS)

Fenta, Ayele Almaw; Yasuda, Hiroshi; Shimizu, Katsuyuki; Haregeweyn, Nigussie; Woldearegay, Kifle

2017-11-01

This study aimed at quantitative analysis of morphometric parameters of Agula watershed and its sub-watersheds using remote sensing data, geographic information system, and statistical methods. Morphometric parameters were evaluated from four perspectives: drainage network, watershed geometry, drainage texture, and relief characteristics. A sixth-order river drains Agula watershed and the drainage network is mainly dendritic type. The mean bifurcation ratio ( R b) was 4.46 and at sub-watershed scale, high R b values ( R b > 5) were observed which might be expected in regions of steeply sloping terrain. The longest flow path of Agula watershed is 48.5 km, with knickpoints along the main river which could be attributed to change of lithology and major faults which are common along the rift escarpments. The watershed has elongated shape suggesting low peak flows for longer duration and hence easier flood management. The drainage texture analysis revealed fine drainage which implies the dominance of impermeable soft rock with low resistance against erosion. High relief and steep slopes dominates, by which rough landforms (hills, breaks, and low mountains) make up 76% of the watershed. The S-shaped hypsometric curve with hypsometric integral of 0.4 suggests that Agula watershed is in equilibrium or mature stage of geomorphic evolution. At sub-watershed scale, the derived morphometric parameters were grouped into three clusters (low, moderate, and high) and considerable spatial variability was observed. The results of this study provide information on drainage morphometry that can help better understand the watershed characteristics and serve as a basis for improved planning, management, and decision making to ensure sustainable use of watershed resources.

A texture-component Avrami model for predicting recrystallization textures, kinetics and grain size

NASA Astrophysics Data System (ADS)

Raabe, Dierk

2007-03-01

The study presents an analytical model for predicting crystallographic textures and the final grain size during primary static recrystallization of metals using texture components. The kinetics is formulated as a matrix variant of the Johnson-Mehl-Avrami-Kolmogorov equation. The matrix form is required since the kinetic and crystallographic evolution of the microstructure is described in terms of a limited set of growing (recrystallizing) and swept (deformed) texture components. The number of components required (5-10) defines the order of the matrix since the kinetic coupling occurs between all recrystallizing and all deformed components. Each such couple is characterized by corresponding values for the nucleation energy and grain boundary mobility. The values of these parameters can be obtained by analytical or numerical coarse graining according to a renormalization scheme which replaces many individual grains which grow via recrystallization in a deformed texture component by a single equivalent recrystallization texture component or by fitting to experimental data. Each deformed component is further characterized by an average stored deformation energy. Each element of the kinetic matrix, reflecting one of the possible couplings between a deformed and a recrystallizing texture component, is then derived in each time step by a set of two differential equations. The first equation describes the thermally activated nucleation and growth processes for the expanded (free) volume for a particular couple of a deformed and a recrystallizing texture component and the second equation is used for calculating the constrained (real) volume for that couple which corrects the free volume for those portions of the deformation component which were already swept. The new method is particularly developed for the fast and physically based process simulation of recrystallization textures with respect to processing. The present paper introduces the method and applies it to the

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.

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.

Laser surface texturing of polypropylene to increase adhesive bonding

NASA Astrophysics Data System (ADS)

Mandolfino, Chiara; Pizzorni, Marco; Lertora, Enrico; Gambaro, Carla

2018-05-01

In this paper, the main parameters of laser surface texturing of polymeric substrates have been studied. The final aim of the texturing is to increase the performance of bonded joints of grey-pigmented polypropylene substrates. The experimental investigation was carried out starting from the identification of the most effective treatment parameters, in order to achieve a good texture without compromising the characteristics of the bulk material. For each of these parameters, three values were individuated and 27 sets of samples were realised. The surface treatment was analysed and related to the mechanical characteristics of the bonded joints performing lap-shear tests. A statistical analysis in order to find the most influential parameter completed the work.

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

Surface texturing of fluoropolymers

NASA Technical Reports Server (NTRS)

Banks, B. A.; Mirtich, M. J.; Sovey, J. S. (Inventor)

1982-01-01

A method is disclosed for improving surface texture for adhesive bonding, metal bonding, substrate plating, decal substrate preparation, and biomedical implant applications. The surface to be bonded is dusted in a controlled fashion to produce a disbursed layer of fine mesh particles which serve as masks. The surface texture is produced by impinging gas ions on the masked surface. The textured surface takes the form of pillars or cones. The bonding material, such as a liquid epoxy, flows between the pillars which results in a bond having increased strength. For bonding metals a thin film of metal is vapor or sputter deposited onto the textured surface. Electroplating or electroless plating is then used to increase the metal thickness in the desired amount.

Perceptual asymmetry in texture perception.

PubMed

Williams, D; Julesz, B

1992-07-15

A fundamental property of human visual perception is our ability to distinguish between textures. A concerted effort has been made to account for texture segregation in terms of linear spatial filter models and their nonlinear extensions. However, for certain texture pairs the ease of discrimination changes when the role of figure and ground are reversed. This asymmetry poses a problem for both linear and nonlinear models. We have isolated a property of texture perception that can account for this asymmetry in discrimination: subjective closure. This property, which is also responsible for visual illusions, appears to be explainable by early visual processes alone. Our results force a reexamination of the process of human texture segregation and of some recent models that were introduced to explain it.

[Quantitative data analysis for live imaging of bone.

PubMed

Seno, Shigeto

Bone tissue is a hard tissue, it was difficult to observe the interior of the bone tissue alive. With the progress of microscopic technology and fluorescent probe technology in recent years, it becomes possible to observe various activities of various cells forming bone society. On the other hand, the quantitative increase in data and the diversification and complexity of the images makes it difficult to perform quantitative analysis by visual inspection. It has been expected to develop a methodology for processing microscopic images and data analysis. In this article, we introduce the research field of bioimage informatics which is the boundary area of biology and information science, and then outline the basic image processing technology for quantitative analysis of live imaging data of bone.

Pure shear and simple shear calcite textures. Comparison of experimental, theoretical and natural data

USGS Publications Warehouse

Wenk, H.-R.; Takeshita, T.; Bechler, E.; Erskine, B.G.; Matthies, S.

1987-01-01

The pattern of lattice preferred orientation (texture) in deformed rocks is an expression of the strain path and the acting deformation mechanisms. A first indication about the strain path is given by the symmetry of pole figures: coaxial deformation produces orthorhombic pole figures, while non-coaxial deformation yields monoclinic or triclinic pole figures. More quantitative information about the strain history can be obtained by comparing natural textures with experimental ones and with theoretical models. For this comparison, a representation in the sensitive three-dimensional orientation distribution space is extremely important and efforts are made to explain this concept. We have been investigating differences between pure shear and simple shear deformation incarbonate rocks and have found considerable agreement between textures produced in plane strain experiments and predictions based on the Taylor model. We were able to simulate the observed changes with strain history (coaxial vs non-coaxial) and the profound texture transition which occurs with increasing temperature. Two natural calcite textures were then selected which we interpreted by comparing them with the experimental and theoretical results. A marble from the Santa Rosa mylonite zone in southern California displays orthorhombic pole figures with patterns consistent with low temperature deformation in pure shear. A limestone from the Tanque Verde detachment fault in Arizona has a monoclinic fabric from which we can interpret that 60% of the deformation occurred by simple shear. ?? 1987.