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

PubMed

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

2018-04-01

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

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

PubMed

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

2017-11-09

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

Computer-aided classification of Alzheimer's disease based on support vector machine with combination of cerebral image features in MRI

NASA Astrophysics Data System (ADS)

Jongkreangkrai, C.; Vichianin, Y.; Tocharoenchai, C.; Arimura, H.; Alzheimer's Disease Neuroimaging Initiative

2016-03-01

Several studies have differentiated Alzheimer's disease (AD) using cerebral image features derived from MR brain images. In this study, we were interested in combining hippocampus and amygdala volumes and entorhinal cortex thickness to improve the performance of AD differentiation. Thus, our objective was to investigate the useful features obtained from MRI for classification of AD patients using support vector machine (SVM). T1-weighted MR brain images of 100 AD patients and 100 normal subjects were processed using FreeSurfer software to measure hippocampus and amygdala volumes and entorhinal cortex thicknesses in both brain hemispheres. Relative volumes of hippocampus and amygdala were calculated to correct variation in individual head size. SVM was employed with five combinations of features (H: hippocampus relative volumes, A: amygdala relative volumes, E: entorhinal cortex thicknesses, HA: hippocampus and amygdala relative volumes and ALL: all features). Receiver operating characteristic (ROC) analysis was used to evaluate the method. AUC values of five combinations were 0.8575 (H), 0.8374 (A), 0.8422 (E), 0.8631 (HA) and 0.8906 (ALL). Although “ALL” provided the highest AUC, there were no statistically significant differences among them except for “A” feature. Our results showed that all suggested features may be feasible for computer-aided classification of AD patients.

Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting

PubMed Central

Caie, Peter D.; Zhou, Ying; Turnbull, Arran K.; Oniscu, Anca; Harrison, David J.

2016-01-01

A number of candidate histopathologic factors show promise in identifying stage II colorectal cancer (CRC) patients at a high risk of disease-specific death, however they can suffer from low reproducibility and none have replaced classical pathologic staging. We developed an image analysis algorithm which standardized the quantification of specific histopathologic features and exported a multi-parametric feature-set captured without bias. The image analysis algorithm was executed across a training set (n = 50) and the resultant big data was distilled through decision tree modelling to identify the most informative parameters to sub-categorize stage II CRC patients. The most significant, and novel, parameter identified was the ‘sum area of poorly differentiated clusters’ (AreaPDC). This feature was validated across a second cohort of stage II CRC patients (n = 134) (HR = 4; 95% CI, 1.5– 11). Finally, the AreaPDC was integrated with the significant features within the clinical pathology report, pT stage and differentiation, into a novel prognostic index (HR = 7.5; 95% CI, 3–18.5) which improved upon current clinical staging (HR = 4.26; 95% CI, 1.7– 10.3). The identification of poorly differentiated clusters as being highly significant in disease progression presents evidence to suggest that these features could be the source of novel targets to decrease the risk of disease specific death. PMID:27322148

HoDOr: histogram of differential orientations for rigid landmark tracking in medical images

NASA Astrophysics Data System (ADS)

Tiwari, Abhishek; Patwardhan, Kedar Anil

2018-03-01

Feature extraction plays a pivotal role in pattern recognition and matching. An ideal feature should be invariant to image transformations such as translation, rotation, scaling, etc. In this work, we present a novel rotation-invariant feature, which is based on Histogram of Oriented Gradients (HOG). We compare performance of the proposed approach with the HOG feature on 2D phantom data, as well as 3D medical imaging data. We have used traditional histogram comparison measures such as Bhattacharyya distance and Normalized Correlation Coefficient (NCC) to assess efficacy of the proposed approach under effects of image rotation. In our experiments, the proposed feature performs 40%, 20%, and 28% better than the HOG feature on phantom (2D), Computed Tomography (CT-3D), and Ultrasound (US-3D) data for image matching, and landmark tracking tasks respectively.

Detection of explosives by differential hyperspectral imaging

NASA Astrophysics Data System (ADS)

Dubroca, Thierry; Brown, Gregory; Hummel, Rolf E.

2014-02-01

Our team has pioneered an explosives detection technique based on hyperspectral imaging of surfaces. Briefly, differential reflectometry (DR) shines ultraviolet (UV) and blue light on two close-by areas on a surface (for example, a piece of luggage on a moving conveyer belt). Upon reflection, the light is collected with a spectrometer combined with a charge coupled device (CCD) camera. A computer processes the data and produces in turn differential reflection spectra taken from these two adjacent areas on the surface. This differential technique is highly sensitive and provides spectroscopic data of materials, particularly of explosives. As an example, 2,4,6-trinitrotoluene displays strong and distinct features in differential reflectograms near 420 and 250 nm, that is, in the near-UV region. Similar, but distinctly different features are observed for other explosives. Finally, a custom algorithm classifies the collected spectral data and outputs an acoustic signal if a threat is detected. This paper presents the complete DR hyperspectral imager which we have designed and built from the hardware to the software, complete with an analysis of the device specifications.

Functional imaging of the semantic system: retrieval of sensory-experienced and verbally learned knowledge.

PubMed

Noppeney, Uta; Price, Cathy J

2003-01-01

This paper considers how functional neuro-imaging can be used to investigate the organization of the semantic system and the limitations associated with this technique. The majority of the functional imaging studies of the semantic system have looked for divisions by varying stimulus category. These studies have led to divergent results and no clear anatomical hypotheses have emerged to account for the dissociations seen in behavioral studies. Only a few functional imaging studies have used task as a variable to differentiate the neural correlates of semantic features more directly. We extend these findings by presenting a new study that contrasts tasks that differentially weight sensory (color and taste) and verbally learned (origin) semantic features. Irrespective of the type of semantic feature retrieved, a common semantic system was activated as demonstrated in many previous studies. In addition, the retrieval of verbally learned, but not sensory-experienced, features enhanced activation in medial and lateral posterior parietal areas. We attribute these "verbally learned" effects to differences in retrieval strategy and conclude that evidence for segregation of semantic features at an anatomical level remains weak. We believe that functional imaging has the potential to increase our understanding of the neuronal infrastructure that sustains semantic processing but progress may require multiple experiments until a consistent explanatory framework emerges.

Selection of the best features for leukocytes classification in blood smear microscopic images

NASA Astrophysics Data System (ADS)

Sarrafzadeh, Omid; Rabbani, Hossein; Talebi, Ardeshir; Banaem, Hossein Usefi

2014-03-01

Automatic differential counting of leukocytes provides invaluable information to pathologist for diagnosis and treatment of many diseases. The main objective of this paper is to detect leukocytes from a blood smear microscopic image and classify them into their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte using features that pathologists consider to differentiate leukocytes. Features contain color, geometric and texture features. Colors of nucleus and cytoplasm vary among the leukocytes. Lymphocytes have single, large, round or oval and Monocytes have singular convoluted shape nucleus. Nucleus of Eosinophils is divided into 2 segments and nucleus of Neutrophils into 2 to 5 segments. Lymphocytes often have no granules, Monocytes have tiny granules, Neutrophils have fine granules and Eosinophils have large granules in cytoplasm. Six color features is extracted from both nucleus and cytoplasm, 6 geometric features only from nucleus and 6 statistical features and 7 moment invariants features only from cytoplasm of leukocytes. These features are fed to support vector machine (SVM) classifiers with one to one architecture. The results obtained by applying the proposed method on blood smear microscopic image of 10 patients including 149 white blood cells (WBCs) indicate that correct rate for all classifiers are above 93% which is in a higher level in comparison with previous literatures.

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments.

PubMed

Chang, Yongjun; Paul, Anjan Kumar; Kim, Namkug; Baek, Jung Hwan; Choi, Young Jun; Ha, Eun Ju; Lee, Kang Dae; Lee, Hyoung Shin; Shin, DaeSeock; Kim, Nakyoung

2016-01-01

To develop a semiautomated computer-aided diagnosis (cad) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid cad software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrence matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of cad with visual inspection by expert radiologists based on established gold standards. Most univariate features for this proposed cad system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed cad system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, "axial ratio" and "max probability" in axial images were most frequently included in the optimal feature sets for the authors' proposed cad system, while "shape" and "calcification" in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed cad system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. The use of thyroid cad to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid cad might be considered a viable way to generate a second opinion for radiologists in clinical practice.

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments

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

Chang, Yongjun; Paul, Anjan Kumar; Kim, Namkug, E-mail: namkugkim@gmail.com

Purpose: To develop a semiautomated computer-aided diagnosis (CAD) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. Methods: A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid CAD software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrencemore » matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of CAD with visual inspection by expert radiologists based on established gold standards. Results: Most univariate features for this proposed CAD system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed CAD system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, “axial ratio” and “max probability” in axial images were most frequently included in the optimal feature sets for the authors’ proposed CAD system, while “shape” and “calcification” in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed CAD system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. Conclusions: The use of thyroid CAD to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid CAD might be considered a viable way to generate a second opinion for radiologists in clinical practice.« less

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

PubMed

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

2011-04-01

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

Spinal infections: clinical and imaging features.

PubMed

Arbelaez, Andres; Restrepo, Feliza; Castillo, Mauricio

2014-10-01

Spinal infections represent a group of rare conditions affecting vertebral bodies, intervertebral discs, paraspinal soft tissues, epidural space, meninges, and spinal cord. The causal factors, clinical presentations, and imaging features are a challenge because the difficulty to differentiate them from other conditions, such as degenerative and inflammatory disorders and spinal neoplasm. They require early recognition because delay diagnosis, imaging, and intervention may have devastating consequences especially in children and the elderly. This article reviews the most common spinal infections, their pathophysiologic, clinical manifestation, and their imaging findings.

Inflammatory myofibroblastic tumor: an entity of CT and MR imaging to differentiate from malignant tumors of the sinonasal cavity.

PubMed

Yan, Zhongyu; Wang, Yongzhe; Zhang, Zhengyu

2014-01-01

Inflammatory myofibroblastic tumor (IMT) is chronic inflammatory lesions of unknown origins. The preoperative diagnosis for tumors in the sinonasal cavity is difficult to distinguish between IMT and aggressive malignancy in most cases. The purpose of this study was to evaluate the imaging features of IMT distinguishing the 2 types of tumors. Computed tomography and magnetic resonance imaging were identified retrospectively with IMT in 14 cases and with aggressive malignancy in 38 cases in the sinonasal cavity proven by pathology. Imaging findings were evaluated, including the configuration, extent, margin, calcification, bone involvement, T1WI and T2WI signal intensity, and degree of enhancement. There was a significant difference between IMT and aggressive malignancy regarding the configuration, extension, calcification, bone change, signal intensity and homogeneous on T2-weighted imaging, and degree of enhancement (P < 0.05). Inflammatory myofibroblastic tumor and aggressive malignancy have some different imaging features that could be helpful in the differentiation between the lesions. Bone erosion with sclerosis, calcification when present, typically homogenous and never hyperintense of T2 appearance, and mild enhancement played an important role in differentiating sinonasal IMT from malignancies.

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.

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

PubMed

Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer. 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216. © 2017 International Society for Magnetic Resonance in Medicine.

In Situ Identification of Nanoparticle Structural Information Using Optical Microscopy.

PubMed

Culver, Kayla S B; Liu, Tingting; Hryn, Alexander J; Fang, Ning; Odom, Teri W

2018-05-11

Diffraction-limited optical microscopy lacks the resolution to characterize directly nanoscale features of single nanoparticles. This paper describes how surprisingly rich structural features of small gold nanostars can be identified using differential interference contrast (DIC) microscopy. First, we established a library of structure-property relationships between nanoparticle shape and DIC optical image and then validated the correlation with electrodynamic simulations and electron microscopy. We found that DIC image patterns of single nanostars could be differentiated between 2D and 3D geometries. Also, DIC images could elucidate the symmetry properties and orientation of nanoparticles. Finally, we demonstrated how this wide-field optical technique can be used for in situ characterization of single nanoparticles rotating at a glass-water interface.

Primary breast leiomyosarcoma and synchronous homolateral lung cancer: a case report

PubMed Central

Meroni, Stefano; Voulaz, Emanuele; Alloisio, Marco; De Sanctis, Rita; Bossi, Paola; Cariboni, Umberto; De Simone, Matilde; Cioffi, Ugo

2017-01-01

Radiological and histological features of breast leiomyosarcoma can mimic a wide variety of other breast lesions, such as mesenchymal tumors, breast lymphomas, poorly differentiated carcinomas and metaplastic breast carcinomas. The authors present the case of a 62-year-old woman with a primary breast leiomyosarcoma with synchronous ipsilateral lung adenocarcinoma. The latter was an incidental finding during pre-surgical staging examinations. Clinicopathological, immunophenotypic and imaging features cancer are described. A brief review of the literature on imaging findings and management of breast leiomyosarcoma is presented. The authors discuss the differential diagnoses in breast imaging and of the extra-mammary incidental findings. Surgical resection remains the cornerstone of treatment, while radiation therapy and chemotherapy remain to be defined on a single-patient basis. PMID:29312765

On-line transmission electron microscopic image analysis of chromatin texture for differentiation of thyroid gland tumors.

PubMed

Kriete, A; Schäffer, R; Harms, H; Aus, H M

1987-06-01

Nuclei of the cells from the thyroid gland were analyzed in a transmission electron microscope by direct TV scanning and on-line image processing. The method uses the advantages of a visual-perception model to detect structures in noisy and low-contrast images. The features analyzed include area, a form factor and texture parameters from the second derivative stage. Three tumor-free thyroid tissues, three follicular adenomas, three follicular carcinomas and three papillary carcinomas were studied. The computer-aided cytophotometric method showed that the most significant differences were the statistics of the chromatin texture features of homogeneity and regularity. These findings document the possibility of an automated differentiation of tumors at the ultrastructural level.

Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis.

PubMed

Lakhman, Yulia; Veeraraghavan, Harini; Chaim, Joshua; Feier, Diana; Goldman, Debra A; Moskowitz, Chaya S; Nougaret, Stephanie; Sosa, Ramon E; Vargas, Hebert Alberto; Soslow, Robert A; Abu-Rustum, Nadeem R; Hricak, Hedvig; Sala, Evis

2017-07-01

To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the feasibility of texture analysis (TA). This retrospective study included 41 women (ALM = 22, LMS = 19) imaged with MRI prior to surgery. Two readers (R1, R2) evaluated each lesion for qualitative MR features. Associations between MR features and LMS were evaluated with Fisher's exact test. Accuracy measures were calculated for the four most significant features. TA was performed for 24 patients (ALM = 14, LMS = 10) with uniform imaging following lesion segmentation on axial T2-weighted images. Texture features were pre-selected using Wilcoxon signed-rank test with Bonferroni correction and analyzed with unsupervised clustering to separate LMS from ALM. Four qualitative MR features most strongly associated with LMS were nodular borders, haemorrhage, "T2 dark" area(s), and central unenhanced area(s) (p ≤ 0.0001 each feature/reader). The highest sensitivity [1.00 (95%CI:0.82-1.00)/0.95 (95%CI: 0.74-1.00)] and specificity [0.95 (95%CI:0.77-1.00)/1.00 (95%CI:0.85-1.00)] were achieved for R1/R2, respectively, when a lesion had ≥3 of these four features. Sixteen texture features differed significantly between LMS and ALM (p-values: <0.001-0.036). Unsupervised clustering achieved accuracy of 0.75 (sensitivity: 0.70; specificity: 0.79). Combination of ≥3 qualitative MR features accurately distinguished LMS from ALM. TA was feasible. • Four qualitative MR features demonstrated the strongest statistical association with LMS. • Combination of ≥3 these features could accurately differentiate LMS from ALM. • Texture analysis was a feasible semi-automated approach for lesion categorization.

The ship edge feature detection based on high and low threshold for remote sensing image

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Shengyang

2018-05-01

In this paper, a method based on high and low threshold is proposed to detect the ship edge feature due to the low accuracy rate caused by the noise. Analyze the relationship between human vision system and the target features, and to determine the ship target by detecting the edge feature. Firstly, using the second-order differential method to enhance the quality of image; Secondly, to improvement the edge operator, we introduction of high and low threshold contrast to enhancement image edge and non-edge points, and the edge as the foreground image, non-edge as a background image using image segmentation to achieve edge detection, and remove the false edges; Finally, the edge features are described based on the result of edge features detection, and determine the ship target. The experimental results show that the proposed method can effectively reduce the number of false edges in edge detection, and has the high accuracy of remote sensing ship edge detection.

Evaluation of facial expression in acute pain in cats.

PubMed

Holden, E; Calvo, G; Collins, M; Bell, A; Reid, J; Scott, E M; Nolan, A M

2014-12-01

To describe the development of a facial expression tool differentiating pain-free cats from those in acute pain. Observers shown facial images from painful and pain-free cats were asked to identify if they were in pain or not. From facial images, anatomical landmarks were identified and distances between these were mapped. Selected distances underwent statistical analysis to identify features discriminating pain-free and painful cats. Additionally, thumbnail photographs were reviewed by two experts to identify discriminating facial features between the groups. Observers (n = 68) had difficulty in identifying pain-free from painful cats, with only 13% of observers being able to discriminate more than 80% of painful cats. Analysis of 78 facial landmarks and 80 distances identified six significant factors differentiating pain-free and painful faces including ear position and areas around the mouth/muzzle. Standardised mouth and ear distances when combined showed excellent discrimination properties, correctly differentiating pain-free and painful cats in 98% of cases. Expert review supported these findings and a cartoon-type picture scale was developed from thumbnail images. Initial investigation into facial features of painful and pain-free cats suggests potentially good discrimination properties of facial images. Further testing is required for development of a clinical tool. © 2014 British Small Animal Veterinary Association.

Fourier domain image fusion for differential X-ray phase-contrast breast imaging.

PubMed

Coello, Eduardo; Sperl, Jonathan I; Bequé, Dirk; Benz, Tobias; Scherer, Kai; Herzen, Julia; Sztrókay-Gaul, Anikó; Hellerhoff, Karin; Pfeiffer, Franz; Cozzini, Cristina; Grandl, Susanne

2017-04-01

X-Ray Phase-Contrast (XPC) imaging is a novel technology with a great potential for applications in clinical practice, with breast imaging being of special interest. This work introduces an intuitive methodology to combine and visualize relevant diagnostic features, present in the X-ray attenuation, phase shift and scattering information retrieved in XPC imaging, using a Fourier domain fusion algorithm. The method allows to present complementary information from the three acquired signals in one single image, minimizing the noise component and maintaining visual similarity to a conventional X-ray image, but with noticeable enhancement in diagnostic features, details and resolution. Radiologists experienced in mammography applied the image fusion method to XPC measurements of mastectomy samples and evaluated the feature content of each input and the fused image. This assessment validated that the combination of all the relevant diagnostic features, contained in the XPC images, was present in the fused image as well. Copyright © 2017 Elsevier B.V. All rights reserved.

Sclerosing Cholangitis: Clinicopathologic Features, Imaging Spectrum, and Systemic Approach to Differential Diagnosis.

PubMed

Seo, Nieun; Kim, So Yeon; Lee, Seung Soo; Byun, Jae Ho; Kim, Jin Hee; Kim, Hyoung Jung; Lee, Moon-Gyu

2016-01-01

Sclerosing cholangitis is a spectrum of chronic progressive cholestatic liver disease characterized by inflammation, fibrosis, and stricture of the bile ducts, which can be classified as primary and secondary sclerosing cholangitis. Primary sclerosing cholangitis is a chronic progressive liver disease of unknown cause. On the other hand, secondary sclerosing cholangitis has identifiable causes that include immunoglobulin G4-related sclerosing disease, recurrent pyogenic cholangitis, ischemic cholangitis, acquired immunodeficiency syndrome-related cholangitis, and eosinophilic cholangitis. In this review, we suggest a systemic approach to the differential diagnosis of sclerosing cholangitis based on the clinical and laboratory findings, as well as the typical imaging features on computed tomography and magnetic resonance (MR) imaging with MR cholangiography. Familiarity with various etiologies of sclerosing cholangitis and awareness of their typical clinical and imaging findings are essential for an accurate diagnosis and appropriate management.

A method for medulloblastoma tumor differentiation based on convolutional neural networks and transfer learning

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Arévalo, John; Judkins, Alexander; Madabhushi, Anant; González, Fabio

2015-12-01

Convolutional neural networks (CNN) have been very successful at addressing different computer vision tasks thanks to their ability to learn image representations directly from large amounts of labeled data. Features learned from a dataset can be used to represent images from a different dataset via an approach called transfer learning. In this paper we apply transfer learning to the challenging task of medulloblastoma tumor differentiation. We compare two different CNN models which were previously trained in two different domains (natural and histopathology images). The first CNN is a state-of-the-art approach in computer vision, a large and deep CNN with 16-layers, Visual Geometry Group (VGG) CNN. The second (IBCa-CNN) is a 2-layer CNN trained for invasive breast cancer tumor classification. Both CNNs are used as visual feature extractors of histopathology image regions of anaplastic and non-anaplastic medulloblastoma tumor from digitized whole-slide images. The features from the two models are used, separately, to train a softmax classifier to discriminate between anaplastic and non-anaplastic medulloblastoma image regions. Experimental results show that the transfer learning approach produce competitive results in comparison with the state of the art approaches for IBCa detection. Results also show that features extracted from the IBCa-CNN have better performance in comparison with features extracted from the VGG-CNN. The former obtains 89.8% while the latter obtains 76.6% in terms of average accuracy.

Differentiation of benign and malignant breast lesions by mechanical imaging

PubMed Central

Kearney, Thomas; Pollak, Stanley B.; Rohatgi, Chand; Sarvazyan, Noune; Airapetian, Suren; Browning, Stephanie; Sarvazyan, Armen

2009-01-01

Mechanical imaging yields tissue elasticity map and provides quantitative characterization of a detected pathology. The changes in the surface stress patterns as a function of applied load provide information about the elastic composition and geometry of the underlying tissue structures. The objective of this study is the clinical evaluation of breast mechanical imager for breast lesion characterization and differentiation between benign and malignant lesions. The breast mechanical imager includes a probe with pressure sensor array, an electronic unit providing data acquisition from the pressure sensors and communication with a touch-screen laptop computer. We have developed an examination procedure and algorithms to provide assessment of breast lesion features such as hardness related parameters, mobility, and shape. A statistical Bayesian classifier was constructed to distinguish between benign and malignant lesions by utilizing all the listed features as the input. Clinical results for 179 cases, collected at four different clinical sites, have demonstrated that the breast mechanical imager provides a reliable image formation of breast tissue abnormalities and calculation of lesion features. Malignant breast lesions (histologically confirmed) demonstrated increased hardness and strain hardening as well as decreased mobility and longer boundary length in comparison with benign lesions. Statistical analysis of differentiation capability for 147 benign and 32 malignant lesions revealed an average sensitivity of 91.4% and specificity of 86.8% with a standard deviation of ±6.1%. The area under the receiver operating characteristic curve characterizing benign and malignant lesion discrimination is 86.1% with the confidence interval ranging from 80.3 to 90.9%, with a significance level of P = 0.0001 (area = 50%). The multisite clinical study demonstrated the capability of mechanical imaging for characterization and differentiation of benign and malignant breast lesions. We hypothesize that the breast mechanical imager has the potential to be used as a cost effective device for cancer diagnostics that could reduce the benign biopsy rate, serve as an adjunct to mammography and to be utilized as a screening device for breast cancer detection. PMID:19306059

Imaging Characteristics of Pathologically Proven Thymic Hyperplasia: Identifying Features That Can Differentiate True From Lymphoid Hyperplasia

PubMed Central

Araki, Tetsuro; Sholl, Lynette M.; Gerbaudo, Victor H.; Hatabu, Hiroto; Nishino, Mizuki

2014-01-01

OBJECTIVE The purpose of this article is to investigate the imaging characteristics of pathologically proven thymic hyperplasia and to identify features that can differentiate true hyperplasia from lymphoid hyperplasia. MATERIALS AND METHODS Thirty-one patients (nine men and 22 women; age range, 20–68 years) with pathologically confirmed thymic hyperplasia (18 true and 13 lymphoid) who underwent preoperative CT (n = 27), PET/CT (n = 5), or MRI (n = 6) were studied. The length and thickness of each thymic lobe and the transverse and anterior-posterior diameters and attenuation of the thymus were measured on CT. Thymic morphologic features and heterogeneity on CT and chemical shift on MRI were evaluated. Maximum standardized uptake values were measured on PET. Imaging features between true and lymphoid hyperplasia were compared. RESULTS No significant differences were observed between true and lymphoid hyperplasia in terms of thymic length, thickness, diameters, morphologic features, and other qualitative features (p > 0.16). The length, thickness, and diameters of thymic hyperplasia were significantly larger than the mean values of normal glands in the corresponding age group (p < 0.001). CT attenuation of lymphoid hyperplasia was significantly higher than that of true hyperplasia among 15 patients with contrast-enhanced CT (median, 47.9 vs 31.4 HU; Wilcoxon p = 0.03). The receiver operating characteristic analysis yielded greater than 41.2 HU as the optimal threshold for differentiating lymphoid hyperplasia from true hyperplasia, with 83% sensitivity and 89% specificity. A decrease of signal intensity on opposed-phase images was present in all four cases with in- and opposed-phase imaging. The mean maximum standardized uptake value was 2.66. CONCLUSION CT attenuation of the thymus was significantly higher in lymphoid hyperplasia than in true hyperplasia, with an optimal threshold of greater than 41.2 HU in this cohort of patients with pathologically confirmed thymic hyperplasia. PMID:24555583

Fractional domain varying-order differential denoising method

NASA Astrophysics Data System (ADS)

Zhang, Yan-Shan; Zhang, Feng; Li, Bing-Zhao; Tao, Ran

2014-10-01

Removal of noise is an important step in the image restoration process, and it remains a challenging problem in image processing. Denoising is a process used to remove the noise from the corrupted image, while retaining the edges and other detailed features as much as possible. Recently, denoising in the fractional domain is a hot research topic. The fractional-order anisotropic diffusion method can bring a less blocky effect and preserve edges in image denoising, a method that has received much interest in the literature. Based on this method, we propose a new method for image denoising, in which fractional-varying-order differential, rather than constant-order differential, is used. The theoretical analysis and experimental results show that compared with the state-of-the-art fractional-order anisotropic diffusion method, the proposed fractional-varying-order differential denoising model can preserve structure and texture well, while quickly removing noise, and yields good visual effects and better peak signal-to-noise ratio.

Differentiation of arterioles from venules in mouse histology images using machine learning

NASA Astrophysics Data System (ADS)

Elkerton, J. S.; Xu, Yiwen; Pickering, J. G.; Ward, Aaron D.

2016-03-01

Analysis and morphological comparison of arteriolar and venular networks are essential to our understanding of multiple diseases affecting every organ system. We have developed and evaluated the first fully automatic software system for differentiation of arterioles from venules on high-resolution digital histology images of the mouse hind limb immunostained for smooth muscle α-actin. Classifiers trained on texture and morphologic features by supervised machine learning provided excellent classification accuracy for differentiation of arterioles and venules, achieving an area under the receiver operating characteristic curve of 0.90 and balanced false-positive and false-negative rates. Feature selection was consistent across cross-validation iterations, and a small set of three features was required to achieve the reported performance, suggesting potential generalizability of the system. This system eliminates the need for laborious manual classification of the hundreds of microvessels occurring in a typical sample, and paves the way for high-throughput analysis the arteriolar and venular networks in the mouse.

Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

PubMed

Yang, Zhangjing; Feng, Piaopiao; Wen, Tian; Wan, Minghua; Hong, Xunning

2017-01-01

Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Textural features of dynamic contrast-enhanced MRI derived model-free and model-based parameter maps in glioma grading.

PubMed

Xie, Tian; Chen, Xiao; Fang, Jingqin; Kang, Houyi; Xue, Wei; Tong, Haipeng; Cao, Peng; Wang, Sumei; Yang, Yizeng; Zhang, Weiguo

2018-04-01

Presurgical glioma grading by dynamic contrast-enhanced MRI (DCE-MRI) has unresolved issues. The aim of this study was to investigate the ability of textural features derived from pharmacokinetic model-based or model-free parameter maps of DCE-MRI in discriminating between different grades of gliomas, and their correlation with pathological index. Retrospective. Forty-two adults with brain gliomas. 3.0T, including conventional anatomic sequences and DCE-MRI sequences (variable flip angle T1-weighted imaging and three-dimensional gradient echo volumetric imaging). Regions of interest on the cross-sectional images with maximal tumor lesion. Five commonly used textural features, including Energy, Entropy, Inertia, Correlation, and Inverse Difference Moment (IDM), were generated. All textural features of model-free parameters (initial area under curve [IAUC], maximal signal intensity [Max SI], maximal up-slope [Max Slope]) could effectively differentiate between grade II (n = 15), grade III (n = 13), and grade IV (n = 14) gliomas (P < 0.05). Two textural features, Entropy and IDM, of four DCE-MRI parameters, including Max SI, Max Slope (model-free parameters), vp (Extended Tofts), and vp (Patlak) could differentiate grade III and IV gliomas (P < 0.01) in four measurements. Both Entropy and IDM of Patlak-based K trans and vp could differentiate grade II (n = 15) from III (n = 13) gliomas (P < 0.01) in four measurements. No textural features of any DCE-MRI parameter maps could discriminate between subtypes of grade II and III gliomas (P < 0.05). Both Entropy and IDM of Extended Tofts- and Patlak-based vp showed highest area under curve in discriminating between grade III and IV gliomas. However, intraclass correlation coefficient (ICC) of these features revealed relatively lower inter-observer agreement. No significant correlation was found between microvascular density and textural features, compared with a moderate correlation found between cellular proliferation index and those features. Textural features of DCE-MRI parameter maps displayed a good ability in glioma grading. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1099-1111. © 2017 International Society for Magnetic Resonance in Medicine.

The management of deep-seated, lowgrade lipomatous lesions.

PubMed

Al-Ani, Zeid; Fernando, Malee; Wilkinson, Victoria; Kotnis, Nikhil

2018-06-01

Deep-seated, low-grade lipomatous lesions detected on imaging often cause uncertainty for diagnosis and treatment. Confidently distinguishing lipomas from well-differentiated liposarcomas is often not possible on imaging. The approach to management of such lesions varies widely between institutions. Applying an evidenced-based approach set around published literature that clearly highlights how criteria such as lesion size, location, age and imaging features can be used to predict the risk of well-differentiated liposarcomas and subsequent de-differentiation would seem sensible. Our aim is to review the literature and produce a unified, evidence-based guideline that will be a useful tool for managing these lesions.

Ultrasound speckle reduction based on fractional order differentiation.

PubMed

Shao, Dangguo; Zhou, Ting; Liu, Fan; Yi, Sanli; Xiang, Yan; Ma, Lei; Xiong, Xin; He, Jianfeng

2017-07-01

Ultrasound images show a granular pattern of noise known as speckle that diminishes their quality and results in difficulties in diagnosis. To preserve edges and features, this paper proposes a fractional differentiation-based image operator to reduce speckle in ultrasound. An image de-noising model based on fractional partial differential equations with balance relation between k (gradient modulus threshold that controls the conduction) and v (the order of fractional differentiation) was constructed by the effective combination of fractional calculus theory and a partial differential equation, and the numerical algorithm of it was achieved using a fractional differential mask operator. The proposed algorithm has better speckle reduction and structure preservation than the three existing methods [P-M model, the speckle reducing anisotropic diffusion (SRAD) technique, and the detail preserving anisotropic diffusion (DPAD) technique]. And it is significantly faster than bilateral filtering (BF) in producing virtually the same experimental results. Ultrasound phantom testing and in vivo imaging show that the proposed method can improve the quality of an ultrasound image in terms of tissue SNR, CNR, and FOM values.

Performance comparison of classifiers for differentiation among obstructive lung diseases based on features of texture analysis at HRCT

NASA Astrophysics Data System (ADS)

Lee, Youngjoo; Seo, Joon Beom; Kang, Bokyoung; Kim, Dongil; Lee, June Goo; Kim, Song Soo; Kim, Namkug; Kang, Suk Ho

2007-03-01

The performance of classification algorithms for differentiating among obstructive lung diseases based on features from texture analysis using HRCT (High Resolution Computerized Tomography) images was compared. HRCT can provide accurate information for the detection of various obstructive lung diseases, including centrilobular emphysema, panlobular emphysema and bronchiolitis obliterans. Features on HRCT images can be subtle, however, particularly in the early stages of disease, and image-based diagnosis is subject to inter-observer variation. To automate the diagnosis and improve the accuracy, we compared three types of automated classification systems, naÃve Bayesian classifier, ANN (Artificial Neural Net) and SVM (Support Vector Machine), based on their ability to differentiate among normal lung and three types of obstructive lung diseases. To assess the performance and cross-validation of these three classifiers, 5 folding methods with 5 randomly chosen groups were used. For a more robust result, each validation was repeated 100 times. SVM showed the best performance, with 86.5% overall sensitivity, significantly different from the other classifiers (one way ANOVA, p<0.01). We address the characteristics of each classifier affecting performance and the issue of which classifier is the most suitable for clinical applications, and propose an appropriate method to choose the best classifier and determine its optimal parameters for optimal disease discrimination. These results can be applied to classifiers for differentiation of other diseases.

Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model

NASA Astrophysics Data System (ADS)

Ma, Ling; Lu, Guolan; Wang, Dongsheng; Wang, Xu; Chen, Zhuo Georgia; Muller, Susan; Chen, Amy; Fei, Baowei

2017-03-01

Hyperspectral imaging (HSI) is an emerging imaging modality that can provide a noninvasive tool for cancer detection and image-guided surgery. HSI acquires high-resolution images at hundreds of spectral bands, providing big data to differentiating different types of tissue. We proposed a deep learning based method for the detection of head and neck cancer with hyperspectral images. Since the deep learning algorithm can learn the feature hierarchically, the learned features are more discriminative and concise than the handcrafted features. In this study, we adopt convolutional neural networks (CNN) to learn the deep feature of pixels for classifying each pixel into tumor or normal tissue. We evaluated our proposed classification method on the dataset containing hyperspectral images from 12 tumor-bearing mice. Experimental results show that our method achieved an average accuracy of 91.36%. The preliminary study demonstrated that our deep learning method can be applied to hyperspectral images for detecting head and neck tumors in animal models.

Differentiation of pre-ablation and post-ablation late gadolinium-enhanced cardiac MRI scans of longstanding persistent atrial fibrillation patients

NASA Astrophysics Data System (ADS)

Yang, Guang; Zhuang, Xiahai; Khan, Habib; Haldar, Shouvik; Nyktari, Eva; Li, Lei; Ye, Xujiong; Slabaugh, Greg; Wong, Tom; Mohiaddin, Raad; Keegan, Jennifer; Firmin, David

2017-03-01

Late Gadolinium-Enhanced Cardiac MRI (LGE CMRI) is an emerging non-invasive technique to image and quantify preablation native and post-ablation atrial scarring. Previous studies have reported that enhanced image intensities of the atrial scarring in the LGE CMRI inversely correlate with the left atrial endocardial voltage invasively obtained by electro-anatomical mapping. However, the reported reproducibility of using LGE CMRI to identify and quantify atrial scarring is variable. This may be due to two reasons: first, delineation of the left atrium (LA) and pulmonary veins (PVs) anatomy generally relies on manual operation that is highly subjective, and this could substantially affect the subsequent atrial scarring segmentation; second, simple intensity based image features may not be good enough to detect subtle changes in atrial scarring. In this study, we hypothesized that texture analysis can provide reliable image features for the LGE CMRI images subject to accurate and objective delineation of the heart anatomy based on a fully-automated whole heart segmentation (WHS) method. We tested the extracted texture features to differentiate between pre-ablation and post-ablation LGE CMRI studies in longstanding persistent atrial fibrillation patients. These patients often have extensive native scarring and differentiation from post-ablation scarring can be difficult. Quantification results showed that our method is capable of solving this classification task, and we can envisage further deployment of this texture analysis based method for other clinical problems using LGE CMRI.

Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

PubMed

Lee, Hansang; Hong, Helen; Kim, Junmo; Jung, Dae Chul

2018-04-01

To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images. A dataset including 80 abdominal CT images of 39 AMLwvf and 41 ccRCC patients was used. We proposed a DFC method for differentiating the small renal masses (SRM) into AMLwvf and ccRCC using the combination of hand-crafted and deep features, and machine learning classifiers. First, 71-dimensional hand-crafted features (HCF) of texture and shape were extracted from the SRM contours. Second, 1000-4000-dimensional deep features (DF) were extracted from the ImageNet pretrained deep learning model with the SRM image patches. In DF extraction, we proposed the texture image patches (TIP) to emphasize the texture information inside the mass in DFs and reduce the mass size variability. Finally, the two features were concatenated and the random forest (RF) classifier was trained on these concatenated features to classify the types of SRMs. The proposed method was tested on our dataset using leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under receiver operating characteristics curve (AUC). In experiments, the combinations of four deep learning models, AlexNet, VGGNet, GoogleNet, and ResNet, and four input image patches, including original, masked, mass-size, and texture image patches, were compared and analyzed. In qualitative evaluation, we observed the change in feature distributions between the proposed and comparative methods using tSNE method. In quantitative evaluation, we evaluated and compared the classification results, and observed that (a) the proposed HCF + DF outperformed HCF-only and DF-only, (b) AlexNet showed generally the best performances among the CNN models, and (c) the proposed TIPs not only achieved the competitive performances among the input patches, but also steady performance regardless of CNN models. As a result, the proposed method achieved the accuracy of 76.6 ± 1.4% for the proposed HCF + DF with AlexNet and TIPs, which improved the accuracy by 6.6%p and 8.3%p compared to HCF-only and DF-only, respectively. The proposed shape features and TIPs improved the HCFs and DFs, respectively, and the feature concatenation further enhanced the quality of features for differentiating AMLwvf from ccRCC in abdominal CE CT images. © 2018 American Association of Physicists in Medicine.

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

PubMed

Kunimatsu, Akira; Kunimatsu, Natsuko; Yasaka, Koichiro; Akai, Hiroyuki; Kamiya, Kouhei; Watadani, Takeyuki; Mori, Harushi; Abe, Osamu

2018-05-16

Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classification by support vector machine, a method of traditional machine learning, using texture features computed from contrast-enhanced T 1 -weighted images. This retrospective study on preoperative brain tumor MRI included 76 consecutives, initially treated patients with glioblastoma (n = 55) or PCNSL (n = 21) from one institution, consisting of independent training group (n = 60: 44 glioblastomas and 16 PCNSLs) and test group (n = 16: 11 glioblastomas and 5 PCNSLs) sequentially separated by time periods. A total set of 67 texture features was computed on routine contrast-enhanced T 1 -weighted images of the training group, and the top four most discriminating features were selected as input variables to train support vector machine classifiers. These features were then evaluated on the test group with subsequent image classification. The area under the receiver operating characteristic curves on the training data was calculated at 0.99 (95% confidence interval [CI]: 0.96-1.00) for the classifier with a Gaussian kernel and 0.87 (95% CI: 0.77-0.95) for the classifier with a linear kernel. On the test data, both of the classifiers showed prediction accuracy of 75% (12/16) of the test images. Although further improvement is needed, our preliminary results suggest that machine learning-based image classification may provide complementary diagnostic information on routine brain MRI.

Differential diagnosis of breast cancer using quantitative, label-free and molecular vibrational imaging

PubMed Central

Yang, Yaliang; Li, Fuhai; Gao, Liang; Wang, Zhiyong; Thrall, Michael J.; Shen, Steven S.; Wong, Kelvin K.; Wong, Stephen T. C.

2011-01-01

We present a label-free, chemically-selective, quantitative imaging strategy to identify breast cancer and differentiate its subtypes using coherent anti-Stokes Raman scattering (CARS) microscopy. Human normal breast tissue, benign proliferative, as well as in situ and invasive carcinomas, were imaged ex vivo. Simply by visualizing cellular and tissue features appearing on CARS images, cancerous lesions can be readily separated from normal tissue and benign proliferative lesion. To further distinguish cancer subtypes, quantitative disease-related features, describing the geometry and distribution of cancer cell nuclei, were extracted and applied to a computerized classification system. The results show that in situ carcinoma was successfully distinguished from invasive carcinoma, while invasive ductal carcinoma (IDC) and invasive lobular carcinoma were also distinguished from each other. Furthermore, 80% of intermediate-grade IDC and 85% of high-grade IDC were correctly distinguished from each other. The proposed quantitative CARS imaging method has the potential to enable rapid diagnosis of breast cancer. PMID:21833355

SU-D-207B-04: Morphological Features of MRI as a Correlate of Capsular Contracture in Breast Cancer Patients with Implant-Based Reconstructions

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

Tyagi, N; Sutton, E; Hunt, M

Purpose: Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. The goal of this study was to identify image-based correlates of CC using MRI imaging in breast cancer patients who received both MRI and clinical evaluation following reconstructive surgery. Methods: We analyzed a retrospective dataset of 50 patients who had both a diagnostic MR and a plastic surgeon’s evaluations of CC score (Baker’s score) within a six month period following mastectomy and reconstructive surgery. T2w sagittal MRIs (TR/TE = 3500/102 ms, slice thickness = 4 mm) were used for morphological shape features (roundness, eccentricity,more » solidity, extent and ratio-length) and histogram features (median, skewness and kurtosis) of the implant and the pectoralis muscle overlying the implant. Implant and pectoralis muscles were segmented in 3D using Computation Environment for Radiological Research (CERR) and shape and histogram features were calculated as a function of Baker’s score. Results: Shape features such as roundness and eccentricity were statistically significant in differentiating grade 1 and grade 2 (p = 0.009; p = 0.06) as well as grade 1 and grade 3 CC (p = 0.001; p = 0.006). Solidity and extent were statistically significant in differentiating grade 1 and grade 3 CC (p = 0.04; p = 0.04). Ratio-length was statistically significant in differentiating all grades of CC except grade 2 and grade 3 that showed borderline significance (p = 0.06). The muscle thickness, median intensity and kurtosis were significant in differentiating between grade 1 and grade 3 (p = 0.02), grade 1 and grade 2 (p = 0.03) and grade 1 and grade 3 (p = 0.01) respectively. Conclusion: Morphological shape features described on MR images were associated with the severity of CC. MRI may be important in objectively evaluating outcomes in breast cancer patients who undergo implant reconstruction.« less

Method for evaluation of human induced pluripotent stem cell quality using image analysis based on the biological morphology of cells.

PubMed

Wakui, Takashi; Matsumoto, Tsuyoshi; Matsubara, Kenta; Kawasaki, Tomoyuki; Yamaguchi, Hiroshi; Akutsu, Hidenori

2017-10-01

We propose an image analysis method for quality evaluation of human pluripotent stem cells based on biologically interpretable features. It is important to maintain the undifferentiated state of induced pluripotent stem cells (iPSCs) while culturing the cells during propagation. Cell culture experts visually select good quality cells exhibiting the morphological features characteristic of undifferentiated cells. Experts have empirically determined that these features comprise prominent and abundant nucleoli, less intercellular spacing, and fewer differentiating cellular nuclei. We quantified these features based on experts' visual inspection of phase contrast images of iPSCs and found that these features are effective for evaluating iPSC quality. We then developed an iPSC quality evaluation method using an image analysis technique. The method allowed accurate classification, equivalent to visual inspection by experts, of three iPSC cell lines.

Duodenal adenocarcinoma presenting as a mass with aneurismal dilatation.

PubMed

Mama, Nadia; Ben Slama, Aïda; Arifa, Nadia; Kadri, Khaled; Sriha, Badreddine; Ksiaa, Mehdi; Jemni, Hela; Tlili-Graiess, Kalthoum

2014-01-01

Duodenal adenocarcinoma is frequent. Aneurysmal dilatation of the small bowel is reported to be a lymphoma characteristic imaging finding. A 57-year-old male was found to have a duodenal adenocarcinoma with aneurismal dilatation on imaging which is an exceptional feature. On laparotomy, the wall thickening of the dilated duodenum extended to the first jejunal loop, with multiple mesenteric lymph nodes and ascites. Segmental palliative resection with gastro-entero-anastomosis was done. Histopathology revealed a moderately differentiated adenocarcinoma with neuro-endocrine differentiation foci. Wide areas of necrosis and vascular emboli were responsible for the radiological feature of the dilated duodenum with wall thickening. Copyright © 2014 Elsevier Inc. All rights reserved.

Intraosseous mucoepidermoid carcinoma: a review of the diagnostic imaging features of four jaw cases.

PubMed

Chan, K C; Pharoah, M; Lee, L; Weinreb, I; Perez-Ordonez, B

2013-01-01

The purpose of this case series is to present the common features of intraosseous mucoepidermoid carcinoma (IMC) of the jaws in plain film and CT imaging. Two oral and maxillofacial radiologists reviewed and characterized the common features of four biopsy-proven cases of IMC in the jaws in plain film and CT imaging obtained from the files of the Department of Oral Radiology, Faculty of Dentistry, University of Toronto, Toronto, Canada. The common features are a well-defined sclerotic periphery, the presence of internal amorphous sclerotic bone and numerous small loculations, lack of septae bordering many of the loculations, and expansion and perforation of the outer cortical plate with extension into surrounding soft tissue. Other characteristics include tooth displacement and root resorption. The four cases of IMC reviewed have common imaging characteristics. All cases share some diagnostic imaging features with other multilocular-appearing entities of the jaws. However, the presence of amorphous sclerotic bone and malignant characteristics can be useful in the differential diagnosis.

A demonstration test of the dual-beam polarimetry differential imaging system for the high-contrast observation

NASA Astrophysics Data System (ADS)

Dou, Jiangpei; Ren, Deqing; Zhu, Yongtian; Wang, Xue; Zhang, Xi; Li, Rong

2012-09-01

We propose a dual-beam polarimetry differential imaging test system that can be used for the direct imaging of the exoplanets. The system is composed of a liquid crystal variable retarder (LCVR) in the pupil to switch between two orthogonal polarized states, and a Wollaston prism (WP) that will be inserted before the final focal focus of the system to create two polarized images for the differential subtraction. Such a system can work separately or be integrated in the coronagraph system to enhance the high-contrast imaging. To demonstrate the feasibility of the proposed system, here we show the initial test result both with and without integrating our developed coronagraph. A unique feature for this system is that each channel can subtract with itself by using the retarder to rotate the planet's polarization orientation, which has the best performance according to our lab test results. Finally, it is shown that the polarimetry differential imaging system is a promising technique and can be used for the direct imaging observation of reflected lights from the exoplanets.

In vivo multimodal nonlinear optical imaging of mucosal tissue

NASA Astrophysics Data System (ADS)

Sun, Ju; Shilagard, Tuya; Bell, Brent; Motamedi, Massoud; Vargas, Gracie

2004-05-01

We present a multimodal nonlinear imaging approach to elucidate microstructures and spectroscopic features of oral mucosa and submucosa in vivo. The hamster buccal pouch was imaged using 3-D high resolution multiphoton and second harmonic generation microscopy. The multimodal imaging approach enables colocalization and differentiation of prominent known spectroscopic and structural features such as keratin, epithelial cells, and submucosal collagen at various depths in tissue. Visualization of cellular morphology and epithelial thickness are in excellent agreement with histological observations. These results suggest that multimodal nonlinear optical microscopy can be an effective tool for studying the physiology and pathology of mucosal tissue.

Stellar Differential Rotation of F-Stars Using DI and ZDI: The Case of HR1817

NASA Astrophysics Data System (ADS)

Marsden, Stephen

2018-04-01

The measure of surface differential rotation via the motion of spots and/or magnetic features on the stellar surface is a critical part of understanding the stellar dynamo. Here we present several epochs of (Zeeman) Doppler imaging of the young late-F star HR1817 from 2001 until 2011. These results show that HR1817 exhibits a high shear of its surface features, significantly above the solar value. It would appear that F stars, with thin convective zones, have surface differential rotation rates much higher than that of low mass stars.

Ordinal measures for iris recognition.

PubMed

Sun, Zhenan; Tan, Tieniu

2009-12-01

Images of a human iris contain rich texture information useful for identity authentication. A key and still open issue in iris recognition is how best to represent such textural information using a compact set of features (iris features). In this paper, we propose using ordinal measures for iris feature representation with the objective of characterizing qualitative relationships between iris regions rather than precise measurements of iris image structures. Such a representation may lose some image-specific information, but it achieves a good trade-off between distinctiveness and robustness. We show that ordinal measures are intrinsic features of iris patterns and largely invariant to illumination changes. Moreover, compactness and low computational complexity of ordinal measures enable highly efficient iris recognition. Ordinal measures are a general concept useful for image analysis and many variants can be derived for ordinal feature extraction. In this paper, we develop multilobe differential filters to compute ordinal measures with flexible intralobe and interlobe parameters such as location, scale, orientation, and distance. Experimental results on three public iris image databases demonstrate the effectiveness of the proposed ordinal feature models.

Differentiation of Solid Renal Tumors with Multiparametric MR Imaging.

PubMed

Lopes Vendrami, Camila; Parada Villavicencio, Carolina; DeJulio, Todd J; Chatterjee, Argha; Casalino, David D; Horowitz, Jeanne M; Oberlin, Daniel T; Yang, Guang-Yu; Nikolaidis, Paul; Miller, Frank H

2017-01-01

Characterization of renal tumors is critical to determine the best therapeutic approach and improve overall patient survival. Because of increased use of high-resolution cross-sectional imaging in clinical practice, renal masses are being discovered with increased frequency. As a result, accurate imaging characterization of these lesions is more important than ever. However, because of the wide array of imaging features encountered as well as overlapping characteristics, identifying reliable imaging criteria for differentiating malignant from benign renal masses remains a challenge. Multiparametric magnetic resonance (MR) imaging based on various anatomic and functional parameters has an important role and adds diagnostic value in detection and characterization of renal masses. MR imaging may allow distinction of benign solid renal masses from several renal cell carcinoma (RCC) subtypes, potentially suggest the histologic grade of a neoplasm, and play an important role in ensuring appropriate patient management to avoid unnecessary surgery or other interventions. It is also a useful noninvasive imaging tool for patients who undergo active surveillance of renal masses and for follow-up after treatment of a renal mass. The purpose of this article is to review the characteristic MR imaging features of RCC and common benign renal masses and propose a diagnostic imaging approach to evaluation of solid renal masses using multiparametric MR imaging. © RSNA, 2017.

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.

Diagnostic imaging of solitary tumors of the spine: what to do and say.

PubMed

Rodallec, Mathieu H; Feydy, Antoine; Larousserie, Frédérique; Anract, Philippe; Campagna, Raphaël; Babinet, Antoine; Zins, Marc; Drapé, Jean-Luc

2008-01-01

Metastatic disease, myeloma, and lymphoma are the most common malignant spinal tumors. Hemangioma is the most common benign tumor of the spine. Other primary osseous lesions of the spine are more unusual but may exhibit characteristic imaging features that can help the radiologist develop a differential diagnosis. Radiologic evaluation of a patient who presents with osseous vertebral lesions often includes radiography, computed tomography (CT), and magnetic resonance (MR) imaging. Because of the complex anatomy of the vertebrae, CT is more useful than conventional radiography for evaluating lesion location and analyzing bone destruction and condensation. The diagnosis of spinal tumors is based on patient age, topographic features of the tumor, and lesion pattern as seen at CT and MR imaging. A systematic approach is useful for recognizing tumors of the spine with characteristic features such as bone island, osteoid osteoma, osteochondroma, chondrosarcoma, vertebral angioma, and aneurysmal bone cyst. In the remaining cases, the differential diagnosis may include other primary spinal tumors, vertebral metastases and major nontumoral lesions simulating a vertebral tumor, Paget disease, spondylitis, echinococcal infection, and aseptic osteitis. In many cases, vertebral biopsy is warranted to guide treatment.

Classification of pulmonary nodules in lung CT images using shape and texture features

NASA Astrophysics Data System (ADS)

Dhara, Ashis Kumar; Mukhopadhyay, Sudipta; Dutta, Anirvan; Garg, Mandeep; Khandelwal, Niranjan; Kumar, Prafulla

2016-03-01

Differentiation of malignant and benign pulmonary nodules is important for prognosis of lung cancer. In this paper, benign and malignant nodules are classified using support vector machine. Several shape-based and texture-based features are used to represent the pulmonary nodules in the feature space. A semi-automated technique is used for nodule segmentation. Relevant features are selected for efficient representation of nodules in the feature space. The proposed scheme and the competing technique are evaluated on a data set of 542 nodules of Lung Image Database Consortium and Image Database Resource Initiative. The nodules with composite rank of malignancy "1","2" are considered as benign and "4","5" are considered as malignant. Area under the receiver operating characteristics curve is 0:9465 for the proposed method. The proposed method outperforms the competing technique.

Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences.

PubMed

Qin, Jiang-Bo; Liu, Zhenyu; Zhang, Hui; Shen, Chen; Wang, Xiao-Chun; Tan, Yan; Wang, Shuo; Wu, Xiao-Feng; Tian, Jie

2017-05-07

BACKGROUND Gliomas are the most common primary brain neoplasms. Misdiagnosis occurs in glioma grading due to an overlap in conventional MRI manifestations. The aim of the present study was to evaluate the power of radiomic features based on multiple MRI sequences - T2-Weighted-Imaging-FLAIR (FLAIR), T1-Weighted-Imaging-Contrast-Enhanced (T1-CE), and Apparent Diffusion Coefficient (ADC) map - in glioma grading, and to improve the power of glioma grading by combining features. MATERIAL AND METHODS Sixty-six patients with histopathologically proven gliomas underwent T2-FLAIR and T1WI-CE sequence scanning with some patients (n=63) also undergoing DWI scanning. A total of 114 radiomic features were derived with radiomic methods by using in-house software. All radiomic features were compared between high-grade gliomas (HGGs) and low-grade gliomas (LGGs). Features with significant statistical differences were selected for receiver operating characteristic (ROC) curve analysis. The relationships between significantly different radiomic features and glial fibrillary acidic protein (GFAP) expression were evaluated. RESULTS A total of 8 radiomic features from 3 MRI sequences displayed significant differences between LGGs and HGGs. FLAIR GLCM Cluster Shade, T1-CE GLCM Entropy, and ADC GLCM Homogeneity were the best features to use in differentiating LGGs and HGGs in each MRI sequence. The combined feature was best able to differentiate LGGs and HGGs, which improved the accuracy of glioma grading compared to the above features in each MRI sequence. A significant correlation was found between GFAP and T1-CE GLCM Entropy, as well as between GFAP and ADC GLCM Homogeneity. CONCLUSIONS The combined radiomic feature had the highest efficacy in distinguishing LGGs from HGGs.

Soft-Tissue Sarcomas of the Abdomen and Pelvis: Radiologic-Pathologic Features, Part 2-Uncommon Sarcomas.

PubMed

Levy, Angela D; Manning, Maria A; Miettinen, Markku M

2017-01-01

Soft-tissue sarcomas occurring in the abdomen and pelvis are an uncommon but important group of malignancies. Recent changes to the World Health Organization classification of soft-tissue tumors include the movement of gastrointestinal stromal tumors (GISTs) into the soft-tissue tumor classification. GIST is the most common intraperitoneal sarcoma. Liposarcoma is the most common retroperitoneal sarcoma, and leiomyosarcoma is the second most common. GIST, liposarcoma, and leiomyosarcoma account for the majority of sarcomas encountered in the abdomen and pelvis and are discussed in part 1 of this article. Undifferentiated pleomorphic sarcoma (previously called malignant fibrous histiocytoma), dermatofibrosarcoma protuberans, solitary fibrous tumor, malignant peripheral nerve sheath tumor, rhabdomyosarcoma, extraskeletal chondro-osseous sarcomas, vascular sarcomas, and sarcomas of uncertain differentiation uncommonly arise in the abdomen and pelvis and the abdominal wall. Although these lesions are rare sarcomas and their imaging features overlap, familiarity with the locations where they occur and their imaging features is important so they can be diagnosed accurately. The anatomic location and clinical history are important factors in the differential diagnosis of these lesions because metastasis, more-common sarcomas, borderline fibroblastic proliferations (such as desmoid tumors), and endometriosis have imaging findings that overlap with those of these uncommon sarcomas. In this article, the clinical, pathologic, and imaging findings of uncommon soft-tissue sarcomas of the abdomen and pelvis and the abdominal wall are reviewed, with an emphasis on their differential diagnosis.

Soft-Tissue Sarcomas of the Abdomen and Pelvis: Radiologic-Pathologic Features, Part 2—Uncommon Sarcomas

PubMed Central

Manning, Maria A.; Miettinen, Markku M.

2017-01-01

Soft-tissue sarcomas occurring in the abdomen and pelvis are an uncommon but important group of malignancies. Recent changes to the World Health Organization classification of soft-tissue tumors include the movement of gastrointestinal stromal tumors (GISTs) into the soft-tissue tumor classification. GIST is the most common intraperitoneal sarcoma. Liposarcoma is the most common retroperitoneal sarcoma, and leiomyosarcoma is the second most common. GIST, liposarcoma, and leiomyosarcoma account for the majority of sarcomas encountered in the abdomen and pelvis and are discussed in part 1 of this article. Undifferentiated pleomorphic sarcoma (previously called malignant fibrous histiocytoma), dermatofibrosarcoma protuberans, solitary fibrous tumor, malignant peripheral nerve sheath tumor, rhabdomyosarcoma, extraskeletal chondro-osseous sarcomas, vascular sarcomas, and sarcomas of uncertain differentiation uncommonly arise in the abdomen and pelvis and the abdominal wall. Although these lesions are rare sarcomas and their imaging features overlap, familiarity with the locations where they occur and their imaging features is important so they can be diagnosed accurately. The anatomic location and clinical history are important factors in the differential diagnosis of these lesions because metastasis, more-common sarcomas, borderline fibroblastic proliferations (such as desmoid tumors), and endometriosis have imaging findings that overlap with those of these uncommon sarcomas. In this article, the clinical, pathologic, and imaging findings of uncommon soft-tissue sarcomas of the abdomen and pelvis and the abdominal wall are reviewed, with an emphasis on their differential diagnosis. PMID:28493803

Combining Landform Thematic Layer and Object-Oriented Image Analysis to Map the Surface Features of Mountainous Flood Plain Areas

NASA Astrophysics Data System (ADS)

Chuang, H.-K.; Lin, M.-L.; Huang, W.-C.

2012-04-01

The Typhoon Morakot on August 2009 brought more than 2,000 mm of cumulative rainfall in southern Taiwan, the extreme rainfall event caused serious damage to the Kaoping River basin. The losses were mostly blamed on the landslides along sides of the river, and shifting of the watercourse even led to the failure of roads and bridges, as well as flooding and levees damage happened around the villages on flood bank and terraces. Alluvial fans resulted from debris flow of stream feeders blocked the main watercourse and debris dam was even formed and collapsed. These disasters have highlighted the importance of identification and map the watercourse alteration, surface features of flood plain area and artificial structures soon after the catastrophic typhoon event for natural hazard mitigation. Interpretation of remote sensing images is an efficient approach to acquire spatial information for vast areas, therefore making it suitable for the differentiation of terrain and objects near the vast flood plain areas in a short term. The object-oriented image analysis program (Definiens Developer 7.0) and multi-band high resolution satellite images (QuickBird, DigitalGlobe) was utilized to interpret the flood plain features from Liouguei to Baolai of the the Kaoping River basin after Typhoon Morakot. Object-oriented image interpretation is the process of using homogenized image blocks as elements instead of pixels for different shapes, textures and the mutual relationships of adjacent elements, as well as categorized conditions and rules for semi-artificial interpretation of surface features. Digital terrain models (DTM) are also employed along with the above process to produce layers with specific "landform thematic layers". These layers are especially helpful in differentiating some confusing categories in the spectrum analysis with improved accuracy, such as landslides and riverbeds, as well as terraces, riverbanks, which are of significant engineering importance in disaster mitigation. In this study, an automatic and fast image interpretation process for eight surface features including main channel, secondary channel, sandbar, flood plain, river terrace, alluvial fan, landslide, and the nearby artificial structures in the mountainous flood plain is proposed. Images along timelines can even be compared in order to differentiate historical events such as village inundations, failure of roads, bridges and levees, as well as alternation of watercourse, and therefore can be used as references for safety evaluation of engineering structures near rivers, disaster prevention and mitigation, and even future land-use planning. Keywords: Flood plain area, Remote sensing, Object-oriented, Surface feature interpretation, Terrain analysis, Thematic layer, Typhoon Morakot

Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images.

PubMed

Badawi, A M; Derbala, A S; Youssef, A M

1999-08-01

Computerized ultrasound tissue characterization has become an objective means for diagnosis of liver diseases. It is difficult to differentiate diffuse liver diseases, namely cirrhotic and fatty liver by visual inspection from the ultrasound images. The visual criteria for differentiating diffused diseases are rather confusing and highly dependent upon the sonographer's experience. This often causes a bias effects in the diagnostic procedure and limits its objectivity and reproducibility. Computerized tissue characterization to assist quantitatively the sonographer for the accurate differentiation and to minimize the degree of risk is thus justified. Fuzzy logic has emerged as one of the most active area in classification. In this paper, we present an approach that employs Fuzzy reasoning techniques to automatically differentiate diffuse liver diseases using numerical quantitative features measured from the ultrasound images. Fuzzy rules were generated from over 140 cases consisting of normal, fatty, and cirrhotic livers. The input to the fuzzy system is an eight dimensional vector of feature values: the mean gray level (MGL), the percentile 10%, the contrast (CON), the angular second moment (ASM), the entropy (ENT), the correlation (COR), the attenuation (ATTEN) and the speckle separation. The output of the fuzzy system is one of the three categories: cirrhosis, fatty or normal. The steps done for differentiating the pathologies are data acquisition and feature extraction, dividing the input spaces of the measured quantitative data into fuzzy sets. Based on the expert knowledge, the fuzzy rules are generated and applied using the fuzzy inference procedures to determine the pathology. Different membership functions are developed for the input spaces. This approach has resulted in very good sensitivities and specificity for classifying diffused liver pathologies. This classification technique can be used in the diagnostic process, together with the history information, laboratory, clinical and pathological examinations.

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.

Classification of Normal and Apoptotic Cells from Fluorescence Microscopy Images Using Generalized Polynomial Chaos and Level Set Function.

PubMed

Du, Yuncheng; Budman, Hector M; Duever, Thomas A

2016-06-01

Accurate automated quantitative analysis of living cells based on fluorescence microscopy images can be very useful for fast evaluation of experimental outcomes and cell culture protocols. In this work, an algorithm is developed for fast differentiation of normal and apoptotic viable Chinese hamster ovary (CHO) cells. For effective segmentation of cell images, a stochastic segmentation algorithm is developed by combining a generalized polynomial chaos expansion with a level set function-based segmentation algorithm. This approach provides a probabilistic description of the segmented cellular regions along the boundary, from which it is possible to calculate morphological changes related to apoptosis, i.e., the curvature and length of a cell's boundary. These features are then used as inputs to a support vector machine (SVM) classifier that is trained to distinguish between normal and apoptotic viable states of CHO cell images. The use of morphological features obtained from the stochastic level set segmentation of cell images in combination with the trained SVM classifier is more efficient in terms of differentiation accuracy as compared with the original deterministic level set method.

A new ultrasonic real-time scanner featuring a servo-controlled transducer displaying a sector image.

PubMed

Skolnick, M L; Matzuk, T

1978-08-01

This paper describes a new real-time servo-controlled sector scanner that produces high-resolution images similar to phased-array systems, but possesses the simplicity of design and low cost best achievable in a mechanical sector scanner. Its unique feature is the transducer head which contains a single moving part--the transducer. Frame rates vary from 0 to 30 degrees and the sector angle from 0 to 60 degrees. Abdominal applications include: differentiation of vascular structures, detection of small masses, imaging of diagonally oriented organs. Survey scanning, and demonstration of regions difficult to image with contact scanners. Cardiac uses are also described.

Recognizing human activities using appearance metric feature and kinematics feature

NASA Astrophysics Data System (ADS)

Qian, Huimin; Zhou, Jun; Lu, Xinbiao; Wu, Xinye

2017-05-01

The problem of automatically recognizing human activities from videos through the fusion of the two most important cues, appearance metric feature and kinematics feature, is considered. And a system of two-dimensional (2-D) Poisson equations is introduced to extract the more discriminative appearance metric feature. Specifically, the moving human blobs are first detected out from the video by background subtraction technique to form a binary image sequence, from which the appearance feature designated as the motion accumulation image and the kinematics feature termed as centroid instantaneous velocity are extracted. Second, 2-D discrete Poisson equations are employed to reinterpret the motion accumulation image to produce a more differentiated Poisson silhouette image, from which the appearance feature vector is created through the dimension reduction technique called bidirectional 2-D principal component analysis, considering the balance between classification accuracy and time consumption. Finally, a cascaded classifier based on the nearest neighbor classifier and two directed acyclic graph support vector machine classifiers, integrated with the fusion of the appearance feature vector and centroid instantaneous velocity vector, is applied to recognize the human activities. Experimental results on the open databases and a homemade one confirm the recognition performance of the proposed algorithm.

Attitudes to Normalisation and Inclusive Education

ERIC Educational Resources Information Center

Sanagi, Tomomi

2016-01-01

The purpose of this paper was to clarify the features of teachers' image on normalisation and inclusive education. The participants of the study were both mainstream teachers and special teachers. One hundred and thirty-eight questionnaires were analysed. (1) Teachers completed the questionnaire of SD (semantic differential) images on…

Algorithms for differentiating between images of heterogeneous tissue across fluorescence microscopes.

PubMed

Chitalia, Rhea; Mueller, Jenna; Fu, Henry L; Whitley, Melodi Javid; Kirsch, David G; Brown, J Quincy; Willett, Rebecca; Ramanujam, Nimmi

2016-09-01

Fluorescence microscopy can be used to acquire real-time images of tissue morphology and with appropriate algorithms can rapidly quantify features associated with disease. The objective of this study was to assess the ability of various segmentation algorithms to isolate fluorescent positive features (FPFs) in heterogeneous images and identify an approach that can be used across multiple fluorescence microscopes with minimal tuning between systems. Specifically, we show a variety of image segmentation algorithms applied to images of stained tumor and muscle tissue acquired with 3 different fluorescence microscopes. Results indicate that a technique called maximally stable extremal regions followed by thresholding (MSER + Binary) yielded the greatest contrast in FPF density between tumor and muscle images across multiple microscopy systems.

Automatic classification of patients with idiopathic Parkinson's disease and progressive supranuclear palsy using diffusion MRI datasets

NASA Astrophysics Data System (ADS)

Talai, Sahand; Boelmans, Kai; Sedlacik, Jan; Forkert, Nils D.

2017-03-01

Parkinsonian syndromes encompass a spectrum of neurodegenerative diseases, which can be classified into various subtypes. The differentiation of these subtypes is typically conducted based on clinical criteria. Due to the overlap of intra-syndrome symptoms, the accurate differential diagnosis based on clinical guidelines remains a challenge with failure rates up to 25%. The aim of this study is to present an image-based classification method of patients with Parkinson's disease (PD) and patients with progressive supranuclear palsy (PSP), an atypical variant of PD. Therefore, apparent diffusion coefficient (ADC) parameter maps were calculated based on diffusion-tensor magnetic resonance imaging (MRI) datasets. Mean ADC values were determined in 82 brain regions using an atlas-based approach. The extracted mean ADC values for each patient were then used as features for classification using a linear kernel support vector machine classifier. To increase the classification accuracy, a feature selection was performed, which resulted in the top 17 attributes to be used as the final input features. A leave-one-out cross validation based on 56 PD and 21 PSP subjects revealed that the proposed method is capable of differentiating PD and PSP patients with an accuracy of 94.8%. In conclusion, the classification of PD and PSP patients based on ADC features obtained from diffusion MRI datasets is a promising new approach for the differentiation of Parkinsonian syndromes in the broader context of decision support systems.

Crack image segmentation based on improved DBC method

NASA Astrophysics Data System (ADS)

Cao, Ting; Yang, Nan; Wang, Fengping; Gao, Ting; Wang, Weixing

2017-11-01

With the development of computer vision technology, crack detection based on digital image segmentation method arouses global attentions among researchers and transportation ministries. Since the crack always exhibits the random shape and complex texture, it is still a challenge to accomplish reliable crack detection results. Therefore, a novel crack image segmentation method based on fractal DBC (differential box counting) is introduced in this paper. The proposed method can estimate every pixel fractal feature based on neighborhood information which can consider the contribution from all possible direction in the related block. The block moves just one pixel every time so that it could cover all the pixels in the crack image. Unlike the classic DBC method which only describes fractal feature for the related region, this novel method can effectively achieve crack image segmentation according to the fractal feature of every pixel. The experiment proves the proposed method can achieve satisfactory results in crack detection.

Optical coherence tomography: A guide to interpretation of common macular diseases

PubMed Central

Bhende, Muna; Shetty, Sharan; Parthasarathy, Mohana Kuppuswamy; Ramya, S

2018-01-01

Optical coherence tomography is a quick, non invasive and reproducible imaging tool for macular lesions and has become an essential part of retina practice. This review address the common protocols for imaging the macula, basics of image interpretation, features of common macular disorders with clues to differentiate mimickers and an introduction to choroidal imaging. It includes case examples and also a practical algorithm for interpretation. PMID:29283118

Differential phase acoustic microscope for micro-NDE

NASA Technical Reports Server (NTRS)

Waters, David D.; Pusateri, T. L.; Huang, S. R.

1992-01-01

A differential phase scanning acoustic microscope (DP-SAM) was developed, fabricated, and tested in this project. This includes the acoustic lens and transducers, driving and receiving electronics, scanning stage, scanning software, and display software. This DP-SAM can produce mechanically raster-scanned acoustic microscopic images of differential phase, differential amplitude, or amplitude of the time gated returned echoes of the samples. The differential phase and differential amplitude images provide better image contrast over the conventional amplitude images. A specially designed miniature dual beam lens was used to form two foci to obtain the differential phase and amplitude information of the echoes. High image resolution (1 micron) was achieved by applying high frequency (around 1 GHz) acoustic signals to the samples and placing two foci close to each other (1 micron). Tone burst was used in this system to obtain a good estimation of the phase differences between echoes from the two adjacent foci. The system can also be used to extract the V(z) acoustic signature. Since two acoustic beams and four receiving modes are available, there are 12 possible combinations to produce an image or a V(z) scan. This provides a unique feature of this system that none of the existing acoustic microscopic systems can provide for the micro-nondestructive evaluation applications. The entire system, including the lens, electronics, and scanning control software, has made a competitive industrial product for nondestructive material inspection and evaluation and has attracted interest from existing acoustic microscope manufacturers.

Parietal and frontal object areas underlie perception of object orientation in depth.

PubMed

Niimi, Ryosuke; Saneyoshi, Ayako; Abe, Reiko; Kaminaga, Tatsuro; Yokosawa, Kazuhiko

2011-05-27

Recent studies have shown that the human parietal and frontal cortices are involved in object image perception. We hypothesized that the parietal/frontal object areas play a role in differentiating the orientations (i.e., views) of an object. By using functional magnetic resonance imaging, we compared brain activations while human observers differentiated between two object images in depth-orientation (orientation task) and activations while they differentiated the images in object identity (identity task). The left intraparietal area, right angular gyrus, and right inferior frontal areas were activated more for the orientation task than for the identity task. The occipitotemporal object areas, however, were activated equally for the two tasks. No region showed greater activation for the identity task. These results suggested that the parietal/frontal object areas encode view-dependent visual features and underlie object orientation perception. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Quantitative radiomic profiling of glioblastoma represents transcriptomic expression.

PubMed

Kong, Doo-Sik; Kim, Junhyung; Ryu, Gyuha; You, Hye-Jin; Sung, Joon Kyung; Han, Yong Hee; Shin, Hye-Mi; Lee, In-Hee; Kim, Sung-Tae; Park, Chul-Kee; Choi, Seung Hong; Choi, Jeong Won; Seol, Ho Jun; Lee, Jung-Il; Nam, Do-Hyun

2018-01-19

Quantitative imaging biomarkers have increasingly emerged in the field of research utilizing available imaging modalities. We aimed to identify good surrogate radiomic features that can represent genetic changes of tumors, thereby establishing noninvasive means for predicting treatment outcome. From May 2012 to June 2014, we retrospectively identified 65 patients with treatment-naïve glioblastoma with available clinical information from the Samsung Medical Center data registry. Preoperative MR imaging data were obtained for all 65 patients with primary glioblastoma. A total of 82 imaging features including first-order statistics, volume, and size features, were semi-automatically extracted from structural and physiologic images such as apparent diffusion coefficient and perfusion images. Using commercially available software, NordicICE, we performed quantitative imaging analysis and collected the dataset composed of radiophenotypic parameters. Unsupervised clustering methods revealed that the radiophenotypic dataset was composed of three clusters. Each cluster represented a distinct molecular classification of glioblastoma; classical type, proneural and neural types, and mesenchymal type. These clusters also reflected differential clinical outcomes. We found that extracted imaging signatures does not represent copy number variation and somatic mutation. Quantitative radiomic features provide a potential evidence to predict molecular phenotype and treatment outcome. Radiomic profiles represents transcriptomic phenotypes more well.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

NASA Astrophysics Data System (ADS)

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-04-01

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans.

PubMed

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-04-15

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

PubMed Central

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-01-01

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features. PMID:27079888

Pulmonary nodule characterization, including computer analysis and quantitative features.

PubMed

Bartholmai, Brian J; Koo, Chi Wan; Johnson, Geoffrey B; White, Darin B; Raghunath, Sushravya M; Rajagopalan, Srinivasan; Moynagh, Michael R; Lindell, Rebecca M; Hartman, Thomas E

2015-03-01

Pulmonary nodules are commonly detected in computed tomography (CT) chest screening of a high-risk population. The specific visual or quantitative features on CT or other modalities can be used to characterize the likelihood that a nodule is benign or malignant. Visual features on CT such as size, attenuation, location, morphology, edge characteristics, and other distinctive "signs" can be highly suggestive of a specific diagnosis and, in general, be used to determine the probability that a specific nodule is benign or malignant. Change in size, attenuation, and morphology on serial follow-up CT, or features on other modalities such as nuclear medicine studies or MRI, can also contribute to the characterization of lung nodules. Imaging analytics can objectively and reproducibly quantify nodule features on CT, nuclear medicine, and magnetic resonance imaging. Some quantitative techniques show great promise in helping to differentiate benign from malignant lesions or to stratify the risk of aggressive versus indolent neoplasm. In this article, we (1) summarize the visual characteristics, descriptors, and signs that may be helpful in management of nodules identified on screening CT, (2) discuss current quantitative and multimodality techniques that aid in the differentiation of nodules, and (3) highlight the power, pitfalls, and limitations of these various techniques.

Utility of Intermediate-Delay Washout CT Images for Differentiation of Malignant and Benign Adrenal Lesions: A Multivariate Analysis.

PubMed

Ng, Chaan S; Altinmakas, Emre; Wei, Wei; Ghosh, Payel; Li, Xiao; Grubbs, Elizabeth G; Perrier, Nancy D; Lee, Jeffrey E; Prieto, Victor G; Hobbs, Brian P

2018-06-27

The objective of this study was to identify features that impact the diagnostic performance of intermediate-delay washout CT for distinguishing malignant from benign adrenal lesions. This retrospective study evaluated 127 pathologically proven adrenal lesions (82 malignant, 45 benign) in 126 patients who had undergone portal venous phase and intermediate-delay washout CT (1-3 minutes after portal venous phase) with or without unenhanced images. Unenhanced images were available for 103 lesions. Quantitatively, lesion CT attenuation on unenhanced (UA) and delayed (DL) images, absolute and relative percentage of enhancement washout (APEW and RPEW, respectively), descriptive CT features (lesion size, margin characteristics, heterogeneity or homogeneity, fat, calcification), patient demographics, and medical history were evaluated for association with lesion status using multiple logistic regression with stepwise model selection. Area under the ROC curve (A z ) was calculated from both univariate and multivariate analyses. The predictive diagnostic performance of multivariate evaluations was ascertained through cross-validation. A z for DL, APEW, RPEW, and UA was 0.751, 0.795, 0.829, and 0.839, respectively. Multivariate analyses yielded the following significant CT quantitative features and associated A z when combined: RPEW and DL (A z = 0.861) when unenhanced images were not available and APEW and UA (A z = 0.889) when unenhanced images were available. Patient demographics and presence of a prior malignancy were additional significant factors, increasing A z to 0.903 and 0.927, respectively. The combined predictive classifier, without and with UA available, yielded 85.7% and 87.3% accuracies with cross-validation, respectively. When appropriately combined with other CT features, washout derived from intermediate-delay CT with or without additional clinical data has potential utility in differentiating malignant from benign adrenal lesions.

SU-F-R-22: Malignancy Classification for Small Pulmonary Nodules with Radiomics and Logistic Regression

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

Huang, W; Tu, S

Purpose: We conducted a retrospective study of Radiomics research for classifying malignancy of small pulmonary nodules. A machine learning algorithm of logistic regression and open research platform of Radiomics, IBEX (Imaging Biomarker Explorer), were used to evaluate the classification accuracy. Methods: The training set included 100 CT image series from cancer patients with small pulmonary nodules where the average diameter is 1.10 cm. These patients registered at Chang Gung Memorial Hospital and received a CT-guided operation of lung cancer lobectomy. The specimens were classified by experienced pathologists with a B (benign) or M (malignant). CT images with slice thickness ofmore » 0.625 mm were acquired from a GE BrightSpeed 16 scanner. The study was formally approved by our institutional internal review board. Nodules were delineated and 374 feature parameters were extracted from IBEX. We first used the t-test and p-value criteria to study which feature can differentiate between group B and M. Then we implemented a logistic regression algorithm to perform nodule malignancy classification. 10-fold cross-validation and the receiver operating characteristic curve (ROC) were used to evaluate the classification accuracy. Finally hierarchical clustering analysis, Spearman rank correlation coefficient, and clustering heat map were used to further study correlation characteristics among different features. Results: 238 features were found differentiable between group B and M based on whether their statistical p-values were less than 0.05. A forward search algorithm was used to select an optimal combination of features for the best classification and 9 features were identified. Our study found the best accuracy of classifying malignancy was 0.79±0.01 with the 10-fold cross-validation. The area under the ROC curve was 0.81±0.02. Conclusion: Benign nodules may be treated as a malignant tumor in low-dose CT and patients may undergo unnecessary surgeries or treatments. Our study may help radiologists to differentiate nodule malignancy for low-dose CT.« less

Noise Gating Solar Images

NASA Astrophysics Data System (ADS)

DeForest, Craig; Seaton, Daniel B.; Darnell, John A.

2017-08-01

I present and demonstrate a new, general purpose post-processing technique, "3D noise gating", that can reduce image noise by an order of magnitude or more without effective loss of spatial or temporal resolution in typical solar applications.Nearly all scientific images are, ultimately, limited by noise. Noise can be direct Poisson "shot noise" from photon counting effects, or introduced by other means such as detector read noise. Noise is typically represented as a random variable (perhaps with location- or image-dependent characteristics) that is sampled once per pixel or once per resolution element of an image sequence. Noise limits many aspects of image analysis, including photometry, spatiotemporal resolution, feature identification, morphology extraction, and background modeling and separation.Identifying and separating noise from image signal is difficult. The common practice of blurring in space and/or time works because most image "signal" is concentrated in the low Fourier components of an image, while noise is evenly distributed. Blurring in space and/or time attenuates the high spatial and temporal frequencies, reducing noise at the expense of also attenuating image detail. Noise-gating exploits the same property -- "coherence" -- that we use to identify features in images, to separate image features from noise.Processing image sequences through 3-D noise gating results in spectacular (more than 10x) improvements in signal-to-noise ratio, while not blurring bright, resolved features in either space or time. This improves most types of image analysis, including feature identification, time sequence extraction, absolute and relative photometry (including differential emission measure analysis), feature tracking, computer vision, correlation tracking, background modeling, cross-scale analysis, visual display/presentation, and image compression.I will introduce noise gating, describe the method, and show examples from several instruments (including SDO/AIA , SDO/HMI, STEREO/SECCHI, and GOES-R/SUVI) that explore the benefits and limits of the technique.

Review of fluorescence guided surgery systems: identification of key performance capabilities beyond indocyanine green imaging

PubMed Central

DSouza, Alisha V.; Lin, Huiyun; Henderson, Eric R.; Samkoe, Kimberley S.; Pogue, Brian W.

2016-01-01

Abstract. There is growing interest in using fluorescence imaging instruments to guide surgery, and the leading options for open-field imaging are reviewed here. While the clinical fluorescence-guided surgery (FGS) field has been focused predominantly on indocyanine green (ICG) imaging, there is accelerated development of more specific molecular tracers. These agents should help advance new indications for which FGS presents a paradigm shift in how molecular information is provided for resection decisions. There has been a steady growth in commercially marketed FGS systems, each with their own differentiated performance characteristics and specifications. A set of desirable criteria is presented to guide the evaluation of instruments, including: (i) real-time overlay of white-light and fluorescence images, (ii) operation within ambient room lighting, (iii) nanomolar-level sensitivity, (iv) quantitative capabilities, (v) simultaneous multiple fluorophore imaging, and (vi) ergonomic utility for open surgery. In this review, United States Food and Drug Administration 510(k) cleared commercial systems and some leading premarket FGS research systems were evaluated to illustrate the continual increase in this performance feature base. Generally, the systems designed for ICG-only imaging have sufficient sensitivity to ICG, but a fraction of the other desired features listed above, with both lower sensitivity and dynamic range. In comparison, the emerging research systems targeted for use with molecular agents have unique capabilities that will be essential for successful clinical imaging studies with low-concentration agents or where superior rejection of ambient light is needed. There is no perfect imaging system, but the feature differences among them are important differentiators in their utility, as outlined in the data and tables here. PMID:27533438

Review of fluorescence guided surgery systems: identification of key performance capabilities beyond indocyanine green imaging

NASA Astrophysics Data System (ADS)

DSouza, Alisha V.; Lin, Huiyun; Henderson, Eric R.; Samkoe, Kimberley S.; Pogue, Brian W.

2016-08-01

There is growing interest in using fluorescence imaging instruments to guide surgery, and the leading options for open-field imaging are reviewed here. While the clinical fluorescence-guided surgery (FGS) field has been focused predominantly on indocyanine green (ICG) imaging, there is accelerated development of more specific molecular tracers. These agents should help advance new indications for which FGS presents a paradigm shift in how molecular information is provided for resection decisions. There has been a steady growth in commercially marketed FGS systems, each with their own differentiated performance characteristics and specifications. A set of desirable criteria is presented to guide the evaluation of instruments, including: (i) real-time overlay of white-light and fluorescence images, (ii) operation within ambient room lighting, (iii) nanomolar-level sensitivity, (iv) quantitative capabilities, (v) simultaneous multiple fluorophore imaging, and (vi) ergonomic utility for open surgery. In this review, United States Food and Drug Administration 510(k) cleared commercial systems and some leading premarket FGS research systems were evaluated to illustrate the continual increase in this performance feature base. Generally, the systems designed for ICG-only imaging have sufficient sensitivity to ICG, but a fraction of the other desired features listed above, with both lower sensitivity and dynamic range. In comparison, the emerging research systems targeted for use with molecular agents have unique capabilities that will be essential for successful clinical imaging studies with low-concentration agents or where superior rejection of ambient light is needed. There is no perfect imaging system, but the feature differences among them are important differentiators in their utility, as outlined in the data and tables here.

Analysis of Texture Using the Fractal Model

NASA Technical Reports Server (NTRS)

Navas, William; Espinosa, Ramon Vasquez

1997-01-01

Properties such as the fractal dimension (FD) can be used for feature extraction and classification of regions within an image. The FD measures the degree of roughness of a surface, so this number is used to characterize a particular region, in order to differentiate it from another. There are two basic approaches discussed in the literature to measure FD: the blanket method, and the box counting method. Both attempt to measure FD by estimating the change in surface area with respect to the change in resolution. We tested both methods but box counting resulted computationally faster and gave better results. Differential Box Counting (DBC) was used to segment a collage containing three textures. The FD is independent of directionality and brightness so five features were used derived from the original image to account for directionality and gray level biases. FD can not be measured on a point, so we use a window that slides across the image giving values of FD to the pixel on the center of the window. Windowing blurs the boundaries of adjacent classes, so an edge-preserving, feature-smoothing algorithm is used to improve classification within segments and to make the boundaries sharper. Segmentation using DBC was 90.8910 accurate.

Posterior ankle impingement in athletes: Pathogenesis, imaging features and differential diagnoses.

PubMed

Hayashi, Daichi; Roemer, Frank W; D'Hooghe, Pieter; Guermazi, Ali

2015-11-01

Posterior ankle impingement is a clinical diagnosis which can be seen following a traumatic hyper-plantar flexion event and may lead to painful symptoms in athletes such as female dancers ('en pointe'), football players, javelin throwers and gymnasts. Symptoms of posterior ankle impingement are due to failure to accommodate the reduced interval between the posterosuperior aspect of the talus and tibial plafond during plantar flexion, and can be due to osseous or soft tissue lesions. There are multiple causes of posterior ankle impingement. Most commonly, the structural correlates of impingement relate to post-traumatic synovitis and intra-articular fibrous bands-scar tissue, capsular scarring, or bony prominences. The aims of this pictorial review article is to describe different types of posterior ankle impingement due to traumatic and non-traumatic osseous and soft tissue pathology in athletes, to describe diagnostic imaging strategies of these pathologies, and illustrate their imaging features, including relevant differential diagnoses. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

MR imaging findings of adenomyosis: correlation with histopathologic features and diagnostic pitfalls.

PubMed

Tamai, Ken; Togashi, Kaori; Ito, Tsuyoshi; Morisawa, Nobuko; Fujiwara, Toshitaka; Koyama, Takashi

2005-01-01

Adenomyosis is a nonneoplastic condition, characterized by benign invasion of ectopic endometrium into the myometrium with hyperplasia of adjacent smooth muscle. The common symptoms include dysmenorrhea, menorrhagia, and abnormal uterine bleeding, but these do not allow diagnosis. Therefore, imaging plays an important role because establishment of the correct preoperative diagnosis is critical to avoid unnecessary intervention. Magnetic resonance (MR) imaging is a highly accurate noninvasive modality for diagnosis of adenomyosis, differentiation of adenomyosis from other gynecologic disorders, and planning of appropriate treatment. Although the typical MR imaging findings are well established, adenomyosis actually varies widely in terms of histopathologic features (adenomyosis with sparse glands), growth patterns (polypoid adenomyoma, adenomyotic cyst, and miniature uterus), responses to hormonal activity (tamoxifen, decidual changes), and responses to treatment (gonadotropin-releasing hormone agonist). The MR imaging findings of adenomyosis occasionally mimic those of uterine malignancy or ovarian cancer. Furthermore, malignancy occasionally develops in otherwise benign adenomyosis. Pitfalls in diagnosis of adenomyosis include myometrial contractions, leiomyoma, adenomatoid tumor, metastases, endometrial carcinoma, and endometrial stromal sarcoma. Knowledge of the various appearances of adenomyosis and the possible pitfalls in differential diagnosis help guide the determination of appropriate treatment options. (c) RSNA, 2005.

Soil moisture in relation to geologic structure and lithology, northern California

NASA Technical Reports Server (NTRS)

Rich, E. I. (Principal Investigator)

1980-01-01

The author has identified the following significant results. Structural features in the Norther California Coast Ranges are clearly discernable on Nite-IR images and some of the structural linears may results in an extension of known faults within the region. The Late Mesozoic marine sedimentary rocks along the western margin of the Sacramento Valley are clearly defined on the Nite-IR images and in a gross way individual layers of sandstone can be differentiated from shale. Late Pleistocene alluvial fans are clearly differentiated from second generation Holocene fans on the basis of tonal characteristics. Although the tonal characteristics change with the seasons, the differentiation of the two sets of fans is still possible.

Atypical β-Catenin Activated Child Hepatocellular Tumor

PubMed Central

Unlu, Havva Akmaz; Karakus, Esra; Yazal Erdem, Arzu; Yakut, Zeynep Ilerisoy

2015-01-01

Hepatocellular adenomas are a benign, focal, hepatic neoplasm that have been divided into four subtypes according to the genetic and pathological features. The β-catenin activated subtype accounts for 10-15% of all hepatocellular adenomas and specific magnetic resonance imaging features have been defined for different hepatocellular adenomas subtypes. The current study aimed to report the magnetic resonance imaging features of a well differentiated hepatocellular carcinoma that developed on the basis of β-catenin activated hepatocellular adenomas in a child. In this case, atypical diffuse steatosis was determined in the lesion. In the literature, diffuse steatosis, which is defined as a feature of the hepatocyte nuclear factor-1α-inactivated hepatocellular adenomas subtype, has not been previously reported in any β-catenin activated hepatocellular adenomas case. Interlacing magnetic resonance imaging findings between subtypes show that there are still many mysteries about this topic and larger studies are warranted. PMID:26157702

The importance of internal facial features in learning new faces.

PubMed

Longmore, Christopher A; Liu, Chang Hong; Young, Andrew W

2015-01-01

For familiar faces, the internal features (eyes, nose, and mouth) are known to be differentially salient for recognition compared to external features such as hairstyle. Two experiments are reported that investigate how this internal feature advantage accrues as a face becomes familiar. In Experiment 1, we tested the contribution of internal and external features to the ability to generalize from a single studied photograph to different views of the same face. A recognition advantage for the internal features over the external features was found after a change of viewpoint, whereas there was no internal feature advantage when the same image was used at study and test. In Experiment 2, we removed the most salient external feature (hairstyle) from studied photographs and looked at how this affected generalization to a novel viewpoint. Removing the hair from images of the face assisted generalization to novel viewpoints, and this was especially the case when photographs showing more than one viewpoint were studied. The results suggest that the internal features play an important role in the generalization between different images of an individual's face by enabling the viewer to detect the common identity-diagnostic elements across non-identical instances of the face.

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.

Breast cancer mitosis detection in histopathological images with spatial feature extraction

NASA Astrophysics Data System (ADS)

Albayrak, Abdülkadir; Bilgin, Gökhan

2013-12-01

In this work, cellular mitosis detection in histopathological images has been investigated. Mitosis detection is very expensive and time consuming process. Development of digital imaging in pathology has enabled reasonable and effective solution to this problem. Segmentation of digital images provides easier analysis of cell structures in histopathological data. To differentiate normal and mitotic cells in histopathological images, feature extraction step is very crucial step for the system accuracy. A mitotic cell has more distinctive textural dissimilarities than the other normal cells. Hence, it is important to incorporate spatial information in feature extraction or in post-processing steps. As a main part of this study, Haralick texture descriptor has been proposed with different spatial window sizes in RGB and La*b* color spaces. So, spatial dependencies of normal and mitotic cellular pixels can be evaluated within different pixel neighborhoods. Extracted features are compared with various sample sizes by Support Vector Machines using k-fold cross validation method. According to the represented results, it has been shown that separation accuracy on mitotic and non-mitotic cellular pixels gets better with the increasing size of spatial window.

Systematic Review and Meta-Analysis of CT Features for Differentiating Complicated and Uncomplicated Appendicitis.

PubMed

Kim, Hae Young; Park, Ji Hoon; Lee, Yoon Jin; Lee, Sung Soo; Jeon, Jong-June; Lee, Kyoung Ho

2018-04-01

Purpose To perform a systematic review and meta-analysis to identify computed tomographic (CT) features for differentiating complicated appendicitis in patients suspected of having appendicitis and to summarize their diagnostic accuracy. Materials and Methods Studies on diagnostic accuracy of CT features for differentiating complicated appendicitis (perforated or gangrenous appendicitis) in patients suspected of having appendicitis were searched in Ovid-MEDLINE, EMBASE, and the Cochrane Library. Overlapping descriptors used in different studies to denote the same image finding were subsumed under a single CT feature. Pooled diagnostic accuracy of the CT features was calculated by using a bivariate random effects model. CT features with pooled diagnostic odds ratios with 95% confidence intervals not including 1 were considered as informative. Results Twenty-three studies were included, and 184 overlapping descriptors for various CT findings were subsumed under 14 features. Of these, 10 features were informative for complicated appendicitis. There was a general tendency for these features to show relatively high specificity but low sensitivity. Extraluminal appendicolith, abscess, appendiceal wall enhancement defect, extraluminal air, ileus, periappendiceal fluid collection, ascites, intraluminal air, and intraluminal appendicolith showed pooled specificity greater than 70% (range, 74%-100%), but sensitivity was limited (range, 14%-59%). Periappendiceal fat stranding was the only feature that showed high sensitivity (94%; 95% confidence interval: 86%, 98%) but low specificity (40%; 95% confidence interval, 23%, 60%). Conclusion Ten informative CT features for differentiating complicated appendicitis were identified in this study, nine of which showed high specificity, but low sensitivity. © RSNA, 2017 Online supplemental material is available for this article.

Tumours around the foramen of Monro: clinical and neuroimaging features and their differential diagnosis.

PubMed

Nishio, Shunji; Morioka, Takato; Suzuki, Satoshi; Fukui, Masashi

2002-03-01

The clinical and neuroimaging features of 20 patients with lateral ventricular tumours located around the foramen of Monro were reviewed retrospectively with special emphasis on the differential diagnoses. Histologic types were: eight neurocytomas, four subependymal giant cell astrocytomas (SGCAs), three subependymomas, two fibrillary astrocytomas, and one each of pilocytic astrocytoma, malignant astrocytoma and malignant teratoma. The mean age of the patients with neurocytoma was 29.6 years, with SGCA 13.3 years and with subependymoma 55.3 years. All tumours appeared nodular in shape, and on computed tomography (CT) neurocytomas were either isodense or highdense with the brain, while all subependymomas and SGCAs were lowdense. Calcification was observed in two SGCAs, and one neurocytoma. Five neurocytomas and all four SGCAs showed mild to moderate contrast enhancement, while all three subependymomas showed either no, or scarce, enhancement. Magnetic resonance imaging (MRI) studies were available in 10 patients, with the signal characteristics of four neurocytomas and three SGCAs being nonspecific, while two subependymomas were both hypointense on T1-weighted images and hyperintense on T2-weighted images. Thus important features for differential diagnosis included age of the patient and density on precontrast CT. In this series, either an extensive excision of the tumour or a partial removal, thus relieving the obstruction of the foramina of Monro, usually provided long term survival, with 18 patients surviving a mean of 10.8 years. Copyright 2002, Elsevier Science Ltd. All rights reserved.

Objective breast tissue image classification using Quantitative Transmission ultrasound tomography

NASA Astrophysics Data System (ADS)

Malik, Bilal; Klock, John; Wiskin, James; Lenox, Mark

2016-12-01

Quantitative Transmission Ultrasound (QT) is a powerful and emerging imaging paradigm which has the potential to perform true three-dimensional image reconstruction of biological tissue. Breast imaging is an important application of QT and allows non-invasive, non-ionizing imaging of whole breasts in vivo. Here, we report the first demonstration of breast tissue image classification in QT imaging. We systematically assess the ability of the QT images’ features to differentiate between normal breast tissue types. The three QT features were used in Support Vector Machines (SVM) classifiers, and classification of breast tissue as either skin, fat, glands, ducts or connective tissue was demonstrated with an overall accuracy of greater than 90%. Finally, the classifier was validated on whole breast image volumes to provide a color-coded breast tissue volume. This study serves as a first step towards a computer-aided detection/diagnosis platform for QT.

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images.

PubMed

Niioka, Hirohiko; Asatani, Satoshi; Yoshimura, Aina; Ohigashi, Hironori; Tagawa, Seiichi; Miyake, Jun

2018-01-01

In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.

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

PubMed

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

2018-01-01

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

Differentiation of nonneoplastic and neoplastic gallbladder polyps 1 cm or bigger with multi-detector row computed tomography.

PubMed

Park, Ko Woon; Kim, Seong Hyun; Choi, Seong Ho; Lee, Won Jae

2010-01-01

To evaluate useful computed tomographic features to differentiate nonneoplastic and neoplastic gallbladder polyps 1 cm or bigger. Thirty-one patients with 32 nonneoplastic polyps and 67 patients with 73 neoplastic polyps 1 cm or bigger underwent unenhanced and dual-phase (arterial and portal venous phases) multi-detector row computed tomography. Gallbladder polyps were diagnosed by cholecystectomy. Computed tomographic features including size (1.5 cm), surface (smooth or irregular), shape (pedunculated or sessile), accompanying wall thickening, basal indentation, perception on unenhanced images, and enhancement pattern between 2 groups were compared using univariate and multivariate analyses. On univariate analysis, age 55 years or older (P = 0.0019), size bigger than 1.5 cm (P < 0.0001), irregular surface (P = 0.0033), sessile shape (P = 0.0016), accompanying wall thickening (P = 0.0056), basal indentation (P = 0.0236), and perception on unenhanced images (P < 0.0001) were significantly more frequent in neoplastic polyps as compared with nonneoplastic polyps. On multivariate analysis, size bigger than 1.5 cm (P = 0.0260), sessile shape (P = 0.0397), and perception on unenhanced images (P < 0.0001) were statistically significant. Size bigger than 1.5 cm, sessile shape, and perception on unenhanced images are the main factors that differentiate neoplastic from nonneoplastic gallbladder polyps 1 cm or bigger.

Imaging findings in craniofacial childhood rhabdomyosarcoma

PubMed Central

Merks, Johannes H. M.; Saeed, Peerooz; Balm, Alfons J. M.; Bras, Johannes; Pieters, Bradley R.; Adam, Judit A.; van Rijn, Rick R.

2010-01-01

Rhabdomyosarcoma (RMS) is the commonest paediatric soft-tissue sarcoma constituting 3–5% of all malignancies in childhood. RMS has a predilection for the head and neck area and tumours in this location account for 40% of all childhood RMS cases. In this review we address the clinical and imaging presentations of craniofacial RMS, discuss the most appropriate imaging techniques, present characteristic imaging features and offer an overview of differential diagnostic considerations. Post-treatment changes will be briefly addressed. PMID:20725831

Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps.

PubMed

Ren, Jiliang; Yuan, Ying; Wu, Yingwei; Tao, Xiaofeng

2018-05-02

The overlap of morphological feature and mean ADC value restricted clinical application of MRI in the differential diagnosis of orbital lymphoma and idiopathic orbital inflammatory pseudotumor (IOIP). In this paper, we aimed to retrospectively evaluate the combined diagnostic value of conventional magnetic resonance imaging (MRI) and whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in the differentiation of the two lesions. In total, 18 patients with orbital lymphoma and 22 patients with IOIP were included, who underwent both conventional MRI and diffusion weighted imaging before treatment. Conventional MRI features and histogram parameters derived from ADC maps, including mean ADC (ADC mean ), median ADC (ADC median ), skewness, kurtosis, 10th, 25th, 75th and 90th percentiles of ADC (ADC 10 , ADC 25 , ADC 75 , ADC 90 ) were evaluated and compared between orbital lymphoma and IOIP. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating. Differential model was built upon the selected variables and receiver operating characteristic (ROC) analysis was also performed to determine the differential ability of the model. Multivariate logistic regression showed ADC 10 (P = 0.023) and involvement of orbit preseptal space (P = 0.029) were the most promising indexes in the discrimination of orbital lymphoma and IOIP. The logistic model defined by ADC 10 and involvement of orbit preseptal space was built, which achieved an AUC of 0.939, with sensitivity of 77.30% and specificity of 94.40%. Conventional MRI feature of involvement of orbit preseptal space and ADC histogram parameter of ADC 10 are valuable in differential diagnosis of orbital lymphoma and IOIP.

Rotation covariant image processing for biomedical applications.

PubMed

Skibbe, Henrik; Reisert, Marco

2013-01-01

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences.

Analysis of 3D OCT images for diagnosis of skin tumors

NASA Astrophysics Data System (ADS)

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

2018-04-01

Skin cancer is one of the fastest growing type of cancer. It represents the most commonly diagnosed malignancy, surpassing lung, breast, colorectal and prostate cancer. So, diagnostics for different types of skin cancer on early stages is a very high challenge for medicine industry. New optical imaging techniques have been developed in order to improve diagnostics precision. Optical coherence tomography (OCT) is based on low-coherence interferometry to detect the intensity of backscattered infrared light from biological tissues by measuring the optical path length. OCT provides the advantage of real-time, in vivo, low-cost imaging of suspicious lesions without having to proceed directly to a tissue biopsy. The post-processing techniques can be used for improving the precision of diagnostics and providing solutions to overcome limitations for OCT. Image processing can include noise filtration and evaluation of textural, geometric, morphological, spectral, statistic and other features. The main idea of this investigation is using information received from multiple analyze on 2D- and 3D-OCT images for skin tumors differentiating. At first, we tested the computer algorithm on OCT data hypercubes and separated B- and C-scans. Combination of 2D and 3D data give us an opportunity to receive common information about tumor (geometric and morphological characteristics) and use more powerful algorithms for features evaluation (fractal and textural) on these separated scans. These groups of features provide closer connection to classical wide-used ABCDE criteria (Asymmetry, Border irregularity, Color, Diameter, Evolution). We used a set of features consisting of fractal dimension, Haralick's, Gabor's, Tamura's, Markov random fields, geometric features and many others. We could note about good results on the test sets in differentiation between BCC and Nevus, MM and Healthy Skin. We received dividing MM from Healthy Skin with sensitivity more 90% and specificity more 92% (168 B-scans from 8 species) by using three Haralick's features like Contrast, Correlation and Energy. The results are very promising to be tested for new cases and new bigger sets of OCT images.

Differentiation between malignant and benign thyroid nodules and stratification of papillary thyroid cancer with aggressive histological features: Whole-lesion diffusion-weighted imaging histogram analysis.

PubMed

Hao, Yonghong; Pan, Chu; Chen, WeiWei; Li, Tao; Zhu, WenZhen; Qi, JianPin

2016-12-01

To explore the usefulness of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) derived from reduced field-of-view (r-FOV) diffusion-weighted imaging (DWI) in differentiating malignant and benign thyroid nodules and stratifying papillary thyroid cancer (PTC) with aggressive histological features. This Institutional Review Board-approved, retrospective study included 93 patients with 101 pathologically proven thyroid nodules. All patients underwent preoperative r-FOV DWI at 3T. The whole-lesion ADC assessments were performed for each patient. Histogram-derived ADC parameters between different subgroups (pathologic type, extrathyroidal extension, lymph node metastasis) were compared. Receiver operating characteristic curve analysis was used to determine optimal histogram parameters in differentiating benign and malignant nodules and predicting aggressiveness of PTC. Mean ADC, median ADC, 5 th percentile ADC, 25 th percentile ADC, 75 th percentile ADC, 95 th percentile ADC (all P < 0.001), and kurtosis (P = 0.001) were significantly lower in malignant thyroid nodules, and mean ADC achieved the highest AUC (0.919) with a cutoff value of 1842.78 × 10 -6 mm 2 /s in differentiating malignant and benign nodules. Compared to the PTCs without extrathyroidal extension, PTCs with extrathyroidal extension showed significantly lower median ADC, 5 th percentile ADC, and 25 th percentile ADC. The 5 th percentile ADC achieved the highest AUC (0.757) with cutoff value of 911.5 × 10 -6 mm 2 /s for differentiating between PTCs with and without extrathyroidal extension. Whole-lesion ADC histogram analysis might help to differentiate malignant nodules from benign ones and show the PTCs with extrathyroidal extension. J. Magn. Reson. Imaging 2016;44:1546-1555. © 2016 International Society for Magnetic Resonance in Medicine.

A systematic approach to vertebral hemangioma.

PubMed

Gaudino, Simona; Martucci, Matia; Colantonio, Raffaella; Lozupone, Emilio; Visconti, Emiliano; Leone, Antonio; Colosimo, Cesare

2015-01-01

Vertebral hemangiomas (VHs) are a frequent and often incidental finding on computed tomography (CT) and magnetic resonance (MR) imaging of the spine. When their imaging appearance is "typical" (coarsened vertical trabeculae on radiographic and CT images, hyperintensity on T1- and T2-weighted MR images), the radiological diagnosis is straightforward. Nonetheless, VHs might also display an "atypical" appearance on MR imaging because of their histological features (amount of fat, vessels, and interstitial edema). Although the majority of VHs are asymptomatic and quiescent lesions, they can exhibit active behaviors, including growing quickly, extending beyond the vertebral body, and invading the paravertebral and/or epidural space with possible compression of the spinal cord and/or nerve roots ("aggressive" VHs). These "atypical" and "aggressive" VHs are a radiological challenge since they can mimic primary bony malignancies or metastases. CT plays a central role in the workup of atypical VHs, being the most appropriate imaging modality to highlight the polka-dot appearance that is representative of them. When aggressive VHs are suspected, both CT and MR are needed. MR is the best imaging modality to characterize the epidural and/or soft-tissue component, helping in the differential diagnosis. Angiography is a useful imaging adjunct for evaluating and even treating aggressive VHs. The primary objectives of this review article are to summarize the clinical, pathological, and imaging features of VHs, as well as the treatment options, and to provide a practical guide for the differential diagnosis, focusing on the rationale assessment of the findings from radiography, CT, and MR imaging.

Complex Spiral Structure in the HD 100546 Transitional Disk as Revealed by GPI and MagAO

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

Follette, Katherine B.; Macintosh, Bruce; Mullen, Wyatt

We present optical and near-infrared high-contrast images of the transitional disk HD 100546 taken with the Magellan Adaptive Optics system (MagAO) and the Gemini Planet Imager (GPI). GPI data include both polarized intensity and total intensity imagery, and MagAO data are taken in Simultaneous Differential Imaging mode at H α . The new GPI H -band total intensity data represent a significant enhancement in sensitivity and field rotation compared to previous data sets and enable a detailed exploration of substructure in the disk. The data are processed with a variety of differential imaging techniques (polarized, angular, reference, and simultaneous differentialmore » imaging) in an attempt to identify the disk structures that are most consistent across wavelengths, processing techniques, and algorithmic parameters. The inner disk cavity at 15 au is clearly resolved in multiple data sets, as are a variety of spiral features. While the cavity and spiral structures are identified at levels significantly distinct from the neighboring regions of the disk under several algorithms and with a range of algorithmic parameters, emission at the location of HD 100546 “ c ” varies from point-like under aggressive algorithmic parameters to a smooth continuous structure with conservative parameters, and is consistent with disk emission. Features identified in the HD 100546 disk bear qualitative similarity to computational models of a moderately inclined two-armed spiral disk, where projection effects and wrapping of the spiral arms around the star result in a number of truncated spiral features in forward-modeled images.« less

Magnetic Resonance Imaging-Based Assessment of Carotid Atheroma: a Comparative Study of Patients with and without Coronary Artery Disease.

PubMed

Usman, Ammara; Sadat, Umar; Teng, Zhongzhao; Graves, Martin J; Boyle, Jonathan R; Varty, Kevin; Hayes, Paul D; Gillard, Jonathan H

2017-02-01

Functional magnetic resonance (MR) imaging of atheroma using contrast media enables assessment of the systemic severity of atherosclerosis in different arterial beds. Whether black-blood imaging has similar ability remains widely unexplored. In this study, we evaluate whether black-blood imaging can differentiate carotid plaques of patients with and without coronary artery disease (CAD) in terms of morphological and biomechanical features of plaque vulnerability, thereby allowing assessment of the systemic severity nature of atherosclerosis in different arterial beds. Forty-one patients with CAD and 59 patients without CAD underwent carotid black-blood MR imaging. Plaque components were segmented to identify large lipid core (LC), ruptured fibrous cap (FC), and plaque hemorrhage (PH). These segmented contours of plaque components were used to quantify maximum structural biomechanical stress. Patients with CAD and without CAD had comparable demographics and comorbidities. Both groups had comparable prevalence of morphological features of plaque vulnerability (FC rupture, 44% versus 41%, P = .90; PH, 58% versus 47%, P = .78; large LC, 32% versus 47%, P = .17), respectively. The maximum biomechanical stress was not significantly different for both groups (241versus 278 kPa, P = .14) respectively. Black-blood imaging does not appear to have the ability to differentiate between the morphological and biomechanical features of plaque vulnerability when comparing patients with and without symptomatic atherosclerotic disease in a distant arterial territory such as coronary artery. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Efficiency of B-mode Ultrasound and Strain Elastography in Differentiating Between Benign and Malignant Cervical Lymph Nodes.

PubMed

Turgut, Eser; Celenk, Cetin; Tanrivermis Sayit, Asli; Bekci, Tumay; Gunbey, Hediye Pinar; Aslan, Kerim

2017-09-01

The purpose of this study was to evaluate the diagnostic accuracy and efficiency of ultrasonography (US), especially when combined with strain elastography (SE), in differentiating between benign and malignant cervical lymph nodes (LNs). Forty-one LNs were examined by B-mode US, power Doppler US, and SE. The following imaging features were analyzed: shape, echogenicity, echogenic hilum, calcification, intranodal vascular pattern, elasticity scores (5 categories), and strain ratio. The average strain ratio was calculated as the mean strain of the adjacent sternocleidomastoid muscle divided by the mean strain of the target LN. The results of the US and SE features were compared with the histopathologic findings. The imaging features that were significantly associated with malignant LNs were an increased short-to-long axis diameter ratio, abnormal or absence of hilum, microcalcification, type 2-3-4 vascularity, 3-4-5 elasticity scores, and a high level of strain ratio (P < 0.05). The cutoff value of the strain index was detected as 1.18. According to this, there was a significant difference (P = 0.004) in the strain index between benign and malignant LNs. Strain elastography is useful in differentiating between benign and malignant cervical LNs, thereby informing decisions to perform a biopsy and/or surgery, and facilitating follow-up.

Comparing the role of shape and texture on staging hepatic fibrosis from medical imaging

NASA Astrophysics Data System (ADS)

Zhang, Xuejun; Louie, Ryan; Liu, Brent J.; Gao, Xin; Tan, Xiaomin; Qu, Xianghe; Long, Liling

2016-03-01

The purpose of this study is to investigate the role of shape and texture in the classification of hepatic fibrosis by selecting the optimal parameters for a better Computer-aided diagnosis (CAD) system. 10 surface shape features are extracted from a standardized profile of liver; while15 texture features calculated from gray level co-occurrence matrix (GLCM) are extracted within an ROI in liver. Each combination of these input subsets is checked by using support vector machine (SVM) with leave-one-case-out method to differentiate fibrosis into two groups: normal or abnormal. The accurate rate value of all 10/15 types number of features is 66.83% by texture, while 85.74% by shape features, respectively. The irregularity of liver shape can demonstrate fibrotic grade efficiently and texture feature of CT image is not recommended to use with shape feature for interpretation of cirrhosis.

Accuracy of computed tomographic features in differentiating intestinal tuberculosis from Crohn's disease: a systematic review with meta-analysis.

PubMed

Kedia, Saurabh; Sharma, Raju; Sreenivas, Vishnubhatla; Madhusudhan, Kumble Seetharama; Sharma, Vishal; Bopanna, Sawan; Pratap Mouli, Venigalla; Dhingra, Rajan; Yadav, Dawesh Prakash; Makharia, Govind; Ahuja, Vineet

2017-04-01

Abdominal computed tomography (CT) can noninvasively image the entire gastrointestinal tract and assess extraintestinal features that are important in differentiating Crohn's disease (CD) and intestinal tuberculosis (ITB). The present meta-analysis pooled the results of all studies on the role of CT abdomen in differentiating between CD and ITB. We searched PubMed and Embase for all publications in English that analyzed the features differentiating between CD and ITB on abdominal CT. The features included comb sign, necrotic lymph nodes, asymmetric bowel wall thickening, skip lesions, fibrofatty proliferation, mural stratification, ileocaecal area, long segment, and left colonic involvements. Sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio (DOR) were calculated for all the features. Symmetric receiver operating characteristic curve was plotted for features present in >3 studies. Heterogeneity and publication bias was assessed and sensitivity analysis was performed by excluding studies that compared features on conventional abdominal CT instead of CT enterography (CTE). We included 6 studies (4 CTE, 1 conventional abdominal CT, and 1 CTE+conventional abdominal CT) involving 417 and 195 patients with CD and ITB, respectively. Necrotic lymph nodes had the highest diagnostic accuracy (sensitivity, 23%; specificity, 100%; DOR, 30.2) for ITB diagnosis, and comb sign (sensitivity, 82%; specificity, 81%; DOR, 21.5) followed by skip lesions (sensitivity, 86%; specificity, 74%; DOR, 16.5) had the highest diagnostic accuracy for CD diagnosis. On sensitivity analysis, the diagnostic accuracy of other features excluding asymmetric bowel wall thickening remained similar. Necrotic lymph nodes and comb sign on abdominal CT had the best diagnostic accuracy in differentiating CD and ITB.

A Simplified Approach to Encephalitis and Its Mimics: Key Clinical Decision Points in the Setting of Specific Imaging Abnormalities.

PubMed

McKnight, Colin D; Kelly, Aine M; Petrou, Myria; Nidecker, Anna E; Lorincz, Matthew T; Altaee, Duaa K; Gebarski, Stephen S; Foerster, Bradley

2017-06-01

Infectious encephalitis is a relatively common cause of morbidity and mortality. Treatment of infectious encephalitis with antiviral medication can be highly effective when administered promptly. Clinical mimics of encephalitis arise from a broad range of pathologic processes, including toxic, metabolic, neoplastic, autoimmune, and cardiovascular etiologies. These mimics need to be rapidly differentiated from infectious encephalitis to appropriately manage the correct etiology; however, the many overlapping signs of these various entities present a challenge to accurate diagnosis. A systematic approach that considers both the clinical manifestations and the imaging findings of infectious encephalitis and its mimics can contribute to more accurate and timely diagnosis. Following an institutional review board approval, a health insurance portability and accountability act (HIPAA)-compliant search of our institutional imaging database (teaching files) was conducted to generate a list of adult and pediatric patients who presented between January 1, 1995 and October 10, 2013 for imaging to evaluate possible cases of encephalitis. Pertinent medical records, including clinical notes as well as surgical and pathology reports, were reviewed and correlated with imaging findings. Clinical and imaging findings were combined to generate useful flowcharts designed to assist in distinguishing infectious encephalitis from its mimics. Key imaging features were reviewed and were placed in the context of the provided flowcharts. Four flowcharts were presented based on the primary anatomic site of imaging abnormality: group 1: temporal lobe; group 2: cerebral cortex; group 3: deep gray matter; and group 4: white matter. An approach that combines features on clinical presentation was then detailed. Imaging examples were used to demonstrate similarities and key differences. Early recognition of infectious encephalitis is critical, but can be quite complex due to diverse pathologies and overlapping features. Synthesis of both the clinical and imaging features of infectious encephalitis and its mimics is critical to a timely and accurate diagnosis. The use of the flowcharts presented in this article can further enable both clinicians and radiologists to more confidently differentiate encephalitis from its mimics and improve patient care. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography.

PubMed

Jeong, Jeong-Won; Shin, Dae C; Do, Synho; Marmarelis, Vasilis Z

2006-08-01

This paper presents a novel segmentation methodology for automated classification and differentiation of soft tissues using multiband data obtained with the newly developed system of high-resolution ultrasonic transmission tomography (HUTT) for imaging biological organs. This methodology extends and combines two existing approaches: the L-level set active contour (AC) segmentation approach and the agglomerative hierarchical kappa-means approach for unsupervised clustering (UC). To prevent the trapping of the current iterative minimization AC algorithm in a local minimum, we introduce a multiresolution approach that applies the level set functions at successively increasing resolutions of the image data. The resulting AC clusters are subsequently rearranged by the UC algorithm that seeks the optimal set of clusters yielding the minimum within-cluster distances in the feature space. The presented results from Monte Carlo simulations and experimental animal-tissue data demonstrate that the proposed methodology outperforms other existing methods without depending on heuristic parameters and provides a reliable means for soft tissue differentiation in HUTT images.

A Computer-Aided Distinction Method of Borderline Grades of Oral Cancer

NASA Astrophysics Data System (ADS)

Sami, Mustafa M.; Saito, Masahisa; Muramatsu, Shogo; Kikuchi, Hisakazu; Saku, Takashi

We have developed a new computer-aided diagnostic system for differentiating oral borderline malignancies in hematoxylin-eosin stained microscopic images. Epithelial dysplasia and carcinoma in-situ (CIS) of oral mucosa are two different borderline grades similar to each other, and it is difficult to distinguish between them. A new image processing and analysis method has been applied to a variety of histopathological features and shows the possibility for differentiating the oral cancer borderline grades automatically. The method is based on comparing the drop-shape similarity level in a particular manually selected pair of neighboring rete ridges. It was found that the considered similarity level in dysplasia was higher than those in epithelial CIS, of which pathological diagnoses were conventionally made by pathologists. The developed image processing method showed a good promise for the computer-aided pathological assessment of oral borderline malignancy differentiation in clinical practice.

Differentiation between Cystic Pituitary Adenomas and Rathke Cleft Cysts: A Diagnostic Model Using MRI.

PubMed

Park, M; Lee, S-K; Choi, J; Kim, S-H; Kim, S H; Shin, N-Y; Kim, J; Ahn, S S

2015-10-01

Cystic pituitary adenomas may mimic Rathke cleft cysts when there is no solid enhancing component found on MR imaging, and preoperative differentiation may enable a more appropriate selection of treatment strategies. We investigated the diagnostic potential of MR imaging features to differentiate cystic pituitary adenomas from Rathke cleft cysts and to develop a diagnostic model. This retrospective study included 54 patients with a cystic pituitary adenoma (40 women; mean age, 37.7 years) and 28 with a Rathke cleft cyst (18 women; mean age, 31.5 years) who underwent MR imaging followed by surgery. The following imaging features were assessed: the presence or absence of a fluid-fluid level, a hypointense rim on T2-weighted images, septation, an off-midline location, the presence or absence of an intracystic nodule, size change, and signal change. On the basis of the results of logistic regression analysis, a diagnostic tree model was developed to differentiate between cystic pituitary adenomas and Rathke cleft cysts. External validation was performed for an additional 16 patients with a cystic pituitary adenoma and 8 patients with a Rathke cleft cyst. The presence of a fluid-fluid level, a hypointense rim on T2-weighted images, septation, and an off-midline location were more common with pituitary adenomas, whereas the presence of an intracystic nodule was more common with Rathke cleft cysts. Multiple logistic regression analysis showed that cystic pituitary adenomas and Rathke cleft cysts can be distinguished on the basis of the presence of a fluid-fluid level, septation, an off-midline location, and the presence of an intracystic nodule (P = .006, .032, .001, and .023, respectively). Among 24 patients in the external validation population, 22 were classified correctly on the basis of the diagnostic tree model used in this study. A systematic approach using this diagnostic tree model can be helpful in distinguishing cystic pituitary adenomas from Rathke cleft cysts. © 2015 by American Journal of Neuroradiology.

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

NASA Astrophysics Data System (ADS)

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

2006-12-01

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

Biocybernetic factors in human perception and memory

NASA Technical Reports Server (NTRS)

Lai, D. C.

1975-01-01

The objective of this research is to develop biocybernetic techniques for use in the analysis and development of skills required for the enhancement of concrete images of the 'eidetic' type. The scan patterns of the eye during inspection of scenes are treated as indicators of the brain's strategy for the intake of visual information. The authors determine the features that differentiate visual scan patterns associated with superior imagery from scan patterns associated with inferior imagery, and simultaneously differentiate the EEG features correlated with superior imagery from those correlated with inferior imagery. A closely-coupled man-machine system has been designed to generate image enhancement and to train the individual to exert greater voluntary control over his own imagery. The models for EEG signals and saccadic eye movement in the man-machine system have been completed. The report describes the details of these models and discusses their usefulness.

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting.

PubMed

Tong, Tong; Ledig, Christian; Guerrero, Ricardo; Schuh, Andreas; Koikkalainen, Juha; Tolonen, Antti; Rhodius, Hanneke; Barkhof, Frederik; Tijms, Betty; Lemstra, Afina W; Soininen, Hilkka; Remes, Anne M; Waldemar, Gunhild; Hasselbalch, Steen; Mecocci, Patrizia; Baroni, Marta; Lötjönen, Jyrki; Flier, Wiesje van der; Rueckert, Daniel

2017-01-01

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.

Development of online lines-scan imaging system for chicken inspection and differentiation

NASA Astrophysics Data System (ADS)

Yang, Chun-Chieh; Chan, Diane E.; Chao, Kuanglin; Chen, Yud-Ren; Kim, Moon S.

2006-10-01

An online line-scan imaging system was developed for differentiation of wholesome and systemically diseased chickens. The hyperspectral imaging system used in this research can be directly converted to multispectral operation and would provide the ideal implementation of essential features for data-efficient high-speed multispectral classification algorithms. The imaging system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera and an imaging spectrograph for line-scan images. The system scanned the surfaces of chicken carcasses on an eviscerating line at a poultry processing plant in December 2005. A method was created to recognize birds entering and exiting the field of view, and to locate a Region of Interest on the chicken images from which useful spectra were extracted for analysis. From analysis of the difference spectra between wholesome and systemically diseased chickens, four wavelengths of 468 nm, 501 nm, 582 nm and 629 nm were selected as key wavelengths for differentiation. The method of locating the Region of Interest will also have practical application in multispectral operation of the line-scan imaging system for online chicken inspection. This line-scan imaging system makes possible the implementation of multispectral inspection using the key wavelengths determined in this study with minimal software adaptations and without the need for cross-system calibration.

Imaging mass spectrometry data reduction: automated feature identification and extraction.

PubMed

McDonnell, Liam A; van Remoortere, Alexandra; de Velde, Nico; van Zeijl, René J M; Deelder, André M

2010-12-01

Imaging MS now enables the parallel analysis of hundreds of biomolecules, spanning multiple molecular classes, which allows tissues to be described by their molecular content and distribution. When combined with advanced data analysis routines, tissues can be analyzed and classified based solely on their molecular content. Such molecular histology techniques have been used to distinguish regions with differential molecular signatures that could not be distinguished using established histologic tools. However, its potential to provide an independent, complementary analysis of clinical tissues has been limited by the very large file sizes and large number of discrete variables associated with imaging MS experiments. Here we demonstrate data reduction tools, based on automated feature identification and extraction, for peptide, protein, and lipid imaging MS, using multiple imaging MS technologies, that reduce data loads and the number of variables by >100×, and that highlight highly-localized features that can be missed using standard data analysis strategies. It is then demonstrated how these capabilities enable multivariate analysis on large imaging MS datasets spanning multiple tissues. Copyright © 2010 American Society for Mass Spectrometry. Published by Elsevier Inc. All rights reserved.

Secretory meningioma: clinicopathologic features of eight cases.

PubMed

Nishio, S; Morioka, T; Suzuki, S; Hirano, K; Fukui, M

2001-07-01

The clinical and morphological features of eight patients with meningothelial meningiomas with numerous pseudopsammoma bodies (secretory meningiomas) are presented. The six female and two male patients ranged in age from 43 to 68 years. Tumours were located at the petroclival region in two, the lateral parasellar region in two, the petrous apex in one and the sphenoid ridge in three patients. On magnetic resonance imaging, they were iso or hypointense on T1-weighted images, and hyper or isointense on T 2-weighted images. Peritumoral brain edema was absent in five cases, and was mild to moderate in three cases. Serum carcinoembryonic antigen (CEA) levels were measured preoperatively in three patients, with one having an elevated serum CEA level which re turned to normal following tumour resection. Immunohistochemical analysis on the resected tumour tissues, pseudopsammoma bodies and surrounding tumour cells were shown to be CEA-positive. Ultrastructurally, pseudopsammoma bodies were composed of granular and filamentous materials located predominantly in the intracellular lumina, which were lined by microvilli. While these morphological features of focal epithelial and secretory differentiation of tumour cells call attention to the broad spectrum of differentiation properties of meningiomas, the biological behavior of the eight tumours reported herein corresponded to those of meningiomas in general. Copyright 2001 Harcourt Publishers Ltd.

MRI differentiation of low-grade from high-grade appendicular chondrosarcoma.

PubMed

Douis, Hassan; Singh, Leanne; Saifuddin, Asif

2014-01-01

To identify magnetic resonance imaging (MRI) features which differentiate low-grade chondral lesions (atypical cartilaginous tumours/grade 1 chondrosarcoma) from high-grade chondrosarcomas (grade 2, grade 3 and dedifferentiated chondrosarcoma) of the major long bones. We identified all patients treated for central atypical cartilaginous tumours and central chondrosarcoma of major long bones (humerus, femur, tibia) over a 13-year period. The MRI studies were assessed for the following features: bone marrow oedema, soft tissue oedema, bone expansion, cortical thickening, cortical destruction, active periostitis, soft tissue mass and tumour length. The MRI-features were compared with the histopathological tumour grading using univariate, multivariate logistic regression and receiver operating characteristic curve (ROC) analyses. One hundred and seventy-nine tumours were included in this retrospective study. There were 28 atypical cartilaginous tumours, 79 grade 1 chondrosarcomas, 36 grade 2 chondrosarcomas, 13 grade 3 chondrosarcomas and 23 dedifferentiated chondrosarcomas. Multivariate analysis demonstrated that bone expansion (P = 0.001), active periostitis (P = 0.001), soft tissue mass (P < 0.001) and tumour length (P < 0.001) were statistically significant differentiating factors between low-grade and high-grade chondral lesions with an area under the ROC curve of 0.956. On MRI, bone expansion, active periostitis, soft tissue mass and tumour length can reliably differentiate high-grade chondrosarcomas from low-grade chondral lesions of the major long bones. • Accurate differentiation of low-grade from high-grade chondrosarcomas is essential before surgery • MRI can reliably differentiate high-grade from low-grade chondrosarcomas of long bone • Differentiating features are bone expansion, periostitis, soft tissue mass and tumour length • Presence of these four MRI features demonstrated a diagnostic accuracy (AUC) of 95.6 % • The findings may result in more accurate diagnosis before definitive surgery.

Interior tomography from differential phase contrast data via Hilbert transform based on spline functions

NASA Astrophysics Data System (ADS)

Yang, Qingsong; Cong, Wenxiang; Wang, Ge

2016-10-01

X-ray phase contrast imaging is an important mode due to its sensitivity to subtle features of soft biological tissues. Grating-based differential phase contrast (DPC) imaging is one of the most promising phase imaging techniques because it works with a normal x-ray tube of a large focal spot at a high flux rate. However, a main obstacle before this paradigm shift is the fabrication of large-area gratings of a small period and a high aspect ratio. Imaging large objects with a size-limited grating results in data truncation which is a new type of the interior problem. While the interior problem was solved for conventional x-ray CT through analytic extension, compressed sensing and iterative reconstruction, the difficulty for interior reconstruction from DPC data lies in that the implementation of the system matrix requires the differential operation on the detector array, which is often inaccurate and unstable in the case of noisy data. Here, we propose an iterative method based on spline functions. The differential data are first back-projected to the image space. Then, a system matrix is calculated whose components are the Hilbert transforms of the spline bases. The system matrix takes the whole image as an input and outputs the back-projected interior data. Prior information normally assumed for compressed sensing is enforced to iteratively solve this inverse problem. Our results demonstrate that the proposed algorithm can successfully reconstruct an interior region of interest (ROI) from the differential phase data through the ROI.

Identification of spectral phenotypes in age-related macular degeneration patients

NASA Astrophysics Data System (ADS)

Davis, Bert; Russell, Steven; Abramoff, Michael; Nemeth, Sheila C.; Barriga, E. Simon; Soliz, Peter

2007-02-01

The purpose of this study is to show that there exists a spectral characteristic that differentiates normal macular tissue from various types of genetic-based macular diseases. This paper demonstrates statistically that hyperspectral images of macular and other retinal tissue can be used to spectrally differentiate different forms of age-related macular degeneration. A hyperspectral fundus imaging device has been developed and tested for the purpose of collecting hyperspectral images of the human retina. A methodology based on partial least squares and ANOVA has been applied to determine the hyperspectral representation of individual spectral characteristics of retinal features. Each discrete tissue type in the retina has an identifiable spectral shape or signature which, when combined with spatial context, aids in detection of pathological features. Variations in the amount and distribution of various ocular pigments or the inclusion of additional biochemical substances will allow detection of pathological conditions prior to traditional histological presentation. Fundus imaging cameras are ubiquitous and are one of the most common imaging modalities used in documenting a patient's retinal state for diagnosis, e.g. remotely, or for monitoring the progression of an ocular disease. The added diagnostic information obtained with only a minor retro-fit of a specialized spectral camera will lead to new diagnostic information to the clinical ophthalmologist or eye-care specialist.

Going beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis.

PubMed

Venkatesh, Santosh S; Levenback, Benjamin J; Sultan, Laith R; Bouzghar, Ghizlane; Sehgal, Chandra M

2015-12-01

The goal of this study was to devise a machine learning methodology as a viable low-cost alternative to a second reader to help augment physicians' interpretations of breast ultrasound images in differentiating benign and malignant masses. Two independent feature sets consisting of visual features based on a radiologist's interpretation of images and computer-extracted features when used as first and second readers and combined by adaptive boosting (AdaBoost) and a pruning classifier resulted in a very high level of diagnostic performance (area under the receiver operating characteristic curve = 0.98) at a cost of pruning a fraction (20%) of the cases for further evaluation by independent methods. AdaBoost also improved the diagnostic performance of the individual human observers and increased the agreement between their analyses. Pairing AdaBoost with selective pruning is a principled methodology for achieving high diagnostic performance without the added cost of an additional reader for differentiating solid breast masses by ultrasound. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Analysis of breast thermograms using Gabor wavelet anisotropy index.

PubMed

Suganthi, S S; Ramakrishnan, S

2014-09-01

In this study, an attempt is made to distinguish the normal and abnormal tissues in breast thermal images using Gabor wavelet transform. Thermograms having normal, benign and malignant tissues are considered in this study and are obtained from public online database. Segmentation of breast tissues is performed by multiplying raw image and ground truth mask. Left and right breast regions are separated after removing the non-breast regions from the segmented image. Based on the pathological conditions, the separated breast regions are grouped as normal and abnormal tissues. Gabor features such as energy and amplitude in different scales and orientations are extracted. Anisotropy and orientation measures are calculated from the extracted features and analyzed. A distinctive variation is observed among different orientations of the extracted features. It is found that the anisotropy measure is capable of differentiating the structural changes due to varied metabolic conditions. Further, the Gabor features also showed relative variations among different pathological conditions. It appears that these features can be used efficiently to identify normal and abnormal tissues and hence, improve the relevance of breast thermography in early detection of breast cancer and content based image retrieval.

Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers.

PubMed

Mougiakakou, Stavroula G; Valavanis, Ioannis K; Nikita, Alexandra; Nikita, Konstantina S

2007-09-01

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed. Number of regions of interests (ROIs) corresponding to C1-C4 have been defined by experienced radiologists in non-enhanced liver CT images. For each ROI, five distinct sets of texture features were extracted using first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. Two different ECs were constructed and compared. The first one consists of five multilayer perceptron neural networks (NNs), each using as input one of the computed texture feature sets or its reduced version after genetic algorithm-based feature selection. The second EC comprised five different primary classifiers, namely one multilayer perceptron NN, one probabilistic NN, and three k-nearest neighbor classifiers, each fed with the combination of the five texture feature sets or their reduced versions. The final decision of each EC was extracted by using appropriate voting schemes, while bootstrap re-sampling was utilized in order to estimate the generalization ability of the CAD architectures based on the available relatively small-sized data set. The best mean classification accuracy (84.96%) is achieved by the second EC using a fused feature set, and the weighted voting scheme. The fused feature set was obtained after appropriate feature selection applied to specific subsets of the original feature set. The comparative assessment of the various CAD architectures shows that combining three types of classifiers with a voting scheme, fed with identical feature sets obtained after appropriate feature selection and fusion, may result in an accurate system able to assist differential diagnosis of focal liver lesions from non-enhanced CT images.

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.

Adenomyosis: from the sign to the diagnosis. Imaging, diagnostic pitfalls and differential diagnosis: a pictorial review.

PubMed

Valentini, A L; Speca, S; Gui, B; Soglia, G; Soglia, B G; Miccò, M; Bonomo, L

2011-12-01

Adenomyosis is a pathological gynaecological condition characterised by benign invasion of the endometrium into the myometrium. It is often misdiagnosed, or is not easily recognised, although it is responsible for disabling symptoms such as menorrhagia, abnormal uterine bleeding, dysmenorrhoea and infertility in premenopausal women. The aim of this pictorial review is to analyse the features of adenomyosis by illustrating the most usual and typical imaging patterns, along with the unusual appearances, seen in a vast array of gynaecological imaging modalities. The different findings of focal and diffuse adenomyosis along with the diagnostic limitations of ultrasound, hysterosalpingography and magnetic resonance imaging are described, as are the pitfalls and differential diagnosis with other pathological conditions that are often misdiagnosed as adenomyosis. The role of the different imaging modalities in planning appropriate treatment and their usefulness in monitoring therapy are also discussed.

Deep learning based classification of breast tumors with shear-wave elastography.

PubMed

Zhang, Qi; Xiao, Yang; Dai, Wei; Suo, Jingfeng; Wang, Congzhi; Shi, Jun; Zheng, Hairong

2016-12-01

This study aims to build a deep learning (DL) architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE), and to evaluate the DL architecture in differentiation between benign and malignant breast tumors. We construct a two-layer DL architecture for SWE feature extraction, comprised of the point-wise gated Boltzmann machine (PGBM) and the restricted Boltzmann machine (RBM). The PGBM contains task-relevant and task-irrelevant hidden units, and the task-relevant units are connected to the RBM. Experimental evaluation was performed with five-fold cross validation on a set of 227 SWE images, 135 of benign tumors and 92 of malignant tumors, from 121 patients. The features learned with our DL architecture were compared with the statistical features quantifying image intensity and texture. Results showed that the DL features achieved better classification performance with an accuracy of 93.4%, a sensitivity of 88.6%, a specificity of 97.1%, and an area under the receiver operating characteristic curve of 0.947. The DL-based method integrates feature learning with feature selection on SWE. It may be potentially used in clinical computer-aided diagnosis of breast cancer. Copyright © 2016 Elsevier B.V. All rights reserved.

In vivo imaging of oral neoplasia using a miniaturized fiber optic confocal reflectance microscope.

PubMed

Maitland, Kristen C; Gillenwater, Ann M; Williams, Michelle D; El-Naggar, Adel K; Descour, Michael R; Richards-Kortum, Rebecca R

2008-11-01

The purpose of this study was to determine whether in vivo images of oral mucosa obtained with a fiber optic confocal reflectance microscope could be used to differentiate normal and neoplastic tissues. We imaged 20 oral sites in eight patients undergoing surgery for squamous cell carcinoma. Normal and abnormal areas within the oral cavity were identified clinically, and real-time videos of each site were obtained in vivo using a fiber optic confocal reflectance microscope. Following imaging, each site was biopsied and submitted for histopathologic examination. We identified distinct features, such as nuclear irregularity and spacing, which can be used to qualitatively differentiate between normal and abnormal tissue. Representative confocal images of normal, pre-neoplastic, and neoplastic oral tissue are presented. Previous work using much larger microscopes has demonstrated the ability of confocal reflectance microscopy to image cellular and tissue architecture in situ. New advances in technology have enabled miniaturization of imaging systems for in vivo use.

Voxel classification based airway tree segmentation

NASA Astrophysics Data System (ADS)

Lo, Pechin; de Bruijne, Marleen

2008-03-01

This paper presents a voxel classification based method for segmenting the human airway tree in volumetric computed tomography (CT) images. In contrast to standard methods that use only voxel intensities, our method uses a more complex appearance model based on a set of local image appearance features and Kth nearest neighbor (KNN) classification. The optimal set of features for classification is selected automatically from a large set of features describing the local image structure at several scales. The use of multiple features enables the appearance model to differentiate between airway tree voxels and other voxels of similar intensities in the lung, thus making the segmentation robust to pathologies such as emphysema. The classifier is trained on imperfect segmentations that can easily be obtained using region growing with a manual threshold selection. Experiments show that the proposed method results in a more robust segmentation that can grow into the smaller airway branches without leaking into emphysematous areas, and is able to segment many branches that are not present in the training set.

Rotation Covariant Image Processing for Biomedical Applications

PubMed Central

Reisert, Marco

2013-01-01

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences. PMID:23710255

Semi-automated discrimination of retinal pigmented epithelial cells in two-photon fluorescence images of mouse retinas.

PubMed

Alexander, Nathan S; Palczewska, Grazyna; Palczewski, Krzysztof

2015-08-01

Automated image segmentation is a critical step toward achieving a quantitative evaluation of disease states with imaging techniques. Two-photon fluorescence microscopy (TPM) has been employed to visualize the retinal pigmented epithelium (RPE) and provide images indicating the health of the retina. However, segmentation of RPE cells within TPM images is difficult due to small differences in fluorescence intensity between cell borders and cell bodies. Here we present a semi-automated method for segmenting RPE cells that relies upon multiple weak features that differentiate cell borders from the remaining image. These features were scored by a search optimization procedure that built up the cell border in segments around a nucleus of interest. With six images used as a test, our method correctly identified cell borders for 69% of nuclei on average. Performance was strongly dependent upon increasing retinosome content in the RPE. TPM image analysis has the potential of providing improved early quantitative assessments of diseases affecting the RPE.

Malignant melanocytic neoplasm of pancreas with liver metastasis: Is it malignant melanoma or clear cell sarcoma?

PubMed

Kodiatte, Thomas Alex; George, Sam Varghese; Chacko, Raju Titus; Ramakrishna, Banumathi

2017-01-01

Malignant melanocytic neoplasm, usually seen in soft tissues, is rare in a visceral location and presents as a diagnostic dilemma. We present a case of pancreatic malignant melanocytic neoplasm with liver metastasis. A 58-year-old man presented with left upper abdominal swelling and loss of appetite. Imaging revealed a large mass arising from the pancreatic tail, and this was diagnosed as malignant neoplasm with melanocytic differentiation on biopsy with the possible differentials of malignant melanoma, clear cell sarcoma (CCS), and perivascular epithelioid cell neoplasm. The patient underwent distal pancreatectomy and splenectomy for the same. Follow-up imaging 6 months later showed a metastatic liver lesion, for which he also underwent a liver resection. BRAF mutational analysis was found to be negative. Both CCS and malignant melanoma have similar morphological features and melanocytic differentiation, but each harbors a distinct genetic background. Differentiation of both has diagnostic and therapeutic implications.

Squash cytology findings of subependymomas: A report of three cases and differential diagnosis.

PubMed

Tokumitsu, Takako; Sato, Yuichiro; Fukushima, Tsuyoshi; Takeshima, Hideo; Sato, Shinya; Asada, Yujiro

2018-03-01

Subependymomas are slowly growing glial tumors, corresponding to WHO grade I. Few descriptions of the cytologic features of this neoplasm are available. This study describes the cytologic features of three subependymomas, as well as their differential diagnosis based on cytology. Three men, aged 52, 56, and 63 years, presented with headache. Magnetic resonance imaging revealed a nodular intraventricular mass in all three patients. Intraoperative squash cytology specimens from the three intraventricular tumors showed nodular clusters with microcystic changes. Nuclei were round to oval in shape, but showed no evidence of severe nuclear atypia or mitoses. Histological examination showed features of subependymoma. Squash cytology findings, including nodular clusters, mild cellular atypia, microcystic changes, and mucoid material, are useful in the rapid intraoperative diagnosis of subependymoma. © 2017 Wiley Periodicals, Inc.

Analysis and classification of commercial ham slice images using directional fractal dimension features.

PubMed

Mendoza, Fernando; Valous, Nektarios A; Allen, Paul; Kenny, Tony A; Ward, Paddy; Sun, Da-Wen

2009-02-01

This paper presents a novel and non-destructive approach to the appearance characterization and classification of commercial pork, turkey and chicken ham slices. Ham slice images were modelled using directional fractal (DF(0°;45°;90°;135°)) dimensions and a minimum distance classifier was adopted to perform the classification task. Also, the role of different colour spaces and the resolution level of the images on DF analysis were investigated. This approach was applied to 480 wafer thin ham slices from four types of hams (120 slices per type): i.e., pork (cooked and smoked), turkey (smoked) and chicken (roasted). DF features were extracted from digitalized intensity images in greyscale, and R, G, B, L(∗), a(∗), b(∗), H, S, and V colour components for three image resolution levels (100%, 50%, and 25%). Simulation results show that in spite of the complexity and high variability in colour and texture appearance, the modelling of ham slice images with DF dimensions allows the capture of differentiating textural features between the four commercial ham types. Independent DF features entail better discrimination than that using the average of four directions. However, DF dimensions reveal a high sensitivity to colour channel, orientation and image resolution for the fractal analysis. The classification accuracy using six DF dimension features (a(90°)(∗),a(135°)(∗),H(0°),H(45°),S(0°),H(90°)) was 93.9% for training data and 82.2% for testing data.

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery.

PubMed

Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W; Chen, Zhuo Georgia; Fei, Baowei

2015-01-01

Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery

NASA Astrophysics Data System (ADS)

Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W.; Chen, Zhuo Georgia; Fei, Baowei

2015-12-01

Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

Correlative feature analysis of FFDM images

NASA Astrophysics Data System (ADS)

Yuan, Yading; Giger, Maryellen L.; Li, Hui; Sennett, Charlene

2008-03-01

Identifying the corresponding image pair of a lesion is an essential step for combining information from different views of the lesion to improve the diagnostic ability of both radiologists and CAD systems. Because of the non-rigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this study, we present a computerized framework that differentiates the corresponding images from different views of a lesion from non-corresponding ones. A dual-stage segmentation method, which employs an initial radial gradient index(RGI) based segmentation and an active contour model, was initially applied to extract mass lesions from the surrounding tissues. Then various lesion features were automatically extracted from each of the two views of each lesion to quantify the characteristics of margin, shape, size, texture and context of the lesion, as well as its distance to nipple. We employed a two-step method to select an effective subset of features, and combined it with a BANN to obtain a discriminant score, which yielded an estimate of the probability that the two images are of the same physical lesion. ROC analysis was used to evaluate the performance of the individual features and the selected feature subset in the task of distinguishing between corresponding and non-corresponding pairs. By using a FFDM database with 124 corresponding image pairs and 35 non-corresponding pairs, the distance feature yielded an AUC (area under the ROC curve) of 0.8 with leave-one-out evaluation by lesion, and the feature subset, which includes distance feature, lesion size and lesion contrast, yielded an AUC of 0.86. The improvement by using multiple features was statistically significant as compared to single feature performance. (p<0.001)

Clinicopathological findings of primary esophageal malignant melanoma: report of six cases and review of literature.

PubMed

Zheng, Jinfeng; Mo, Haiying; Ma, Shufang; Wang, Zhenzheng

2014-01-01

We studied images and histopathological features of primary esophageal malignant melanoma to explore the clinical pathological features, diagnosis, differential diagnoses, and treatment. Immunolabelling was conducted on six cases of esophageal malignant melanoma using histological and immunohistochemical techniques. Combined with the related literature, the clinical manifestations, imaging, histopathological and immunohistochemical features, treatment, and prognosis of primary esophageal malignant melanoma were observed and analyzed. The six patients with primary esophageal malignant melanoma were all male with an average age of 63.4 years. Poor food intake was observed in all patients, and the symptoms showed progressive aggravation. Endoscopic feed tube revealed dark brown and black nodular and polypoid lesions, 1/4-1/2 loop cavity. Tumor histopathology revealed the following characteristics: tumor cells arranged in nests, sheets and cords, round or polygonal, abundant and red-stained cytoplasm, melanin granules in the cytoplasm, heterogeneous nucleus sizes, centered or deviated nuclei, clearly identifiable nucleoli, and apparent pathological mitosis. The immune phenotype was as follows: tumor cells had diffuse expression of HMB45, Melan A, and S100. The cells were CK negative, and the Ki67-positive cell number was 40%-45%. Primary esophageal malignant melanoma is rare with high malignancy and poor prognosis. Immunohistochemical staining is helpful for diagnosing this tumor. The differential diagnosis includes low differentiated carcinoma, primitive neuroectodermal tumor, esophageal sarcomatoid carcinoma, esophageal lymphoma, and other tumors.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Is an "ideal" service institution image the same for all referral sources? The case of chemical dependency treatment programs.

PubMed

Johnson, K; LaTour, M S

1993-01-01

In a competitive market like chemical dependency treatment, segmenting the professional referral market according to an "ideal" service image may offer a service institution a strategic advantage. Results of this study suggest that while different professionals in a referral market may attach differential importance to the same service feature, a favorable or unfavorable "image" seems to encompass how well both the professional and the professionals' client are treated by the service institution.

Thoracic Imaging Features of Legionnaire's Disease.

PubMed

Mittal, Sameer; Singh, Ayushi P; Gold, Menachem; Leung, Ann N; Haramati, Linda B; Katz, Douglas S

2017-03-01

Imaging examinations are often performed in patients with Legionnaires' disease. The literature to date has documented that the imaging findings in this disorder are relatively nonspecific, and it is therefore difficult to prospectively differentiate legionella pneumonia from other forms of pneumonia, and from other noninfectious thoracic processes. Through a review of clinical cases and the literature, our objective is for the reader to gain a better understanding of the spectrum of radiographic manifestations of Legionnaires' disease. Copyright © 2016 Elsevier Inc. All rights reserved.

High-Precision Image Aided Inertial Navigation with Known Features: Observability Analysis and Performance Evaluation

PubMed Central

Jiang, Weiping; Wang, Li; Niu, Xiaoji; Zhang, Quan; Zhang, Hui; Tang, Min; Hu, Xiangyun

2014-01-01

A high-precision image-aided inertial navigation system (INS) is proposed as an alternative to the carrier-phase-based differential Global Navigation Satellite Systems (CDGNSSs) when satellite-based navigation systems are unavailable. In this paper, the image/INS integrated algorithm is modeled by a tightly-coupled iterative extended Kalman filter (IEKF). Tightly-coupled integration ensures that the integrated system is reliable, even if few known feature points (i.e., less than three) are observed in the images. A new global observability analysis of this tightly-coupled integration is presented to guarantee that the system is observable under the necessary conditions. The analysis conclusions were verified by simulations and field tests. The field tests also indicate that high-precision position (centimeter-level) and attitude (half-degree-level)-integrated solutions can be achieved in a global reference. PMID:25330046

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.

Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation

PubMed Central

Qian, Xiaohua; Tan, Hua; Zhang, Jian; Zhao, Weilin; Chan, Michael D.; Zhou, Xiaobo

2016-01-01

Purpose: Pseudoprogression (PsP) can mimic true tumor progression (TTP) on magnetic resonance imaging in patients with glioblastoma multiform (GBM). The phenotypical similarity between PsP and TTP makes it a challenging task for physicians to distinguish these entities. So far, no approved biomarkers or computer-aided diagnosis systems have been used clinically for this purpose. Methods: To address this challenge, the authors developed an objective classification system for PsP and TTP based on longitudinal diffusion tensor imaging. A novel spatio-temporal discriminative dictionary learning scheme was proposed to differentiate PsP and TTP, thereby avoiding segmentation of the region of interest. The authors constructed a novel discriminative sparse matrix with the classification-oriented dictionary learning approach by excluding the shared features of two categories, so that the pooled features captured the subtle difference between PsP and TTP. The most discriminating features were then identified from the pooled features by their feature scoring system. Finally, the authors stratified patients with GBM into PsP and TTP by a support vector machine approach. Tenfold cross-validation (CV) and the area under the receiver operating characteristic (AUC) were used to assess the robustness of the developed system. Results: The average accuracy and AUC values after ten rounds of tenfold CV were 0.867 and 0.92, respectively. The authors also assessed the effects of different methods and factors (such as data types, pooling techniques, and dimensionality reduction approaches) on the performance of their classification system which obtained the best performance. Conclusions: The proposed objective classification system without segmentation achieved a desirable and reliable performance in differentiating PsP from TTP. Thus, the developed approach is expected to advance the clinical research and diagnosis of PsP and TTP. PMID:27806598

TU-D-207B-03: Early Assessment of Response to Chemoradiotherapy Based On Textural Analysis of Pre and Mid-Treatment FDG-PET Image in Locally Advanced Head and Neck Cancer

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

Cui, Y; Pollom, E; Loo, B

Purpose: To evaluate whether tumor textural features extracted from both pre- and mid-treatment FDG-PET images predict early response to chemoradiotherapy in locally advanced head and neck cancer, and investigate whether they provide complementary value to conventional volume-based measurements. Methods: Ninety-four patients with locally advanced head and neck cancers were retrospectively studied. All patients received definitive chemoradiotherapy and underwent FDG-PET planning scans both before and during treatment. Within the primary tumor we extracted 6 textural features based on gray-level co-occurrence matrices (GLCM): entropy, dissimilarity, contrast, correlation, energy, and homogeneity. These image features were evaluated for their predictive power of treatment responsemore » to chemoradiotherapy in terms of local recurrence free survival (LRFS) and progression free survival (PFS). Logrank test were used to assess the statistical significance of the stratification between low- and high-risk groups. P-values were adjusted for multiple comparisons by the false discovery rate (FDR) method. Results: All six textural features extracted from pre-treatment PET images significantly differentiated low- and high-risk patient groups for LRFS (P=0.011–0.038) and PFS (P=0.029–0.034). On the other hand, none of the textural features on mid-treatment PET images was statistically significant in stratifying LRFS (P=0.212–0.445) or PFS (P=0.168–0.299). An imaging signature that combines textural feature (GLCM homogeneity) and metabolic tumor volume showed an improved performance for predicting LRFS (hazard ratio: 22.8, P<0.0001) and PFS (hazard ratio: 13.9, P=0.0005) in leave-one-out cross validation. Intra-tumor heterogeneity measured by textural features was significantly lower in mid-treatment PET images than in pre-treatment PET images (T-test: P<1.4e-6). Conclusion: Tumor textural features on pretreatment FDG-PET images are predictive for response to chemoradiotherapy in locally advanced head and neck cancer. The complementary information offered by textural features improves patient stratification and may potentially aid in personalized risk-adaptive therapy.« less

Differential diagnosis of neurodegenerative diseases using structural MRI data

PubMed Central

Koikkalainen, Juha; Rhodius-Meester, Hanneke; Tolonen, Antti; Barkhof, Frederik; Tijms, Betty; Lemstra, Afina W.; Tong, Tong; Guerrero, Ricardo; Schuh, Andreas; Ledig, Christian; Rueckert, Daniel; Soininen, Hilkka; Remes, Anne M.; Waldemar, Gunhild; Hasselbalch, Steen; Mecocci, Patrizia; van der Flier, Wiesje; Lötjönen, Jyrki

2016-01-01

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making. PMID:27104138

Use of Myocardial T1 Mapping at 3.0 T to Differentiate Anderson-Fabry Disease from Hypertrophic Cardiomyopathy.

PubMed

Karur, Gauri R; Robison, Sean; Iwanochko, Robert M; Morel, Chantal F; Crean, Andrew M; Thavendiranathan, Paaladinesh; Nguyen, Elsie T; Mathur, Shobhit; Wasim, Syed; Hanneman, Kate

2018-04-24

Purpose To compare left ventricular (LV) and right ventricular (RV) 3.0-T cardiac magnetic resonance (MR) imaging T1 values in Anderson-Fabry disease (AFD) and hypertrophic cardiomyopathy (HCM) and evaluate the diagnostic value of native T1 values beyond age, sex, and conventional imaging features. Materials and Methods For this prospective study, 30 patients with gene-positive AFD (37% male; mean age ± standard deviation, 45.0 years ± 14.1) and 30 patients with HCM (57% male; mean age, 49.3 years ± 13.5) were prospectively recruited between June 2016 and September 2017 to undergo cardiac MR imaging T1 mapping with a modified Look-Locker inversion recovery (MOLLI) acquisition scheme at 3.0 T (repetition time msec/echo time msec, 280/1.12; section thickness, 8 mm). LV and RV T1 values were evaluated. Statistical analysis included independent samples t test, receiver operating characteristic curve analysis, multivariable logistic regression, and likelihood ratio test. Results Septal LV, global LV, and RV native T1 values were significantly lower in AFD compared with those in HCM (1161 msec ± 47 vs 1296 msec ± 55, respectively [P < .001]; 1192 msec ± 52 vs 1268 msec ± 55 [P < .001]; and 1221 msec ± 54 vs 1271 msec ± 37 [P = .001], respectively). A septal LV native T1 cutoff point of 1220 msec or lower distinguished AFD from HCM with sensitivity of 97%, specificity of 93%, and accuracy of 95%. Septal LV native T1 values differentiated AFD from HCM after adjustment for age, sex, and conventional imaging features (odds ratio, 0.94; 95% confidence interval: 0.91, 0.98; P = < .001). In a nested logistic regression model with age, sex, and conventional imaging features, model fit was significantly improved by the addition of septal LV native T1 values (χ 2 [df = 1] = 33.4; P < .001). Conclusion Cardiac MR imaging native T1 values at 3.0 T are significantly lower in patients with AFD compared with those with HCM and provide independent and incremental diagnostic value beyond age, sex, and conventional imaging features. © RSNA, 2018.

A new assessment model for tumor heterogeneity analysis with [18]F-FDG PET images.

PubMed

Wang, Ping; Xu, Wengui; Sun, Jian; Yang, Chengwen; Wang, Gang; Sa, Yu; Hu, Xin-Hua; Feng, Yuanming

2016-01-01

It has been shown that the intratumor heterogeneity can be characterized with quantitative analysis of the [18]F-FDG PET image data. The existing models employ multiple parameters for feature extraction which makes it difficult to implement in clinical settings for the quantitative characterization. This article reports an easy-to-use and differential SUV based model for quantitative assessment of the intratumor heterogeneity from 3D [18]F-FDG PET image data. An H index is defined to assess tumor heterogeneity by summing voxel-wise distribution of differential SUV from the [18]F-FDG PET image data. The summation is weighted by the distance of SUV difference among neighboring voxels from the center of the tumor and can thus yield increased values for tumors with peripheral sub-regions of high SUV that often serves as an indicator of augmented malignancy. Furthermore, the sign of H index is used to differentiate the rate of change for volume averaged SUV from its center to periphery. The new model with the H index has been compared with a widely-used model of gray level co-occurrence matrix (GLCM) for image texture characterization with phantoms of different configurations and the [18]F-FDG PET image data of 6 lung cancer patients to evaluate its effectiveness and feasibility for clinical uses. The comparison of the H index and GLCM parameters with the phantoms demonstrate that the H index can characterize the SUV heterogeneity in all of 6 2D phantoms while only 1 GLCM parameter can do for 1 and fail to differentiate for other 2D phantoms. For the 8 3D phantoms, the H index can clearly differentiate all of them while the 4 GLCM parameters provide complicated patterns in the characterization. Feasibility study with the PET image data from 6 lung cancer patients show that the H index provides an effective single-parameter metric to characterize tumor heterogeneity in terms of the local SUV variation, and it has higher correlation with tumor volume change after radiotherapy (R(2) = 0.83) than the 4 GLCM parameters (R(2) = 0.63, 0.73, 0.59 and 0.75 for Energy, Contrast, Local Homogeneity and Entropy respectively). The new model of the H index has the capacity to characterize the intratumor heterogeneity feature from 3D [18]F-FDG PET image data. As a single parameter with an intuitive definition, the H index offers potential for clinical applications.

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

In this paper, we propose a report about our examining of the validity of OCT in identifying changes using a skin cancer texture analysis compiled from Haralick texture features, fractal dimension, Markov random field method and the complex directional features from different tissues. Described features have been used to detect specific spatial characteristics, which can differentiate healthy tissue from diverse skin cancers in cross-section OCT images (B- and/or C-scans). In this work, we used an interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in OCT images. The Haralick texture features as contrast, correlation, energy, and homogeneity have been calculated in various directions. A box-counting method is performed to evaluate fractal dimension of skin probes. Markov random field have been used for the quality enhancing of the classifying. Additionally, we used the complex directional field calculated by the local gradient methodology to increase of the assessment quality of the diagnosis method. Our results demonstrate that these texture features may present helpful information to discriminate tumor from healthy tissue. The experimental data set contains 488 OCT-images with normal skin and tumors as Basal Cell Carcinoma (BCC), Malignant Melanoma (MM) and Nevus. All images were acquired from our laboratory SD-OCT setup based on broadband light source, delivering an output power of 20 mW at the central wavelength of 840 nm with a bandwidth of 25 nm. We obtained sensitivity about 97% and specificity about 73% for a task of discrimination between MM and Nevus.

Algorithms for Autonomous Plume Detection on Outer Planet Satellites

NASA Astrophysics Data System (ADS)

Lin, Y.; Bunte, M. K.; Saripalli, S.; Greeley, R.

2011-12-01

We investigate techniques for automated detection of geophysical events (i.e., volcanic plumes) from spacecraft images. The algorithms presented here have not been previously applied to detection of transient events on outer planet satellites. We apply Scale Invariant Feature Transform (SIFT) to raw images of Io and Enceladus from the Voyager, Galileo, Cassini, and New Horizons missions. SIFT produces distinct interest points in every image; feature descriptors are reasonably invariant to changes in illumination, image noise, rotation, scaling, and small changes in viewpoint. We classified these descriptors as plumes using the k-nearest neighbor (KNN) algorithm. In KNN, an object is classified by its similarity to examples in a training set of images based on user defined thresholds. Using the complete database of Io images and a selection of Enceladus images where 1-3 plumes were manually detected in each image, we successfully detected 74% of plumes in Galileo and New Horizons images, 95% in Voyager images, and 93% in Cassini images. Preliminary tests yielded some false positive detections; further iterations will improve performance. In images where detections fail, plumes are less than 9 pixels in size or are lost in image glare. We compared the appearance of plumes and illuminated mountain slopes to determine the potential for feature classification. We successfully differentiated features. An advantage over other methods is the ability to detect plumes in non-limb views where they appear in the shadowed part of the surface; improvements will enable detection against the illuminated background surface where gradient changes would otherwise preclude detection. This detection method has potential applications to future outer planet missions for sustained plume monitoring campaigns and onboard automated prioritization of all spacecraft data. The complementary nature of this method is such that it could be used in conjunction with edge detection algorithms to increase effectiveness. We have demonstrated an ability to detect transient events above the planetary limb and on the surface and to distinguish feature classes in spacecraft images.

Ring-Like Enhancement of Hepatocellular Carcinoma in Gadoxetic Acid-Enhanced Multiphasic Hepatic Arterial Phase Imaging With Differential Subsampling With Cartesian Ordering.

PubMed

Ichikawa, Shintaro; Motosugi, Utaroh; Oishi, Naoki; Shimizu, Tatsuya; Wakayama, Tetsuya; Enomoto, Nobuyuki; Matsuda, Masanori; Onishi, Hiroshi

2018-04-01

The aim of this study was to evaluate the efficacy of multiphasic hepatic arterial phase (HAP) imaging using DISCO (differential subsampling with Cartesian ordering) in increasing the confidence of diagnosis of hepatocellular carcinoma (HCC). This retrospective study was approved by the institutional review board, and the requirement for informed patient consent was waived. Consecutive patients (from 2 study periods) with malignant liver nodules were examined by gadoxetic acid-enhanced magnetic resonance imaging using either multiphasic (6 phases; n = 135) or single (n = 230) HAP imaging, which revealed 519 liver nodules other than benign ones (HCC, 497; cholangiocarcinoma, 11; metastases, 10; and malignant lymphoma, 1). All nodules were scored in accordance with the Liver Imaging Reporting and Data System (LI-RADS v2014), with or without consideration of ring-like enhancement in multiphasic HAP images as a major feature. In the multiphasic HAP group, 178 of 191 HCCs were scored as LR-3 to LR-5 (3 [1.69%], 85 [47.8%], and 90 [50.6%], respectively). Upon considering ring-like enhancement in multiphasic HAP images as a major feature, 5 more HCCs were scored as LR-5 (95 [53.4%]), which was a significantly more confident diagnosis than that with single HAP images (295 of 306 HCCs scored as LR-3 to LR-5: 13 [4.41%], 147 [49.8%], and 135 [45.8%], respectively; P = 0.0296). There was no significant difference in false-positive or false-negative diagnoses between the multiphasic and single HAP groups (P = 0.8400 and 0.1043, respectively). Multiphasic HAP imaging can improve the confidence of diagnosis of HCCs in gadoxetic acid-enhanced magnetic resonance imaging.

A new method using multiphoton imaging and morphometric analysis for differentiating chromophobe renal cell carcinoma and oncocytoma kidney tumors

NASA Astrophysics Data System (ADS)

Wu, Binlin; Mukherjee, Sushmita; Jain, Manu

2016-03-01

Distinguishing chromophobe renal cell carcinoma (chRCC) from oncocytoma on hematoxylin and eosin images may be difficult and require time-consuming ancillary procedures. Multiphoton microscopy (MPM), an optical imaging modality, was used to rapidly generate sub-cellular histological resolution images from formalin-fixed unstained tissue sections from chRCC and oncocytoma.Tissues were excited using 780nm wavelength and emission signals (including second harmonic generation and autofluorescence) were collected in different channels between 390 nm and 650 nm. Granular structure in the cell cytoplasm was observed in both chRCC and oncocytoma. Quantitative morphometric analysis was conducted to distinguish chRCC and oncocytoma. To perform the analysis, cytoplasm and granules in tumor cells were segmented from the images. Their area and fluorescence intensity were found in different channels. Multiple features were measured to quantify the morphological and fluorescence properties. Linear support vector machine (SVM) was used for classification. Re-substitution validation, cross validation and receiver operating characteristic (ROC) curve were implemented to evaluate the efficacy of the SVM classifier. A wrapper feature algorithm was used to select the optimal features which provided the best predictive performance in separating the two tissue types (classes). Statistical measures such as sensitivity, specificity, accuracy and area under curve (AUC) of ROC were calculated to evaluate the efficacy of the classification. Over 80% accuracy was achieved as the predictive performance. This method, if validated on a larger and more diverse sample set, may serve as an automated rapid diagnostic tool to differentiate between chRCC and oncocytoma. An advantage of such automated methods are that they are free from investigator bias and variability.

Computed tomography in hypersensitivity pneumonitis: main findings, differential diagnosis and pitfalls.

PubMed

Dias, Olívia Meira; Baldi, Bruno Guedes; Pennati, Francesca; Aliverti, Andrea; Chate, Rodrigo Caruso; Sawamura, Márcio Valente Yamada; Carvalho, Carlos Roberto Ribeiro de; Albuquerque, André Luis Pereira de

2018-01-01

Hypersensitivity pneumonitis (HP) is a disease with variable clinical presentation in which inflammation in the lung parenchyma is caused by the inhalation of specific organic antigens or low molecular weight substances in genetically susceptible individuals. Alterations of the acute, subacute and chronic forms may eventually overlap, and the diagnosis based on temporality and presence of fibrosis (acute/inflammatory HP vs. chronic HP) seems to be more feasible and useful in clinical practice. Differential diagnosis of chronic HP with other interstitial fibrotic diseases is challenging due to the overlap of the clinical history, and the functional and imaging findings of these pathologies in the terminal stages. Areas covered: This article reviews the essential features of HP with emphasis on imaging features. Moreover, the main methodological limitations of high-resolution computed tomography (HRCT) interpretation are discussed, as well as new perspectives with volumetric quantitative CT analysis as a useful tool for retrieving detailed and accurate information from the lung parenchyma. Expert commentary: Mosaic attenuation is a prominent feature of this disease, but air trapping in chronic HP seems overestimated. Quantitative analysis has the potential to estimate the involvement of the pulmonary parenchyma more accurately and could correlate better with pulmonary function results.

a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

NASA Astrophysics Data System (ADS)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

Correlative feature analysis on FFDM

PubMed Central

Yuan, Yading; Giger, Maryellen L.; Li, Hui; Sennett, Charlene

2008-01-01

Identifying the corresponding images of a lesion in different views is an essential step in improving the diagnostic ability of both radiologists and computer-aided diagnosis (CAD) systems. Because of the nonrigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this pilot study, we present a computerized framework that differentiates between corresponding images of the same lesion in different views and noncorresponding images, i.e., images of different lesions. A dual-stage segmentation method, which employs an initial radial gradient index (RGI) based segmentation and an active contour model, is applied to extract mass lesions from the surrounding parenchyma. Then various lesion features are automatically extracted from each of the two views of each lesion to quantify the characteristics of density, size, texture and the neighborhood of the lesion, as well as its distance to the nipple. A two-step scheme is employed to estimate the probability that the two lesion images from different mammographic views are of the same physical lesion. In the first step, a correspondence metric for each pairwise feature is estimated by a Bayesian artificial neural network (BANN). Then, these pairwise correspondence metrics are combined using another BANN to yield an overall probability of correspondence. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the individual features and the selected feature subset in the task of distinguishing corresponding pairs from noncorresponding pairs. Using a FFDM database with 123 corresponding image pairs and 82 noncorresponding pairs, the distance feature yielded an area under the ROC curve (AUC) of 0.81±0.02 with leave-one-out (by physical lesion) evaluation, and the feature metric subset, which included distance, gradient texture, and ROI-based correlation, yielded an AUC of 0.87±0.02. The improvement by using multiple feature metrics was statistically significant compared to single feature performance. PMID:19175108

TU-CD-BRB-10: 18F-FDG PET Image-Derived Tumor Features Highlight Altered Pathways Identified by Trancriptomic Analysis in Head and Neck Cancer

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

Tixier, F; INSERM UMR1101 LaTIM, Brest; Cheze-Le-Rest, C

2015-06-15

Purpose: Several quantitative features can be extracted from 18F-FDG PET images, such as standardized uptake values (SUVs), metabolic tumor volume (MTV), shape characterization (SC) or intra-tumor radiotracer heterogeneity quantification (HQ). Some of these features calculated from baseline 18F-FDG PET images have shown a prognostic and predictive clinical value. It has been hypothesized that these features highlight underlying tumor patho-physiological processes at smaller scales. The objective of this study was to investigate the ability of recovering alterations of signaling pathways from FDG PET image-derived features. Methods: 52 patients were prospectively recruited from two medical centers (Brest and Poitiers). All patients underwentmore » an FDG PET scan for staging and biopsies of both healthy and primary tumor tissues. Biopsies went through a transcriptomic analysis performed in four spates on 4×44k chips (Agilent™). Primary tumors were delineated in the PET images using the Fuzzy Locally Adaptive Bayesian algorithm and characterized using 10 features including SUVs, SC and HQ. A module network algorithm followed by functional annotation was exploited in order to link PET features with signaling pathways alterations. Results: Several PET-derived features were found to discriminate differentially expressed genes between tumor and healthy tissue (fold-change >2, p<0.01) into 30 co-regulated groups (p<0.05). Functional annotations applied to these groups of genes highlighted associations with well-known pathways involved in cancer processes, such as cell proliferation and apoptosis, as well as with more specific ones such as unsaturated fatty acids. Conclusion: Quantitative features extracted from baseline 18F-FDG PET images usually exploited only for diagnosis and staging, were identified in this work as being related to specific altered pathways and may show promise as tools for personalizing treatment decisions.« less

Edge detection of optical subaperture image based on improved differential box-counting method

NASA Astrophysics Data System (ADS)

Li, Yi; Hui, Mei; Liu, Ming; Dong, Liquan; Kong, Lingqin; Zhao, Yuejin

2018-01-01

Optical synthetic aperture imaging technology is an effective approach to improve imaging resolution. Compared with monolithic mirror system, the image of optical synthetic aperture system is often more complex at the edge, and as a result of the existence of gap between segments, which makes stitching becomes a difficult problem. So it is necessary to extract the edge of subaperture image for achieving effective stitching. Fractal dimension as a measure feature can describe image surface texture characteristics, which provides a new approach for edge detection. In our research, an improved differential box-counting method is used to calculate fractal dimension of image, then the obtained fractal dimension is mapped to grayscale image to detect edges. Compared with original differential box-counting method, this method has two improvements as follows: by modifying the box-counting mechanism, a box with a fixed height is replaced by a box with adaptive height, which solves the problem of over-counting the number of boxes covering image intensity surface; an image reconstruction method based on super-resolution convolutional neural network is used to enlarge small size image, which can solve the problem that fractal dimension can't be calculated accurately under the small size image, and this method may well maintain scale invariability of fractal dimension. The experimental results show that the proposed algorithm can effectively eliminate noise and has a lower false detection rate compared with the traditional edge detection algorithms. In addition, this algorithm can maintain the integrity and continuity of image edge in the case of retaining important edge information.

Combining Diffusion Tensor Metrics and DSC Perfusion Imaging: Can It Improve the Diagnostic Accuracy in Differentiating Tumefactive Demyelination from High-Grade Glioma?

PubMed

Hiremath, S B; Muraleedharan, A; Kumar, S; Nagesh, C; Kesavadas, C; Abraham, M; Kapilamoorthy, T R; Thomas, B

2017-04-01

Tumefactive demyelinating lesions with atypical features can mimic high-grade gliomas on conventional imaging sequences. The aim of this study was to assess the role of conventional imaging, DTI metrics ( p:q tensor decomposition), and DSC perfusion in differentiating tumefactive demyelinating lesions and high-grade gliomas. Fourteen patients with tumefactive demyelinating lesions and 21 patients with high-grade gliomas underwent brain MR imaging with conventional, DTI, and DSC perfusion imaging. Imaging sequences were assessed for differentiation of the lesions. DTI metrics in the enhancing areas and perilesional hyperintensity were obtained by ROI analysis, and the relative CBV values in enhancing areas were calculated on DSC perfusion imaging. Conventional imaging sequences had a sensitivity of 80.9% and specificity of 57.1% in differentiating high-grade gliomas ( P = .049) from tumefactive demyelinating lesions. DTI metrics ( p : q tensor decomposition) and DSC perfusion demonstrated a statistically significant difference in the mean values of ADC, the isotropic component of the diffusion tensor, the anisotropic component of the diffusion tensor, the total magnitude of the diffusion tensor, and rCBV among enhancing portions in tumefactive demyelinating lesions and high-grade gliomas ( P ≤ .02), with the highest specificity for ADC, the anisotropic component of the diffusion tensor, and relative CBV (92.9%). Mean fractional anisotropy values showed no significant statistical difference between tumefactive demyelinating lesions and high-grade gliomas. The combination of DTI and DSC parameters improved the diagnostic accuracy (area under the curve = 0.901). Addition of a heterogeneous enhancement pattern to DTI and DSC parameters improved it further (area under the curve = 0.966). The sensitivity increased from 71.4% to 85.7% after the addition of the enhancement pattern. DTI and DSC perfusion add profoundly to conventional imaging in differentiating tumefactive demyelinating lesions and high-grade gliomas. The combination of DTI metrics and DSC perfusion markedly improved diagnostic accuracy. © 2017 by American Journal of Neuroradiology.

Multimodality imaging of ovarian cystic lesions: Review with an imaging based algorithmic approach

PubMed Central

Wasnik, Ashish P; Menias, Christine O; Platt, Joel F; Lalchandani, Usha R; Bedi, Deepak G; Elsayes, Khaled M

2013-01-01

Ovarian cystic masses include a spectrum of benign, borderline and high grade malignant neoplasms. Imaging plays a crucial role in characterization and pretreatment planning of incidentally detected or suspected adnexal masses, as diagnosis of ovarian malignancy at an early stage is correlated with a better prognosis. Knowledge of differential diagnosis, imaging features, management trends and an algorithmic approach of such lesions is important for optimal clinical management. This article illustrates a multi-modality approach in the diagnosis of a spectrum of ovarian cystic masses and also proposes an algorithmic approach for the diagnosis of these lesions. PMID:23671748

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Quantitative label-free multimodality nonlinear optical imaging for in situ differentiation of cancerous lesions

NASA Astrophysics Data System (ADS)

Xu, Xiaoyun; Li, Xiaoyan; Cheng, Jie; Liu, Zhengfan; Thrall, Michael J.; Wang, Xi; Wang, Zhiyong; Wong, Stephen T. C.

2013-03-01

The development of real-time, label-free imaging techniques has recently attracted research interest for in situ differentiation of cancerous lesions from normal tissues. Molecule-specific intrinsic contrast can arise from label-free imaging techniques such as Coherent Anti-Stokes Raman Scattering (CARS), Two-Photon Excited AutoFluorescence (TPEAF), and Second Harmonic Generation (SHG), which, in combination, would hold the promise of a powerful label-free tool for cancer diagnosis. Among cancer-related deaths, lung carcinoma is the leading cause for both sexes. Although early treatment can increase the survival rate dramatically, lesion detection and precise diagnosis at an early stage is unusual due to its asymptomatic nature and limitations of current diagnostic techniques that make screening difficult. We investigated the potential of using multimodality nonlinear optical microscopy that incorporates CARS, TPEAF, and SHG techniques for differentiation of lung cancer from normal tissue. Cancerous and non-cancerous lung tissue samples from patients were imaged using CARS, TPEAF, and SHG techniques for comparison. These images showed good pathology correlation with hematoxylin and eosin (H and E) stained sections from the same tissue samples. Ongoing work includes imaging at various penetration depths to show three-dimensional morphologies of tumor cell nuclei using CARS, elastin using TPEAF, and collagen using SHG and developing classification algorithms for quantitative feature extraction to enable lung cancer diagnosis. Our results indicate that via real-time morphology analyses, a multimodality nonlinear optical imaging platform potentially offers a powerful minimally-invasive way to differentiate cancer lesions from surrounding non-tumor tissues in vivo for clinical applications.

Progressive massive fibrosis in patients with pneumoconiosis: utility of MRI in differentiating from lung cancer.

PubMed

Ogihara, Yukihiro; Ashizawa, Kazuto; Hayashi, Hideyuki; Nagayasu, Takeshi; Hayashi, Tomayoshi; Honda, Sumihisa; Uetani, Masataka

2018-01-01

Background It is occasionally difficult to distinguish progressive massive fibrosis (PMF) from lung cancer on computed tomography (CT) in patients with pneumoconiosis. Purpose To evaluate the magnetic resonance imaging (MRI) features of PMF and to assess its ability to differentiate PMF from lung cancer. Material and Methods Between 2000 and 2014, 40 pulmonary lesions suspected to be lung cancer on the basis of CT in 28 patients with known pneumoconiosis were evaluated. Twenty-four of the 40 lesions were pathologically or clinically diagnosed as PMF. The signal pattern on T2-weighted (T2W) images, post-contrast enhancement pattern on T1-weighted (T1W) images, and the pattern of the time intensity curve (TIC) on contrast-enhanced dynamic studies were evaluated. All images were analyzed independently by two chest radiologists. Results All 24 PMF lesions showed low signal intensity (SI) on T2W images (sensitivity, 100%), while 15 of 16 lung cancer lesions showed intermediate or high SI on T2W images (specificity, 94%) when PMF was regarded as a positive result. Six of 17 PMF lesions showed a homogeneous enhancement pattern (sensitivity, 35%), and 4/9 lung cancer lesions showed an inhomogeneous or a ring-like enhancement pattern (specificity, 44%). Six of 16 PMF lesions showed a gradually increasing enhancement pattern (sensitivity, 38%), and 7/9 lung cancer lesions showed rapid enhancement pattern (specificity, 78%). Conclusion When differentiation between PMF and lung cancer in patients with pneumoconiosis is difficult on CT, an additional MRI study, particularly the T2W imaging sequence, may help differentiate between the two.

Thanatophoric Dwarfism

PubMed Central

Shah, K.; Astley, R.; Cameron, A. H.

1973-01-01

A review of the radiographs of children previously classified as achondroplasiacs revealed six thanatophoric dwarfs. The main radiological differentiating features were the greater degree of shortening of the long bones, including the fibula, the curvature of the femora, the very small size of the thorax and, particularly, the very narrow ossified elements of the vertebral bodies. Perhaps the most important aspect of differential diagnosis lies in recognition in utero. The reported association with clover-leaf deformity of the skull in sibs provides the strongest evidence for genetic differentiation from classical achondroplasia. More evidence might be obtained by a widespread search through hospital radiological museums. Images PMID:4204337

On the Application of Pattern Recognition and AI Technique to the Cytoscreening of Vaginal Smears by Computer

NASA Astrophysics Data System (ADS)

Bow, Sing T.; Wang, Xia-Fang

1989-05-01

In this paper the concepts of pattern recognition, image processing and artificial intelligence are applied to the development of an intelligent cytoscreening system to differentiate the abnormal cytological objects from the normal ones in vaginal smears. To achieve this goal,work listed below are involved: 1. Enhancement of the microscopic images of the smears; 2. Elevation of the qualitative differentiation under the microscope by cytologists to a quantitative differentiation plateau on the epithelial cells, ciliated cells, vacuolated cells, foreign-body-giant cells, plasma cells, lymph cells, white blood cells, red blood cells, etc. These knowledges are to be inputted into our intelligent cyto-screening system to ameliorate machine differentiation; 3. Selection of a set of effective features to characterize the cytological objects onto various regions of the multiclustered by computer algorithms; and 4. Systematical summarization of the knowledge that a gynecologist has and the way he/she follows when dealing with a case.

Tissue Feature-Based and Segmented Deformable Image Registration for Improved Modeling of the Shear Movement of the Lungs

PubMed Central

Xie, Yaoqin; Chao, Ming; Xing, Lei

2009-01-01

Purpose To report a tissue feature-based image registration strategy with explicit inclusion of the differential motions of thoracic structures. Methods and Materials The proposed technique started with auto-identification of a number of corresponding points with distinct tissue features. The tissue feature points were found by using the scale-invariant feature transform (SIFT) method. The control point pairs were then sorted into different “colors” according to the organs they reside and used to model the involved organs individually. A thin-plate spline (TPS) method was used to register a structure characterized by the control points with a given “color”. The proposed technique was applied to study a digital phantom case, three lung and three liver cancer patients. Results For the phantom case, a comparison with the conventional TPS method showed that the registration accuracy was markedly improved when the differential motions of the lung and chest wall were taken into account. On average, the registration error and the standard deviation (SD) of the 15 points against the known ground truth are reduced from 3.0 mm to 0.5 mm and from 1.5 mm to 0.2 mm, respectively, when the new method was used. Similar level of improvement was achieved for the clinical cases. Conclusions The segmented deformable approach provides a natural and logical solution to model the discontinuous organ motions and greatly improves the accuracy and robustness of deformable registration. PMID:19545792

SAPHO: What radiologists should know.

PubMed

Depasquale, R; Kumar, N; Lalam, R K; Tins, B J; Tyrrell, P N M; Singh, J; Cassar-Pullicino, V N

2012-03-01

SAPHO (synovitis, acne, pustulosis, hyperostosis, and osteitis) is an umbrella acronym for inflammatory clinical conditions whose common denominator is aseptic osteoarticular involvement with characteristic skin lesions. It involves all ages, can involve any skeletal site, and has variable imaging appearances depending on the stage/age of the lesion and imaging method. It mimics important differentials including infection and neoplasia. Awareness of the imaging features, especially in the spine, facilitates early diagnosis, prevents repeated biopsies, and avoids unnecessary surgery, while initiating appropriate treatment. Copyright © 2011 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Uterine leiomyomas: histopathologic features, MR imaging findings, differential diagnosis, and treatment.

PubMed

Murase, E; Siegelman, E S; Outwater, E K; Perez-Jaffe, L A; Tureck, R W

1999-01-01

Leiomyomas are the most common uterine neoplasm and are composed of smooth muscle with varying amounts of fibrous connective tissue. As leiomyomas enlarge, they may outgrow their blood supply, resulting in various types of degeneration: hyaline or myxoid degeneration, calcification, cystic degeneration, and red degeneration. Leiomyomas are classified as submucosal, intramural, or subserosal; the latter may become pedunculated and simulate ovarian neoplasms. Although most leiomyomas are asymptomatic, patients may present with abnormal uterine bleeding, pressure on adjacent organs, pain, infertility, or a palpable abdominalpelvic mass. Magnetic resonance (MR) imaging is the most accurate imaging technique for detection and localization of leiomyomas. On T2-weighted images, nondegenerated leiomyomas appear as well-circumscribed masses of decreased signal intensity; however, cellular leiomyomas can have relatively higher signal intensity on T2-weighted images and demonstrate enhancement on contrast material-enhanced images. Degenerated leiomyomas have variable appearances on T2-weighted images and contrast-enhanced images. The differential diagnosis of leiomyomas includes adenomyosis, solid adnexal mass, focal myometrial contraction, and uterine leiomyosarcoma. For patients with symptoms, medical or surgical treatment may be indicated. MR imaging also has a role in treatment of leiomyomas by assisting in surgical planning and monitoring the response to medical therapy.

Small angle x-ray scattering with edge-illumination

NASA Astrophysics Data System (ADS)

Modregger, Peter; Cremona, Tiziana P.; Benarafa, Charaf; Schittny, Johannes C.; Olivo, Alessandro; Endrizzi, Marco

2016-08-01

Sensitivity to sub-pixel sample features has been demonstrated as a valuable capability of phase contrast x-ray imaging. Here, we report on a method to obtain angular-resolved small angle x-ray scattering distributions with edge-illumination- based imaging utilizing incoherent illumination from an x-ray tube. Our approach provides both the three established image modalities (absorption, differential phase and scatter strength), plus a number of additional contrasts related to unresolved sample features. The complementarity of these contrasts is experimentally validated by using different materials in powder form. As a significant application example we show that the extended complementary contrasts could allow the diagnosis of pulmonary emphysema in a murine model. In support of this, we demonstrate that the properties of the retrieved scattering distributions are consistent with the expectation of increased feature sizes related to pulmonary emphysema. Combined with the simplicity of implementation of edge-illumination, these findings suggest a high potential for exploiting extended sub-pixel contrasts in the diagnosis of lung diseases and beyond.

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening

NASA Astrophysics Data System (ADS)

Tu, Shu-Ju; Wang, Chih-Wei; Pan, Kuang-Tse; Wu, Yi-Cheng; Wu, Chen-Te

2018-03-01

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p  =  0.002 518), sigma (p  =  0.002 781), uniformity (p  =  0.032 41), and entropy (p  =  0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining truly malignant nodules and therefore reduce problems in lung cancer screening.

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening.

PubMed

Tu, Shu-Ju; Wang, Chih-Wei; Pan, Kuang-Tse; Wu, Yi-Cheng; Wu, Chen-Te

2018-03-14

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p  =  0.002 518), sigma (p  =  0.002 781), uniformity (p  =  0.032 41), and entropy (p  =  0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining truly malignant nodules and therefore reduce problems in lung cancer screening.

Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness

NASA Astrophysics Data System (ADS)

Vignati, A.; Mazzetti, S.; Giannini, V.; Russo, F.; Bollito, E.; Porpiglia, F.; Stasi, M.; Regge, D.

2015-04-01

To explore contrast (C) and homogeneity (H) gray-level co-occurrence matrix texture features on T2-weighted (T2w) Magnetic Resonance (MR) images and apparent diffusion coefficient (ADC) maps for predicting prostate cancer (PCa) aggressiveness, and to compare them with traditional ADC metrics for differentiating low- from intermediate/high-grade PCas. The local Ethics Committee approved this prospective study of 93 patients (median age, 65 years), who underwent 1.5 T multiparametric endorectal MR imaging before prostatectomy. Clinically significant (volume ≥0.5 ml) peripheral tumours were outlined on histological sections, contoured on T2w and ADC images, and their pathological Gleason Score (pGS) was recorded. C, H, and traditional ADC metrics (mean, median, 10th and 25th percentile) were calculated on the largest lesion slice, and correlated with the pGS through the Spearman correlation coefficient. The area under the receiver operating characteristic curve (AUC) assessed how parameters differentiate pGS = 6 from pGS ≥ 7. The dataset included 49 clinically significant PCas with a balanced distribution of pGS. The Spearman ρ and AUC values on ADC were: -0.489, 0.823 (mean) -0.522, 0.821 (median) -0.569, 0.854 (10th percentile) -0.556, 0.854 (25th percentile) -0.386, 0.871 (C); 0.533, 0.923 (H); while on T2w they were: -0.654, 0.945 (C); 0.645, 0.962 (H). AUC of H on ADC and T2w, and C on T2w were significantly higher than that of the mean ADC (p = 0.05). H and C calculated on T2w images outperform ADC parameters in correlating with pGS and differentiating low- from intermediate/high-risk PCas, supporting the role of T2w MR imaging in assessing PCa biological aggressiveness.

Soft-Tissue Sarcomas of the Abdomen and Pelvis: Radiologic-Pathologic Features, Part 1-Common Sarcomas: From the Radiologic Pathology Archives.

PubMed

Levy, Angela D; Manning, Maria A; Al-Refaie, Waddah B; Miettinen, Markku M

2017-01-01

Soft-tissue sarcomas are a diverse group of rare mesenchymal malignancies that can arise at any location in the body and affect all age groups. These sarcomas are most common in the extremities, trunk wall, retroperitoneum, and head and neck. In the adult population, soft-tissue sarcomas arising in the abdomen and pelvis are often large masses at the time of diagnosis because they are usually clinically silent or cause vague or mild symptoms until they invade or compress vital organs. In contrast, soft-tissue sarcomas arising from the abdominal wall come to clinical attention earlier in the course of disease because they cause a palpable mass, abdominal wall deformity, or pain that is more clinically apparent. The imaging features of abdominal and pelvic sarcomas and abdominal wall sarcomas can be nonspecific and overlap with more common pathologic conditions, making diagnosis difficult or, in some cases, delaying diagnosis. Liposarcoma (well-differentiated and dedifferentiated liposarcomas), leiomyosarcoma, and gastrointestinal stromal tumor (GIST) are the most common intra-abdominal primary sarcomas. Any soft-tissue sarcoma can arise in the abdominal wall. Knowledge of the classification and pathologic features of soft-tissue sarcomas, the anatomic locations where they occur, and their cross-sectional imaging features helps the radiologist establish the diagnosis or differential diagnosis so that patients with soft-tissue sarcomas can receive optimal treatment and management. In part 1 of this article, the most common soft-tissue sarcomas (liposarcoma, leiomyosarcoma, and GIST) are reviewed, with a discussion on anatomic locations, classification, clinical considerations, and differential diagnosis. Part 2 will focus on the remainder of the soft-tissue sarcomas occurring in the abdomen and pelvis.

Soft-Tissue Sarcomas of the Abdomen and Pelvis: Radiologic-Pathologic Features, Part 1—Common Sarcomas: From the Radiologic Pathology Archives

PubMed Central

Manning, Maria A.; Al-Refaie, Waddah B.; Miettinen, Markku M.

2017-01-01

Soft-tissue sarcomas are a diverse group of rare mesenchymal malignancies that can arise at any location in the body and affect all age groups. These sarcomas are most common in the extremities, trunk wall, retroperitoneum, and head and neck. In the adult population, soft-tissue sarcomas arising in the abdomen and pelvis are often large masses at the time of diagnosis because they are usually clinically silent or cause vague or mild symptoms until they invade or compress vital organs. In contrast, soft-tissue sarcomas arising from the abdominal wall come to clinical attention earlier in the course of disease because they cause a palpable mass, abdominal wall deformity, or pain that is more clinically apparent. The imaging features of abdominal and pelvic sarcomas and abdominal wall sarcomas can be nonspecific and overlap with more common pathologic conditions, making diagnosis difficult or, in some cases, delaying diagnosis. Liposarcoma (well-differentiated and dedifferentiated liposarcomas), leiomyosarcoma, and gastrointestinal stromal tumor (GIST) are the most common intra-abdominal primary sarcomas. Any soft-tissue sarcoma can arise in the abdominal wall. Knowledge of the classification and pathologic features of soft-tissue sarcomas, the anatomic locations where they occur, and their cross-sectional imaging features helps the radiologist establish the diagnosis or differential diagnosis so that patients with soft-tissue sarcomas can receive optimal treatment and management. In part 1 of this article, the most common soft-tissue sarcomas (liposarcoma, leiomyosarcoma, and GIST) are reviewed, with a discussion on anatomic locations, classification, clinical considerations, and differential diagnosis. Part 2 will focus on the remainder of the soft-tissue sarcomas occurring in the abdomen and pelvis. PMID:28287938

Diagnosing lung cancer using coherent anti-Stokes Raman scattering microscopy

NASA Astrophysics Data System (ADS)

Gao, Liang; Yang, Yaliang; Xing, Jiong; Thrall, Michael J.; Wang, Zhiyong; Li, Fuhai; Luo, Pengfei; Wong, Kelvin K.; Zhao, Hong; Wong, Stephen T. C.

2011-03-01

Lung carcinoma is the most prevalent type of cancer in the world, and it is responsible for more deaths than other types of cancer. During diagnosis, a pathologist primarily aims to differentiate small cell carcinoma from non-small cell carcinoma on biopsy and cytology specimens, which is time consuming due to the time required for tissue processing and staining. To speed up the diagnostic process, we investigated the feasibility of using coherent anti-Stokes Raman scattering (CARS) microscopy as a label-free strategy to image lung lesions and differentiate subtypes of lung cancers. Different mouse lung cancer models were developed by injecting human lung cancer cell lines, including adenocarcinoma, squamous cell carcinoma, and small cell carcinoma, into lungs of the nude mice. CARS images were acquired from normal lung tissues and different subtypes of cancer lesions ex vivo using intrinsic contrasts from symmetric CH2 bonds. These images showed good correlation with the hematoxylin and eosin (H&E) stained sections from the same tissue samples with regard to cell size, density, and cell-cell distance. These features are routinely used in diagnosing lung lesions. Our results showed that the CARS technique is capable of providing a visualizable platform to differentiate different kinds of lung cancers using the same pathological features without histological staining and thus has the potential to serve as a more efficient examination tool for diagnostic pathology. In addition, incorporating with suitable fiber-optic probes would render the CARS technique as a promising approach for in vivo diagnosis of lung cancer.

Diagnosing aneurysmal and unicameral bone cysts with magnetic resonance imaging.

PubMed

Sullivan, R J; Meyer, J S; Dormans, J P; Davidson, R S

1999-09-01

The differential between aneurysmal bone cysts and unicameral bone cysts usually is clear clinically and radiographically. Occasionally there are cases in which the diagnosis is not clear. Because natural history and treatment are different, the ability to distinguish between these two entities before surgery is important. The authors reviewed, in a blinded fashion, the preoperative magnetic resonance images to investigate criteria that could be used to differentiate between the two lesions. All patients had operative or pathologic confirmation of an aneurysmal bone cyst or unicameral bone cyst. The authors analyzed the preoperative magnetic resonance images of 14 patients with diagnostically difficult bone cysts (eight children with unicameral bone cysts and six children with aneurysmal bone cysts) and correlated these findings with diagnosis after biopsy or cyst aspiration and contrast injection. The presence of a double density fluid level within the lesion strongly indicated that the lesion was an aneurysmal bone cyst, rather than a unicameral bone cyst. Other criteria that suggested the lesion was an aneurysmal bone cyst were the presence of septations within the lesion and signal characteristics of low intensity on T1 images and high intensity on T2 images. The authors identified a way of helping to differentiate between aneurysmal bone cysts and unicameral bone cysts on magnetic resonance images. Double density fluid level, septation, and low signal on T1 images and high signal on T2 images strongly suggest the bone cyst in question is an aneurysmal bone cyst, rather than a unicameral bone cyst. This may be helpful before surgery for the child who has a cystic lesion for which radiographic features do not allow a clear differentiation of unicameral bone cyst from aneurysmal bone cyst.

Optimal Non-Invasive Fault Classification Model for Packaged Ceramic Tile Quality Monitoring Using MMW Imaging

NASA Astrophysics Data System (ADS)

Agarwal, Smriti; Singh, Dharmendra

2016-04-01

Millimeter wave (MMW) frequency has emerged as an efficient tool for different stand-off imaging applications. In this paper, we have dealt with a novel MMW imaging application, i.e., non-invasive packaged goods quality estimation for industrial quality monitoring applications. An active MMW imaging radar operating at 60 GHz has been ingeniously designed for concealed fault estimation. Ceramic tiles covered with commonly used packaging cardboard were used as concealed targets for undercover fault classification. A comparison of computer vision-based state-of-the-art feature extraction techniques, viz, discrete Fourier transform (DFT), wavelet transform (WT), principal component analysis (PCA), gray level co-occurrence texture (GLCM), and histogram of oriented gradient (HOG) has been done with respect to their efficient and differentiable feature vector generation capability for undercover target fault classification. An extensive number of experiments were performed with different ceramic tile fault configurations, viz., vertical crack, horizontal crack, random crack, diagonal crack along with the non-faulty tiles. Further, an independent algorithm validation was done demonstrating classification accuracy: 80, 86.67, 73.33, and 93.33 % for DFT, WT, PCA, GLCM, and HOG feature-based artificial neural network (ANN) classifier models, respectively. Classification results show good capability for HOG feature extraction technique towards non-destructive quality inspection with appreciably low false alarm as compared to other techniques. Thereby, a robust and optimal image feature-based neural network classification model has been proposed for non-invasive, automatic fault monitoring for a financially and commercially competent industrial growth.

Personal and Impersonal Stimuli Differentially Engage Brain Networks during Moral Reasoning

ERIC Educational Resources Information Center

Xue, Shao-Wei; Wang, Yan; Tang, Yi-Yuan

2013-01-01

Moral decision making has recently attracted considerable attention as a core feature of all human endeavors. Previous functional magnetic resonance imaging studies about moral judgment have identified brain areas associated with cognitive or emotional engagement. Here, we applied graph theory-based network analysis of event-related potentials…

First images of asteroid 243 Ida

USGS Publications Warehouse

Belton, M.J.S.; Chapman, C.R.; Veverka, J.; Klaasen, K.P.; Harch, A.; Greeley, R.; Greenberg, R.; Head, J. W.; McEwen, A.; Morrison, D.; Thomas, P.C.; Davies, M.E.; Carr, M.H.; Neukum, G.; Fanale, F.P.; Davis, D.R.; Anger, C.; Gierasch, P.J.; Ingersoll, A.P.; Pilcher, C.B.

1994-01-01

The first images of the asteroid 243 Ida from Galileo show an irregular object measuring 56 kilometers by 24 kilometers by 21 kilometers. Its surface is rich in geologic features, including systems of grooves, blocks, chutes, albedo features, crater chains, and a full range of crater morphologies. The largest blocks may be distributed nonuniformly across the surface; lineaments and dark-floored craters also have preferential locations. Ida is interpreted to have a substantial regolith. The high crater density and size-frequency distribution (-3 differential power-law index) indicate a surface in equilibrium with saturated cratering. A minimum model crater age for Ida - and therefore for the Koronis family to which Ida belongs - is estimated at 1 billion years, older than expected.

Can Abdominal Computed Tomography Imaging Help Accurately Identify a Dedifferentiated Component in a Well-Differentiated Liposarcoma?

PubMed

Bhosale, Priya; Wang, Jieqi; Varma, Datla; Jensen, Corey; Patnana, Madhavi; Wei, Wei; Chauhan, Anil; Feig, Barry; Patel, Shreyaskumar; Somaiah, Neeta; Sagebiel, Tara

To assess the ability of computed tomography (CT) to differentiate an atypical lipomatous tumor/well-differentiated liposarcoma (WDLPS) from a WDLPS with a dedifferentiated component (DDLPS) within it. Forty-nine untreated patients with abdominal atypical lipomatous tumors/well-differentiated liposarcomas who had undergone contrast-enhanced CT were identified using an institutional database. Three radiologists who were blinded to the pathology findings evaluated all the images independently to determine whether a dedifferentiated component was present within the WDLPS. The CT images were evaluated for fat content (≤25% or >25%); presence of ground-glass density, enhancing and/or necrotic nodules; presence of a capsule surrounding the mass; septations; and presence and pattern of calcifications. A multivariate logistic regression model with generalized estimating equations was used to correlate imaging features with pathology findings. Kappa statistics were calculated to assess agreement between the three radiologists. On the basis of pathological findings, 12 patients had been diagnosed with DDLPS within a WDLPS and 37 had been diagnosed with WDLPS. The presence of an enhancing or a centrally necrotic nodule within the atypical lipomatous tumor was associated with dedifferentiated liposarcoma (P = 0.02 and P = 0.0003, respectively). The three readers showed almost perfect agreement in overall diagnosis (κ r = 0.83; 95% confidence interval, 0.67-0.99). An enhancing or centrally necrotic nodule may be indicative of a dedifferentiated component in well-differentiated liposarcoma. Ground-glass density nodules may not be indicative of dedifferentiation.

Scalable topographies to support proliferation and Oct4 expression by human induced pluripotent stem cells

PubMed Central

Reimer, Andreas; Vasilevich, Aliaksei; Hulshof, Frits; Viswanathan, Priyalakshmi; van Blitterswijk, Clemens A.; de Boer, Jan; Watt, Fiona M.

2016-01-01

It is well established that topographical features modulate cell behaviour, including cell morphology, proliferation and differentiation. To define the effects of topography on human induced pluripotent stem cells (iPSC), we plated cells on a topographical library containing over 1000 different features in medium lacking animal products (xeno-free). Using high content imaging, we determined the effect of each topography on cell proliferation and expression of the pluripotency marker Oct4 24 h after seeding. Features that maintained Oct4 expression also supported proliferation and cell-cell adhesion at 24 h, and by 4 days colonies of Oct4-positive, Sox2-positive cells had formed. Computational analysis revealed that small feature size was the most important determinant of pluripotency, followed by high wave number and high feature density. Using this information we correctly predicted whether any given topography within our library would support the pluripotent state at 24 h. This approach not only facilitates the design of substrates for optimal human iPSC expansion, but also, potentially, identification of topographies with other desirable characteristics, such as promoting differentiation. PMID:26757610

Scalable topographies to support proliferation and Oct4 expression by human induced pluripotent stem cells.

PubMed

Reimer, Andreas; Vasilevich, Aliaksei; Hulshof, Frits; Viswanathan, Priyalakshmi; van Blitterswijk, Clemens A; de Boer, Jan; Watt, Fiona M

2016-01-13

It is well established that topographical features modulate cell behaviour, including cell morphology, proliferation and differentiation. To define the effects of topography on human induced pluripotent stem cells (iPSC), we plated cells on a topographical library containing over 1000 different features in medium lacking animal products (xeno-free). Using high content imaging, we determined the effect of each topography on cell proliferation and expression of the pluripotency marker Oct4 24 h after seeding. Features that maintained Oct4 expression also supported proliferation and cell-cell adhesion at 24 h, and by 4 days colonies of Oct4-positive, Sox2-positive cells had formed. Computational analysis revealed that small feature size was the most important determinant of pluripotency, followed by high wave number and high feature density. Using this information we correctly predicted whether any given topography within our library would support the pluripotent state at 24 h. This approach not only facilitates the design of substrates for optimal human iPSC expansion, but also, potentially, identification of topographies with other desirable characteristics, such as promoting differentiation.

A new method for fusion, denoising and enhancement of x-ray images retrieved from Talbot-Lau grating interferometry.

PubMed

Scholkmann, Felix; Revol, Vincent; Kaufmann, Rolf; Baronowski, Heidrun; Kottler, Christian

2014-03-21

This paper introduces a new image denoising, fusion and enhancement framework for combining and optimal visualization of x-ray attenuation contrast (AC), differential phase contrast (DPC) and dark-field contrast (DFC) images retrieved from x-ray Talbot-Lau grating interferometry. The new image fusion framework comprises three steps: (i) denoising each input image (AC, DPC and DFC) through adaptive Wiener filtering, (ii) performing a two-step image fusion process based on the shift-invariant wavelet transform, i.e. first fusing the AC with the DPC image and then fusing the resulting image with the DFC image, and finally (iii) enhancing the fused image to obtain a final image using adaptive histogram equalization, adaptive sharpening and contrast optimization. Application examples are presented for two biological objects (a human tooth and a cherry) and the proposed method is compared to two recently published AC/DPC/DFC image processing techniques. In conclusion, the new framework for the processing of AC, DPC and DFC allows the most relevant features of all three images to be combined in one image while reducing the noise and enhancing adaptively the relevant image features. The newly developed framework may be used in technical and medical applications.

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.

Classification of optical coherence tomography images for diagnosing different ocular diseases

NASA Astrophysics Data System (ADS)

Gholami, Peyman; Sheikh Hassani, Mohsen; Kuppuswamy Parthasarathy, Mohana; Zelek, John S.; Lakshminarayanan, Vasudevan

2018-03-01

Optical Coherence tomography (OCT) images provide several indicators, e.g., the shape and the thickness of different retinal layers, which can be used for various clinical and non-clinical purposes. We propose an automated classification method to identify different ocular diseases, based on the local binary pattern features. The database consists of normal and diseased human eye SD-OCT images. We use a multiphase approach for building our classifier, including preprocessing, Meta learning, and active learning. Pre-processing is applied to the data to handle missing features from images and replace them with the mean or median of the corresponding feature. All the features are run through a Correlation-based Feature Subset Selection algorithm to detect the most informative features and omit the less informative ones. A Meta learning approach is applied to the data, in which a SVM and random forest are combined to obtain a more robust classifier. Active learning is also applied to strengthen our classifier around the decision boundary. The primary experimental results indicate that our method is able to differentiate between the normal and non-normal retina with an area under the ROC curve (AUC) of 98.6% and also to diagnose the three common retina-related diseases, i.e., Age-related Macular Degeneration, Diabetic Retinopathy, and Macular Hole, with an AUC of 100%, 95% and 83.8% respectively. These results indicate a better performance of the proposed method compared to most of the previous works in the literature.

Digital image processing of nanometer-size metal particles on amorphous substrates

NASA Technical Reports Server (NTRS)

Soria, F.; Artal, P.; Bescos, J.; Heinemann, K.

1989-01-01

The task of differentiating very small metal aggregates supported on amorphous films from the phase contrast image features inherently stemming from the support is extremely difficult in the nanometer particle size range. Digital image processing was employed to overcome some of the ambiguities in evaluating such micrographs. It was demonstrated that such processing allowed positive particle detection and a limited degree of statistical size analysis even for micrographs where by bare eye examination the distribution between particles and erroneous substrate features would seem highly ambiguous. The smallest size class detected for Pd/C samples peaks at 0.8 nm. This size class was found in various samples prepared under different evaporation conditions and it is concluded that these particles consist of 'a magic number' of 13 atoms and have cubooctahedral or icosahedral crystal structure.

White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks.

PubMed

Choi, Jin Woo; Ku, Yunseo; Yoo, Byeong Wook; Kim, Jung-Ah; Lee, Dong Soon; Chai, Young Jun; Kong, Hyoun-Joong; Kim, Hee Chan

2017-01-01

The white blood cell differential count of the bone marrow provides information concerning the distribution of immature and mature cells within maturation stages. The results of such examinations are important for the diagnosis of various diseases and for follow-up care after chemotherapy. However, manual, labor-intensive methods to determine the differential count lead to inter- and intra-variations among the results obtained by hematologists. Therefore, an automated system to conduct the white blood cell differential count is highly desirable, but several difficulties hinder progress. There are variations in the white blood cells of each maturation stage, small inter-class differences within each stage, and variations in images because of the different acquisition and staining processes. Moreover, a large number of classes need to be classified for bone marrow smear analysis, and the high density of touching cells in bone marrow smears renders difficult the segmentation of single cells, which is crucial to traditional image processing and machine learning. Few studies have attempted to discriminate bone marrow cells, and even these have either discriminated only a few classes or yielded insufficient performance. In this study, we propose an automated white blood cell differential counting system from bone marrow smear images using a dual-stage convolutional neural network (CNN). A total of 2,174 patch images were collected for training and testing. The dual-stage CNN classified images into 10 classes of the myeloid and erythroid maturation series, and achieved an accuracy of 97.06%, a precision of 97.13%, a recall of 97.06%, and an F-1 score of 97.1%. The proposed method not only showed high classification performance, but also successfully classified raw images without single cell segmentation and manual feature extraction by implementing CNN. Moreover, it demonstrated rotation and location invariance. These results highlight the promise of the proposed method as an automated white blood cell differential count system.

White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks

PubMed Central

Choi, Jin Woo; Ku, Yunseo; Yoo, Byeong Wook; Kim, Jung-Ah; Lee, Dong Soon; Chai, Young Jun; Kong, Hyoun-Joong

2017-01-01

The white blood cell differential count of the bone marrow provides information concerning the distribution of immature and mature cells within maturation stages. The results of such examinations are important for the diagnosis of various diseases and for follow-up care after chemotherapy. However, manual, labor-intensive methods to determine the differential count lead to inter- and intra-variations among the results obtained by hematologists. Therefore, an automated system to conduct the white blood cell differential count is highly desirable, but several difficulties hinder progress. There are variations in the white blood cells of each maturation stage, small inter-class differences within each stage, and variations in images because of the different acquisition and staining processes. Moreover, a large number of classes need to be classified for bone marrow smear analysis, and the high density of touching cells in bone marrow smears renders difficult the segmentation of single cells, which is crucial to traditional image processing and machine learning. Few studies have attempted to discriminate bone marrow cells, and even these have either discriminated only a few classes or yielded insufficient performance. In this study, we propose an automated white blood cell differential counting system from bone marrow smear images using a dual-stage convolutional neural network (CNN). A total of 2,174 patch images were collected for training and testing. The dual-stage CNN classified images into 10 classes of the myeloid and erythroid maturation series, and achieved an accuracy of 97.06%, a precision of 97.13%, a recall of 97.06%, and an F-1 score of 97.1%. The proposed method not only showed high classification performance, but also successfully classified raw images without single cell segmentation and manual feature extraction by implementing CNN. Moreover, it demonstrated rotation and location invariance. These results highlight the promise of the proposed method as an automated white blood cell differential count system. PMID:29228051

Usefulness of a Novel Ultrasonographic Classification Based on Anechoic Area Patterns for Differentiating Warthin Tumors from Pleomorphic Adenomas of the Parotid Gland

PubMed Central

Matsuda, Eriko; Fukuhara, Takahiro; Donishi, Ryohei; Kawamoto, Katsuyuki; Hirooka, Yasuaki; Takeuchi, Hiromi

2018-01-01

Background Ultrasonographic homogeneity is an important differential finding between Warthin tumor and pleomorphic adenoma, two types of benign parotid gland tumors, with the former likely to be heterogeneous and the latter homogeneous. However, differences in the performance of ultrasound machines or the homogeneity cut-off level affect the judgment of ultrasonographic homogeneity. Therefore, in this study, we adopted a novel system for classifying the composition of tumors via ultrasonography, using anechoic area as a substitute for differences in homogeneity to differentiate between Warthin tumors and pleomorphic adenomas. Methods We evaluated 68 tumors that were histopathologically diagnosed as Warthin tumor or pleomorphic adenoma between July 2009 and November 2015. Ultrasonographic images of the tumors were evaluated on the basis of key differentiating features, including features on B-mode imaging and color Doppler imaging. Additionally, the tumors were classified into four groups based on anechoic area, and findings were compared between Warthin tumors and pleomorphic adenomas. Results While 38 of the tumors were pleomorphic adenomas, 30 were Warthin tumors. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for detection of Warthin tumors using our novel classification system were 73.3%, 76.3%, 71.0%, 78.4% and 75.0%, respectively. Compared to pleomorphic adenomas, Warthin tumors showed large or sponge-like anechoic areas, rich vascularization and an oval shape even at large tumor sizes, and the difference was significant. On defining Warthin tumor as a tumor demonstrating two or more of the findings noted above, the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy for its detection were 73.3%, 84.2%, 78.6%, 80.0% and 79.4%, respectively. Conclusion Our novel classification system based on anechoic area patterns demonstrated by the tumors had high sensitivity, specificity and diagnostic accuracy for differentiating Warthin tumors from pleomorphic adenomas. PMID:29434491

Usefulness of a Novel Ultrasonographic Classification Based on Anechoic Area Patterns for Differentiating Warthin Tumors from Pleomorphic Adenomas of the Parotid Gland.

PubMed

Matsuda, Eriko; Fukuhara, Takahiro; Donishi, Ryohei; Kawamoto, Katsuyuki; Hirooka, Yasuaki; Takeuchi, Hiromi

2017-12-01

Ultrasonographic homogeneity is an important differential finding between Warthin tumor and pleomorphic adenoma, two types of benign parotid gland tumors, with the former likely to be heterogeneous and the latter homogeneous. However, differences in the performance of ultrasound machines or the homogeneity cut-off level affect the judgment of ultrasonographic homogeneity. Therefore, in this study, we adopted a novel system for classifying the composition of tumors via ultrasonography, using anechoic area as a substitute for differences in homogeneity to differentiate between Warthin tumors and pleomorphic adenomas. We evaluated 68 tumors that were histopathologically diagnosed as Warthin tumor or pleomorphic adenoma between July 2009 and November 2015. Ultrasonographic images of the tumors were evaluated on the basis of key differentiating features, including features on B-mode imaging and color Doppler imaging. Additionally, the tumors were classified into four groups based on anechoic area, and findings were compared between Warthin tumors and pleomorphic adenomas. While 38 of the tumors were pleomorphic adenomas, 30 were Warthin tumors. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for detection of Warthin tumors using our novel classification system were 73.3%, 76.3%, 71.0%, 78.4% and 75.0%, respectively. Compared to pleomorphic adenomas, Warthin tumors showed large or sponge-like anechoic areas, rich vascularization and an oval shape even at large tumor sizes, and the difference was significant. On defining Warthin tumor as a tumor demonstrating two or more of the findings noted above, the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy for its detection were 73.3%, 84.2%, 78.6%, 80.0% and 79.4%, respectively. Our novel classification system based on anechoic area patterns demonstrated by the tumors had high sensitivity, specificity and diagnostic accuracy for differentiating Warthin tumors from pleomorphic adenomas.

iPixel: a visual content-based and semantic search engine for retrieving digitized mammograms by using collective intelligence.

PubMed

Alor-Hernández, Giner; Pérez-Gallardo, Yuliana; Posada-Gómez, Rubén; Cortes-Robles, Guillermo; Rodríguez-González, Alejandro; Aguilar-Laserre, Alberto A

2012-09-01

Nowadays, traditional search engines such as Google, Yahoo and Bing facilitate the retrieval of information in the format of images, but the results are not always useful for the users. This is mainly due to two problems: (1) the semantic keywords are not taken into consideration and (2) it is not always possible to establish a query using the image features. This issue has been covered in different domains in order to develop content-based image retrieval (CBIR) systems. The expert community has focussed their attention on the healthcare domain, where a lot of visual information for medical analysis is available. This paper provides a solution called iPixel Visual Search Engine, which involves semantics and content issues in order to search for digitized mammograms. iPixel offers the possibility of retrieving mammogram features using collective intelligence and implementing a CBIR algorithm. Our proposal compares not only features with similar semantic meaning, but also visual features. In this sense, the comparisons are made in different ways: by the number of regions per image, by maximum and minimum size of regions per image and by average intensity level of each region. iPixel Visual Search Engine supports the medical community in differential diagnoses related to the diseases of the breast. The iPixel Visual Search Engine has been validated by experts in the healthcare domain, such as radiologists, in addition to experts in digital image analysis.

MR imaging features and staging of neuroendocrine carcinomas of the uterine cervix with pathological correlations.

PubMed

Duan, Xiaohui; Ban, Xiaohua; Zhang, Xiang; Hu, Huijun; Li, Guozhao; Wang, Dongye; Wang, Charles Qian; Zhang, Fang; Shen, Jun

2016-12-01

To determine MR imaging features and staging accuracy of neuroendocrine carcinomas (NECs) of the uterine cervix with pathological correlations. Twenty-six patients with histologically proven NECs, 60 patients with squamous cell carcinomas (SCCs), and 30 patients with adenocarcinomas of the uterine cervix were included. The clinical data, pathological findings, and MRI findings were reviewed retrospectively. MRI features of cervical NECs, SCCs, and adenocarcinomas were compared, and MRI staging of cervical NECs was compared with the pathological staging. Cervical NECs showed a higher tendency toward a homogeneous signal intensity on T2-weighted imaging and a homogeneous enhancement pattern, as well as a lower ADC value of tumour and a higher incidence of lymphadenopathy, compared with SCCs and adenocarcinomas (P < 0.05). An ADC value cutoff of 0.90 × 10 -3  mm 2 /s was robust for differentiation between cervical NECs and other cervical cancers, with a sensitivity of 63.3 % and a specificity of 95 %. In 21 patients who underwent radical hysterectomy and lymphadenectomy, the overall accuracy of tumour staging by MR imaging was 85.7 % with reference to pathology staging. Homogeneous lesion texture and low ADC value are likely suggestive features of cervical NECs and MR imaging is reliable for the staging of cervical NECs. • Cervical NECs show a tendency of lesion homogeneity and lymphadenopathy • Low ADC values are found in cervical NECs • MRI is an accurate imaging modality for the cervical NEC staging.

[Value of MR imaging and MR angiography in the differential diagnosis of carotid space tumour].

PubMed

Liu, Pei-fang; Li, Xin; Bao, Run-xian; Liu, Jing-zu; Ge, Zheng-jin

2004-04-01

To determine the imaging features of magnetic resonance imaging (MRI) and 2D time of flight (TOF) MR angiography (MRA) and study the value in the differential diagnosis and surgical planning for carotid space tumors. Twenty-six patients with suspected pulsatile carotid space mass were imaged by MRI and 2D TOF MRA from 1996 to 2003. Its characteristic findings were analyzed for lesion shape, margin, signal intensity, angle of common carotid bifurcation, and the relationship between the great vessels and carotid space mass. Of the 26 patients, 22 were verified histopathologically, including 15 carotid body tumors (1 patient had bilateral carotid body tumors), 4 carotid artery aneurysms, 3 schwannomas, and 1 metastatic carcinoma. The rest four patients had clinical pseudomasses proved by MRI and MRA as considerable dilated or tortuous carotid artery as compared with the contralateral one. Combined MRI and MRA assessment of carotid body tumors and carotid artery aneurysm yielded an accuracy of 100%. It was also revealed that the anatomy shown on the MRI and axial MRA source images was consistent with that found by surgery. MRI in combination with MRA is considered as non-invasive imaging technique for the evaluation of carotid space tumor showing superiority to other modalities in the differential diagnosis between vascular versus non-vascular tumours. This method may take the place of traumatic carotid angiography.

Smart Cameras for Remote Science Survey

NASA Technical Reports Server (NTRS)

Thompson, David R.; Abbey, William; Allwood, Abigail; Bekker, Dmitriy; Bornstein, Benjamin; Cabrol, Nathalie A.; Castano, Rebecca; Estlin, Tara; Fuchs, Thomas; Wagstaff, Kiri L.

2012-01-01

Communication with remote exploration spacecraft is often intermittent and bandwidth is highly constrained. Future missions could use onboard science data understanding to prioritize downlink of critical features [1], draft summary maps of visited terrain [2], or identify targets of opportunity for followup measurements [3]. We describe a generic approach to classify geologic surfaces for autonomous science operations, suitable for parallelized implementations in FPGA hardware. We map these surfaces with texture channels - distinctive numerical signatures that differentiate properties such as roughness, pavement coatings, regolith characteristics, sedimentary fabrics and differential outcrop weathering. This work describes our basic image analysis approach and reports an initial performance evaluation using surface images from the Mars Exploration Rovers. Future work will incorporate these methods into camera hardware for real-time processing.

Scary gas: a spectrum of soft tissue gas encountered in the axial body (part II).

PubMed

Sandstrom, Claire K; Osman, Sherif F; Linnau, Ken F

2017-08-01

Ectopic gas in the mediastinum, subperitoneal abdomen, and superficial soft tissues is concerning and can be seen in the setting of trauma, iatrogenic injuries, infection, and inflammation. It can spread along different dissection pathways and may present remotely from the involved organ as described in part one. Recognition of ectopic gas on imaging and differentiating it from other causes of benign gas is very important as these conditions associated with ectopic gas can lead to rapid patient deterioration and usually require urgent surgery. In part two, the different causes of ectopic and benign gas in the torso are reviewed as well as the imaging features that can help to narrow the differential diagnosis.

Imaging the complex Farallon subduction system with USArray derived joint inversion of body waves and surface waves

NASA Astrophysics Data System (ADS)

Porritt, R. W.; Allen, R. M.; Pollitz, F. F.; Hung, S.

2012-12-01

The 150 million year history of subduction of the Farallon plate is being well elucidated by the passage of USArray. In this study, we use body wave relative delay times to generate independent P, SV, and SH relative velocity models for the USArray footprint. In addition, we use Rayleigh wave phase velocities derived from teleseismic earthquakes and ambient seismic noise to constrain the lithospheric structure where body waves have limited crossing ray information to form the SV-joint velocity model. The model volume contains a complex series of high velocities mostly along a planar front representing the remnants of the Farallon plate system. This feature has significant lateral and radial extent; beginning off the western coast of the US and terminating east of the model resolution, which goes to the Mississippi river. The bottom of the slab is well imaged through the mantle transition zone to at least 1000km. However, low velocity anomalies within this plane show the slab is far from a continuous sheet. Low velocities break up the slab into several major provinces, relating to different ages of orogens and an episode of flat slab subduction. Additionally, high velocities are often imaged well above the trace of the top of the slab with similar anomaly amplitude and dip as the main slab. While many of these anomalies have been interpreted as mantle drips, the similarity to the slab suggests a possible subduction origin for the features. However, the relatively shallow depths of these features require some mechanism of differentiation to develop neutral buoyancy. The prevalence of these high velocities, such as the Siletzia Curtain, Isabella Anomaly, Nevada Anomaly, and a newly imaged feature under southwest Texas, suggests a differentiation mechanism is fairly common among plates subducting under North America allowing for the observation of widespread shallow high velocity anomalies.

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.

Primary bone tumors of adulthood

PubMed Central

Teo, Harvey E L; Peh, Wilfred C G

2004-01-01

Imaging plays a crucial role in the evaluation of primary bone tumors in adults. Initial radiographic evaluation is indicated in all cases with suspected primary bone tumors. Radiographs are useful for providing the diagnosis, a short list of differential diagnosis or at least indicating the degree of aggressiveness of the lesion. More detailed information about the lesion, such as cortical destruction or local spread, can be obtained using cross-sectional imaging techniques such as computed tomography and magnetic resonance imaging. This article discusses the characteristic features of the more common primary bone tumors of adulthood, and also the pre-treatment evaluation and staging of these lesions using imaging techniques. PMID:18250012

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.

BP Piscium: its flaring disc imaged with SPHERE/ZIMPOL★

NASA Astrophysics Data System (ADS)

de Boer, J.; Girard, J. H.; Canovas, H.; Min, M.; Sitko, M.; Ginski, C.; Jeffers, S. V.; Mawet, D.; Milli, J.; Rodenhuis, M.; Snik, F.; Keller, C. U.

2017-03-01

Whether BP Piscium (BP Psc) is either a pre-main sequence T Tauri star at d ≈ 80 pc, or a post-main sequence G giant at d ≈ 300 pc is still not clear. As a first-ascent giant, it is the first to be observed with a molecular and dust disc. Alternatively, BP Psc would be among the nearest T Tauri stars with a protoplanetary disc (PPD). We investigate whether the disc geometry resembles typical PPDs, by comparing polarimetric images with radiative transfer models. Our Very Large Telescope/Spectro-Polarimetric High-contrast Exoplanet REsearch (SPHERE)/Zurich IMaging Polarimeter (ZIMPOL) observations allow us to perform polarimetric differential imaging, reference star differential imaging, and Richardson-Lucy deconvolution. We present the first visible light polarization and intensity images of the disc of BP Psc. Our deconvolution confirms the disc shape as detected before, mainly showing the southern side of the disc. In polarized intensity the disc is imaged at larger detail and also shows the northern side, giving it the typical shape of high-inclination flared discs. We explain the observed disc features by retrieving the large-scale geometry with MCMAX radiative transfer modelling, which yields a strongly flared model, atypical for discs of T Tauri stars.

Second harmonic generation microscopy differentiates collagen type I and type III in COPD

NASA Astrophysics Data System (ADS)

Suzuki, Masaru; Kayra, Damian; Elliott, W. Mark; Hogg, James C.; Abraham, Thomas

2012-03-01

The structural remodeling of extracellular matrix proteins in peripheral lung region is an important feature in chronic obstructive pulmonary disease (COPD). Multiphoton microscopy is capable of inducing specific second harmonic generation (SHG) signal from non-centrosymmetric structural proteins such as fibrillar collagens. In this study, SHG microscopy was used to examine structural remodeling of the fibrillar collagens in human lungs undergoing emphysematous destruction (n=2). The SHG signals originating from these diseased lung thin sections from base to apex (n=16) were captured simultaneously in both forward and backward directions. We found that the SHG images detected in the forward direction showed well-developed and well-structured thick collagen fibers while the SHG images detected in the backward direction showed striking different morphological features which included the diffused pattern of forward detected structures plus other forms of collagen structures. Comparison of these images with the wellestablished immunohistochemical staining indicated that the structures detected in the forward direction are primarily the thick collagen type I fibers and the structures identified in the backward direction are diffusive structures of forward detected collagen type I plus collagen type III. In conclusion, we here demonstrate the feasibility of SHG microscopy in differentiating fibrillar collagen subtypes and understanding their remodeling in diseased lung tissues.

Random-Forest Classification of High-Resolution Remote Sensing Images and Ndsm Over Urban Areas

NASA Astrophysics Data System (ADS)

Sun, X. F.; Lin, X. G.

2017-09-01

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample's category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.

Adult Brain Tumors and Pseudotumors: Interesting (Bizarre) Cases.

PubMed

Causil, Lazaro D; Ames, Romy; Puac, Paulo; Castillo, Mauricio

2016-11-01

Some brain tumors results are interesting due to their rarity at presentation and overwhelming imaging characteristics, posing a diagnostic challenge in the eyes of any experienced neuroradiologist. This article focuses on the most important features regarding epidemiology, location, clinical presentation, histopathology, and imaging findings of cases considered "bizarre." A review of the most recent literature dealing with these unusual tumors and pseudotumors is presented, highlighting key points related to the diagnosis, treatments, outcomes, and differential diagnosis. Copyright © 2016 Elsevier Inc. All rights reserved.

Non Lipomatous Benign Lesions Mimicking Soft-tissue Sarcomas: A Pictorial Essay

PubMed Central

CORAN, ALESSANDRO; ORSATTI, GIOVANNA; CRIMÌ, FILIPPO; RASTRELLI, MARCO; DI MAGGIO, ANTONIO; PONZONI, ALBERTO; ATTAR, SHADY; STRAMARE, ROBERTO

2018-01-01

The incidental finding of soft tissue masses is a challenge for the radiologist. Benign and malignant lesions can be differentiated relying on patient history, symptoms and mostly with the help of imaging. Ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI) become fundamental in order to distinguish these lesions but the radiologist needs to know the main characteristics of benign soft tissue masses and sarcomas. Herein, we present a pictorial review of lesions mimicking soft tissue sarcomas features. PMID:29475903

Characterization of human oral tissues based on quantitative analysis of optical coherence tomography images

NASA Astrophysics Data System (ADS)

Salehi, Hassan S.; Kosa, Ali; Mahdian, Mina; Moslehpour, Saeid; Alnajjar, Hisham; Tadinada, Aditya

2017-02-01

In this paper, five types of tissues, human enamel, human cortical bone, human trabecular bone, muscular tissue, and fatty tissue were imaged ex vivo using optical coherence tomography (OCT). The specimens were prepared in blocks of 5 x 5 x 3 mm (width x length x height). The OCT imaging system was a swept source OCT system operating at wavelengths ranging between 1250 nm and 1360 nm with an average power of 18 mW and a scan rate of 50 to 100 kHz. The imaging probe was placed on top of a 2 x 2 cm stabilizing device to maintain a standard distance from the samples. Ten image samples from each type of tissue were obtained. To acquire images with minimum inhomogeneity, imaging was performed multiple times at different points. Based on the observed texture differences between OCT images of soft and hard tissues, spatial and spectral features were quantitatively extracted from the OCT images. The Radon transform from angles of 0 deg to 90 deg was computed, averaged over all the angles, normalized to peak at unity, and then fitted with Gaussian function. The mean absolute values of the spatial frequency components of the OCT image were considered as a feature, where 2-D fast Fourier transform (FFT) was done to OCT images. These OCT features can reliably differentiate between a range of hard and soft tissues, and could be extremely valuable in assisting dentists for in vivo evaluation of oral tissues and early detection of pathologic changes in tissues.

An evolutionary computation based algorithm for calculating solar differential rotation by automatic tracking of coronal bright points

NASA Astrophysics Data System (ADS)

Shahamatnia, Ehsan; Dorotovič, Ivan; Fonseca, Jose M.; Ribeiro, Rita A.

2016-03-01

Developing specialized software tools is essential to support studies of solar activity evolution. With new space missions such as Solar Dynamics Observatory (SDO), solar images are being produced in unprecedented volumes. To capitalize on that huge data availability, the scientific community needs a new generation of software tools for automatic and efficient data processing. In this paper a prototype of a modular framework for solar feature detection, characterization, and tracking is presented. To develop an efficient system capable of automatic solar feature tracking and measuring, a hybrid approach combining specialized image processing, evolutionary optimization, and soft computing algorithms is being followed. The specialized hybrid algorithm for tracking solar features allows automatic feature tracking while gathering characterization details about the tracked features. The hybrid algorithm takes advantages of the snake model, a specialized image processing algorithm widely used in applications such as boundary delineation, image segmentation, and object tracking. Further, it exploits the flexibility and efficiency of Particle Swarm Optimization (PSO), a stochastic population based optimization algorithm. PSO has been used successfully in a wide range of applications including combinatorial optimization, control, clustering, robotics, scheduling, and image processing and video analysis applications. The proposed tool, denoted PSO-Snake model, was already successfully tested in other works for tracking sunspots and coronal bright points. In this work, we discuss the application of the PSO-Snake algorithm for calculating the sidereal rotational angular velocity of the solar corona. To validate the results we compare them with published manual results performed by an expert.

Ensemble approach for differentiation of malignant melanoma

NASA Astrophysics Data System (ADS)

Rastgoo, Mojdeh; Morel, Olivier; Marzani, Franck; Garcia, Rafael

2015-04-01

Melanoma is the deadliest type of skin cancer, yet it is the most treatable kind depending on its early diagnosis. The early prognosis of melanoma is a challenging task for both clinicians and dermatologists. Due to the importance of early diagnosis and in order to assist the dermatologists, we propose an automated framework based on ensemble learning methods and dermoscopy images to differentiate melanoma from dysplastic and benign lesions. The evaluation of our framework on the recent and public dermoscopy benchmark (PH2 dataset) indicates the potential of proposed method. Our evaluation, using only global features, revealed that ensembles such as random forest perform better than single learner. Using random forest ensemble and combination of color and texture features, our framework achieved the highest sensitivity of 94% and specificity of 92%.

Quantification of confocal fluorescence microscopy for the detection of cervical intraepithelial neoplasia.

PubMed

Sheikhzadeh, Fahime; Ward, Rabab K; Carraro, Anita; Chen, Zhao Yang; van Niekerk, Dirk; Miller, Dianne; Ehlen, Tom; MacAulay, Calum E; Follen, Michele; Lane, Pierre M; Guillaud, Martial

2015-10-24

Cervical cancer remains a major health problem, especially in developing countries. Colposcopic examination is used to detect high-grade lesions in patients with a history of abnormal pap smears. New technologies are needed to improve the sensitivity and specificity of this technique. We propose to test the potential of fluorescence confocal microscopy to identify high-grade lesions. We examined the quantification of ex vivo confocal fluorescence microscopy to differentiate among normal cervical tissue, low-grade Cervical Intraepithelial Neoplasia (CIN), and high-grade CIN. We sought to (1) quantify nuclear morphology and tissue architecture features by analyzing images of cervical biopsies; and (2) determine the accuracy of high-grade CIN detection via confocal microscopy relative to the accuracy of detection by colposcopic impression. Forty-six biopsies obtained from colposcopically normal and abnormal cervical sites were evaluated. Confocal images were acquired at different depths from the epithelial surface and histological images were analyzed using in-house software. The features calculated from the confocal images compared well with those features obtained from the histological images and histopathological reviews of the specimens (obtained by a gynecologic pathologist). The correlations between two of these features (the nuclear-cytoplasmic ratio and the average of three nearest Delaunay-neighbors distance) and the grade of dysplasia were higher than that of colposcopic impression. The sensitivity of detecting high-grade dysplasia by analysing images collected at the surface of the epithelium, and at 15 and 30 μm below the epithelial surface were respectively 100, 100, and 92 %. Quantitative analysis of confocal fluorescence images showed its capacity for discriminating high-grade CIN lesions vs. low-grade CIN lesions and normal tissues, at different depth of imaging. This approach could be used to help clinicians identify high-grade CIN in clinical settings.

Can Abdominal CT Imaging Help Accurately Identify a Dedifferentiated Component in a Well-Differentiated Liposarcoma?

PubMed Central

Bhosale, Priya; Wang, Jieqi; Varma, Datla G.K; Jensen, Corey; Patnana, Madhavi; Wei, Wei; Chauhan, Anil; Feig, Barry; Patel, Shreyaskumar; Somaiah, Neeta; Sagebiel, Tara

2016-01-01

Purpose To assess the ability of CT to differentiate an atypical lipomatous tumor (ALT)/well-differentiated liposarcoma (WDLPS) from a WDLPS with a dedifferentiated component (DDLPS) within it. Materials and Methods Forty-nine untreated patients with abdominal atypical lipomatous tumors/well-differentiated liposarcomas who had undergone contrast-enhanced CT were identified using an institutional database. Three radiologists who were blinded to the pathology findings evaluated all the images independently to determine whether a dedifferentiated component was present within the WDLPS. The CT images were evaluated for fat content (≤25% or >25%); presence of ground-glass density, enhancing and/or necrotic nodules; presence of a capsule surrounding the mass; septations; and presence and pattern of calcifications. A multivariate logistic regression model with generalized estimating equations was used to correlate imaging features with pathology findings. Kappa statistics were calculated to assess agreement between the three radiologists. Results On the basis of pathological findings, 12 patients had been diagnosed with DDLPS within a WDLPS and 37 had been diagnosed with WDLPS. The presence of an enhancing or a centrally necrotic nodule within the atypical lipomatous tumor was associated with dedifferentiated liposarcoma (p = 0.02 and p = 0.0003, respectively). The three readers showed almost perfect agreement in overall diagnosis (kappa r = 0.83; 95% confidence-interval 0.67 to 0.99). Conclusion An enhancing or centrally necrotic nodule may be indicative of a dedifferentiated component in well-differentiated liposarcoma. Ground-glass density nodules may not be indicative of dedifferentiation. PMID:27454788

A neuromorphic approach to satellite image understanding

NASA Astrophysics Data System (ADS)

Partsinevelos, Panagiotis; Perakakis, Manolis

2014-05-01

Remote sensing satellite imagery provides high altitude, top viewing aspects of large geographic regions and as such the depicted features are not always easily recognizable. Nevertheless, geoscientists familiar to remote sensing data, gradually gain experience and enhance their satellite image interpretation skills. The aim of this study is to devise a novel computational neuro-centered classification approach for feature extraction and image understanding. Object recognition through image processing practices is related to a series of known image/feature based attributes including size, shape, association, texture, etc. The objective of the study is to weight these attribute values towards the enhancement of feature recognition. The key cognitive experimentation concern is to define the point when a user recognizes a feature as it varies in terms of the above mentioned attributes and relate it with their corresponding values. Towards this end, we have set up an experimentation methodology that utilizes cognitive data from brain signals (EEG) and eye gaze data (eye tracking) of subjects watching satellite images of varying attributes; this allows the collection of rich real-time data that will be used for designing the image classifier. Since the data are already labeled by users (using an input device) a first step is to compare the performance of various machine-learning algorithms on the collected data. On the long-run, the aim of this work would be to investigate the automatic classification of unlabeled images (unsupervised learning) based purely on image attributes. The outcome of this innovative process is twofold: First, in an abundance of remote sensing image datasets we may define the essential image specifications in order to collect the appropriate data for each application and improve processing and resource efficiency. E.g. for a fault extraction application in a given scale a medium resolution 4-band image, may be more effective than costly, multispectral, very high resolution imagery. Second, we attempt to relate the experienced against the non-experienced user understanding in order to indirectly assess the possible limits of purely computational systems. In other words, obtain the conceptual limits of computation vs human cognition concerning feature recognition from satellite imagery. Preliminary results of this pilot study show relations between collected data and differentiation of the image attributes which indicates that our methodology can lead to important results.

A Taxonomy of 3D Occluded Objects Recognition Techniques

NASA Astrophysics Data System (ADS)

Soleimanizadeh, Shiva; Mohamad, Dzulkifli; Saba, Tanzila; Al-ghamdi, Jarallah Saleh

2016-03-01

The overall performances of object recognition techniques under different condition (e.g., occlusion, viewpoint, and illumination) have been improved significantly in recent years. New applications and hardware are shifted towards digital photography, and digital media. This faces an increase in Internet usage requiring object recognition for certain applications; particularly occulded objects. However occlusion is still an issue unhandled, interlacing the relations between extracted feature points through image, research is going on to develop efficient techniques and easy to use algorithms that would help users to source images; this need to overcome problems and issues regarding occlusion. The aim of this research is to review recognition occluded objects algorithms and figure out their pros and cons to solve the occlusion problem features, which are extracted from occluded object to distinguish objects from other co-existing objects by determining the new techniques, which could differentiate the occluded fragment and sections inside an image.

Beef quality grading using machine vision

NASA Astrophysics Data System (ADS)

Jeyamkondan, S.; Ray, N.; Kranzler, Glenn A.; Biju, Nisha

2000-12-01

A video image analysis system was developed to support automation of beef quality grading. Forty images of ribeye steaks were acquired. Fat and lean meat were differentiated using a fuzzy c-means clustering algorithm. Muscle longissimus dorsi (l.d.) was segmented from the ribeye using morphological operations. At the end of each iteration of erosion and dilation, a convex hull was fitted to the image and compactness was measured. The number of iterations was selected to yield the most compact l.d. Match between the l.d. muscle traced by an expert grader and that segmented by the program was 95.9%. Marbling and color features were extracted from the l.d. muscle and were used to build regression models to predict marbling and color scores. Quality grade was predicted using another regression model incorporating all features. Grades predicted by the model were statistically equivalent to the grades assigned by expert graders.

Training of polyp staging systems using mixed imaging modalities.

PubMed

Wimmer, Georg; Gadermayr, Michael; Kwitt, Roland; Häfner, Michael; Tamaki, Toru; Yoshida, Shigeto; Tanaka, Shinji; Merhof, Dorit; Uhl, Andreas

2018-05-04

In medical image data sets, the number of images is usually quite small. The small number of training samples does not allow to properly train classifiers which leads to massive overfitting to the training data. In this work, we investigate whether increasing the number of training samples by merging datasets from different imaging modalities can be effectively applied to improve predictive performance. Further, we investigate if the extracted features from the employed image representations differ between different imaging modalities and if domain adaption helps to overcome these differences. We employ twelve feature extraction methods to differentiate between non-neoplastic and neoplastic lesions. Experiments are performed using four different classifier training strategies, each with a different combination of training data. The specifically designed setup for these experiments enables a fair comparison between the four training strategies. Combining high definition with high magnification training data and chromoscopic with non-chromoscopic training data partly improved the results. The usage of domain adaptation has only a small effect on the results compared to just using non-adapted training data. Merging datasets from different imaging modalities turned out to be partially beneficial for the case of combining high definition endoscopic data with high magnification endoscopic data and for combining chromoscopic with non-chromoscopic data. NBI and chromoendoscopy on the other hand are mostly too different with respect to the extracted features to combine images of these two modalities for classifier training. Copyright © 2018 Elsevier Ltd. All rights reserved.

Spinal bone marrow necrosis with vertebral compression fracture: differentiation of BMN from AVN.

PubMed

Nix, J S; Fitzgerald, R T; Samant, R S; Harrison, M; Angtuaco, E J

2014-09-01

Bone marrow necrosis (BMN) is a rare malignancy-associated hematologic disorder characterized by necrosis of myeloid and stromal marrow elements with preservation of cortical bone. Overlap between the imaging appearances of BMN and avascular necrosis (AVN) raises the potential for diagnostic confusion. We report a case of BMN presenting with a traumatic multi-level vertebral body collapse, and finding that may potentially confound distinction between the two entities. We discuss important pathophysiologic, clinical, and radiologic differences between BMN and AVN with emphasis on features important in the differential diagnosis.

A reconstruction method for cone-beam differential x-ray phase-contrast computed tomography.

PubMed

Fu, Jian; Velroyen, Astrid; Tan, Renbo; Zhang, Junwei; Chen, Liyuan; Tapfer, Arne; Bech, Martin; Pfeiffer, Franz

2012-09-10

Most existing differential phase-contrast computed tomography (DPC-CT) approaches are based on three kinds of scanning geometries, described by parallel-beam, fan-beam and cone-beam. Due to the potential of compact imaging systems with magnified spatial resolution, cone-beam DPC-CT has attracted significant interest. In this paper, we report a reconstruction method based on a back-projection filtration (BPF) algorithm for cone-beam DPC-CT. Due to the differential nature of phase contrast projections, the algorithm restrains from differentiation of the projection data prior to back-projection, unlike BPF algorithms commonly used for absorption-based CT data. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured with a three-grating interferometer and a micro-focus x-ray tube source. Moreover, the numerical simulation and experimental results demonstrate that the proposed method can deal with several classes of truncated cone-beam datasets. We believe that this feature is of particular interest for future medical cone-beam phase-contrast CT imaging applications.

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

PubMed

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

2009-02-01

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

Region-Based Prediction for Image Compression in the Cloud.

PubMed

Begaint, Jean; Thoreau, Dominique; Guillotel, Philippe; Guillemot, Christine

2018-04-01

Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we use a semi-local approach to exploit inter-image correlation. The reference image is first segmented into multiple planar regions determined from matched local features and super-pixels. The geometric and photometric disparities between the matched regions of the reference image and the current image are then compensated. Finally, multiple references are generated from the estimated compensation models and organized in a pseudo-sequence to differentially encode the input image using classical video coding tools. Experimental results demonstrate that the proposed approach yields significant rate-distortion performance improvements compared with the current image inter-coding solutions such as high efficiency video coding.

Recognition of finger flexion motion from ultrasound image: a feasibility study.

PubMed

Shi, Jun; Guo, Jing-Yi; Hu, Shu-Xian; Zheng, Yong-Ping

2012-10-01

Muscle contraction results in structural and morphologic changes of the related muscle. Therefore, finger flexion can be monitored from measurements of these morphologic changes. We used ultrasound imaging to record muscle activities during finger flexion and extracted features to discriminate different fingers' flexions using a support vector machine (SVM). Registration of ultrasound images before and after finger flexion was performed to generate a deformation field, from which angle features and wavelet-based features were extracted. The SVM was then used to classify the motions of different fingers. The experimental results showed that the overall mean recognition accuracy was 94.05% ± 4.10%, with the highest for the thumb (97%) and the lowest for the ring finger (92%) and the mean F value was 0.94 ± 0.02, indicating high accuracy and reliability of this method. The results suggest that the proposed method has the potential to be used as an alternative method of surface electromyography in differentiating the motions of different fingers. Copyright © 2012 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Cranio-orbital primary intraosseous haemangioma.

PubMed

Gupta, T; Rose, G E; Manisali, M; Minhas, P; Uddin, J M; Verity, D H

2013-11-01

Primary intraosseous haemangioma (IOH) is a rare benign neoplasm presenting in the fourth and fifth decades of life. The spine and skull are the most commonly involved, orbital involvement is extremely rare. We describe six patients with cranio-orbital IOH, the largest case series to date. Retrospective review of six patients with histologically confirmed primary IOH involving the orbit. Clinical characteristics, imaging features, approach to management, and histopathological findings are described. Five patients were male with a median age of 56. Pain and diplopia were the most common presenting features. A characteristic 'honeycomb' pattern on CT imaging was demonstrated in three of the cases. Complete surgical excision was performed in all cases with presurgical embolisation carried out in one case. In all the cases, histological studies identified cavernous vascular spaces within the bony tissue. These channels were lined by single layer of cytologically normal endothelial cells. IOCH of the cranio-orbital region is rare; in the absence of typical imaging features, the differential diagnosis includes chondroma, chondrosarcoma, bony metastasis, and lymphoma. Surgical excision may be necessary to exclude more sinister pathology. Intraoperative haemorrhage can be severe and may be reduced by preoperative embolisation.

CT and MRI imaging of the brain in MELAS syndrome.

PubMed

Pauli, Wojciech; Zarzycki, Artur; Krzyształowski, Adam; Walecka, Anna

2013-07-01

MELAS syndrome (mitochondrial myopathy, encephalopathy, lactic acidosis, stroke-like episodes) is a rare, multisystem disorder which belongs to a group of mitochondrial metabolic diseases. As other diseases in this group, it is inherited in the maternal line. In this report, we discussed a case of a 10-year-old girl with clinical and radiological picture of MELAS syndrome. We would like to describe characteristic radiological features of MELAS syndrome in CT, MRI and MR spectroscopy of the brain and differential diagnosis. The rarity of this disorder and the complexity of its clinical presentation make MELAS patients among the most difficult to diagnose. Brain imaging studies require a wide differential diagnosis, primarily to distinguish between MELAS and ischemic stroke. Particularly helpful are the MRI and MR spectroscopy techniques.

Automatic classification of cardioembolic and arteriosclerotic ischemic strokes from apparent diffusion coefficient datasets using texture analysis and deep learning

NASA Astrophysics Data System (ADS)

Villafruela, Javier; Crites, Sebastian; Cheng, Bastian; Knaack, Christian; Thomalla, Götz; Menon, Bijoy K.; Forkert, Nils D.

2017-03-01

Stroke is a leading cause of death and disability in the western hemisphere. Acute ischemic strokes can be broadly classified based on the underlying cause into atherosclerotic strokes, cardioembolic strokes, small vessels disease, and stroke with other causes. The ability to determine the exact origin of an acute ischemic stroke is highly relevant for optimal treatment decision and preventing recurrent events. However, the differentiation of atherosclerotic and cardioembolic phenotypes can be especially challenging due to similar appearance and symptoms. The aim of this study was to develop and evaluate the feasibility of an image-based machine learning approach for discriminating between arteriosclerotic and cardioembolic acute ischemic strokes using 56 apparent diffusion coefficient (ADC) datasets from acute stroke patients. For this purpose, acute infarct lesions were semi-atomically segmented and 30,981 geometric and texture image features were extracted for each stroke volume. To improve the performance and accuracy, categorical Pearson's χ2 test was used to select the most informative features while removing redundant attributes. As a result, only 289 features were finally included for training of a deep multilayer feed-forward neural network without bootstrapping. The proposed method was evaluated using a leave-one-out cross validation scheme. The proposed classification method achieved an average area under receiver operator characteristic curve value of 0.93 and a classification accuracy of 94.64%. These first results suggest that the proposed image-based classification framework can support neurologists in clinical routine differentiating between atherosclerotic and cardioembolic phenotypes.

Gadoxetate Acid-Enhanced MR Imaging for HCC: A Review for Clinicians

PubMed Central

Chanyaputhipong, Jendana; Low, Su-Chong Albert; Chow, Pierce K. H.

2011-01-01

Hepatocellular carcinoma (HCC) is increasingly being detected at an earlier stage, owing to the screening programs and regular imaging follow-up in high-risk populations. Small HCCs still pose diagnostic challenges on imaging due to decreased sensitivity and increased frequency of atypical features. Differentiating early HCC from premalignant or benign nodules is important as management differs and has implications on both the quality of life and the overall survival for the patients. Gadoxetate acid (Gd-EOB-DTPA, Primovist®, Bayer Schering Pharma) is a relatively new, safe and well-tolerated liver-specific contrast agent for magnetic resonance (MR) imaging of the liver that has combined perfusion- and hepatocyte-specific properties, allowing for the acquisition of both dynamic and hepatobiliary phase images. Its high biliary uptake and excretion improves lesion detection and characterization by increasing liver-to-lesion conspicuity in the added hepatobiliary phase imaging. To date, gadoxetate acid-enhanced MRI has been mostly shown to be superior to unenhanced MRI, computed tomography, and other types of contrast agents in the detection and characterization of liver lesions. This review article focuses on the evolving role of gadoxetate acid in the characterization of HCC, differentiating it from other mimickers of HCC. PMID:21994860

Swimsuit issues: promoting positive body image in young women's magazines.

PubMed

Boyd, Elizabeth Reid; Moncrieff-Boyd, Jessica

2011-08-01

This preliminary study reviews the promotion of healthy body image to young Australian women, following the 2009 introduction of the voluntary Industry Code of Conduct on Body Image. The Code includes using diverse sized models in magazines. A qualitative content analysis of the 2010 annual 'swimsuit issues' was conducted on 10 Australian young women's magazines. Pictorial and/or textual editorial evidence of promoting diverse body shapes and sizes was regarded as indicative of the magazines' upholding aspects of the voluntary Code of Conduct for Body Image. Diverse sized models were incorporated in four of the seven magazines with swimsuit features sampled. Body size differentials were presented as part of the swimsuit features in three of the magazines sampled. Tips for diverse body type enhancement were included in four of the magazines. All magazines met at least one criterion. One magazine displayed evidence of all three criteria. Preliminary examination suggests that more than half of young women's magazines are upholding elements of the voluntary Code of Conduct for Body Image, through representation of diverse-sized women in their swimsuit issues.

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.

Improving image quality in laboratory x-ray phase-contrast imaging

NASA Astrophysics Data System (ADS)

De Marco, F.; Marschner, M.; Birnbacher, L.; Viermetz, M.; Noël, P.; Herzen, J.; Pfeiffer, F.

2017-03-01

Grating-based X-ray phase-contrast (gbPC) is known to provide significant benefits for biomedical imaging. To investigate these benefits, a high-sensitivity gbPC micro-CT setup for small (≍ 5 cm) biological samples has been constructed. Unfortunately, high differential-phase sensitivity leads to an increased magnitude of data processing artifacts, limiting the quality of tomographic reconstructions. Most importantly, processing of phase-stepping data with incorrect stepping positions can introduce artifacts resembling Moiré fringes to the projections. Additionally, the focal spot size of the X-ray source limits resolution of tomograms. Here we present a set of algorithms to minimize artifacts, increase resolution and improve visual impression of projections and tomograms from the examined setup. We assessed two algorithms for artifact reduction: Firstly, a correction algorithm exploiting correlations of the artifacts and differential-phase data was developed and tested. Artifacts were reliably removed without compromising image data. Secondly, we implemented a new algorithm for flatfield selection, which was shown to exclude flat-fields with strong artifacts. Both procedures successfully improved image quality of projections and tomograms. Deconvolution of all projections of a CT scan can minimize blurring introduced by the finite size of the X-ray source focal spot. Application of the Richardson-Lucy deconvolution algorithm to gbPC-CT projections resulted in an improved resolution of phase-contrast tomograms. Additionally, we found that nearest-neighbor interpolation of projections can improve the visual impression of very small features in phase-contrast tomograms. In conclusion, we achieved an increase in image resolution and quality for the investigated setup, which may lead to an improved detection of very small sample features, thereby maximizing the setup's utility.

Electronic structure and simulated STM images of non-honeycomb phosphorene allotropes

NASA Astrophysics Data System (ADS)

Kaur, Sumandeep; Kumar, Ashok; Srivastava, Sunita; Tankeshwar, K.

2018-04-01

We have investigated the electronic structure and simulated STM images of various non-honeycomb allotropes of phosphorene namely ɛ - P, ζ - P, η - P and θ - P, within combined density functional theory and Tersoff-Hamman approach. All these allotropes are found to be energetically stable and electronically semiconductingwith bandgap ranging between 0.5-1.2 eV. Simulated STM images show distinctly different features in terms of the topography. Different maximas in the distance-height profile indicates the difference in buckling of atoms in these allotropes. Distinctly different images obtained in this study may be useful to differentiate various allotropes that can serve as fingerprints to identify various allotropes during the synthesis of phosphorene.

Assessment of ultrasonography and computed tomography for the evaluation of unilateral orbital disease in dogs.

PubMed

Boroffka, Susanne A E B; Verbruggen, Anne-Marie; Grinwis, Guy C M; Voorhout, George; Barthez, Paul Y

2007-03-01

To describe clinical, ultrasonographic, and computed tomographic (CT) features of confirmed neoplastic and nonneoplastic disease in dogs with unilateral orbital diseases, determine criteria to differentiate between the 2 conditions, and assess the relative value of ultrasonography and CT for the differential diagnosis of these 2 conditions. Prospective study. 29 dogs with unilateral neoplastic orbital disease and 16 dogs with unilateral nonneoplastic orbital disease. Clinical history and results of physical and ophthalmologic examinations were recorded. Ultrasonographic and CT images were evaluated, and discriminating factors were identified to differentiate neoplastic from nonneoplastic diseases. Diagnostic value of ultrasonography and CT was assessed. Dogs with neoplastic disease were significantly older; had clinical signs for a longer time before initial examination; had more progressive onset of clinical signs; and more frequently had protrusion of the nictitating membrane, fever, and anorexia. The most discriminating factor for both imaging modalities was delineation of the margins (odds ratio was 41.7 for ultrasonography and 45 for CT), with neoplastic lesions clearly delineated more often. Ultrasonographically, neoplastic lesions were more frequently hypoechoic and homogeneous, with indentation of the globe and bone involvement evident more frequently than for nonneoplastic lesions. Mineralization was detected only with neoplasia. Fluctuant fluid was seen more frequently in dogs with nonneoplastic disease. Computed tomography more frequently revealed extraorbital involvement. Diagnostic value was similar for both imaging modalities. Ultrasonography and CT are valuable imaging modalities to assist in differentiating neoplastic from nonneoplastic unilateral orbital disease in dogs.

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.

Lysozyme activity and nitroblue-tetrazolium reduction in leukaemic cells

PubMed Central

Catovsky, D.; Galton, D. A. G.

1973-01-01

The cytochemical methods for lysozyme and nitroblue-tetrazolium reduction have been used to study the blast cells of acute myeloid leukaemia. Both proved useful in characterizing the cases with predominant monocytic differentiation. The demonstration of lysozyme activity helped to define two main groups: (a) with predominantly lysozyme-negative cells (myeloblastic-promyelocytic), and (b) with considerable numbers of positive cells (monoblastic-monocytic). In addition this test was also of value in the differentiation of other leukaemic disorders. Reduction of nitroblue-tetrazolium was also a feature of monocytic differentiation. The combination of these two methods with those for myeloperoxidase and non-specific esterase activity contributes to the cytological characterization of acute myeloid leukaemia. Images PMID:4511938

Differentiating Amino Acid Residues and Side Chain Orientations in Peptides Using Scanning Tunneling Microscopy

PubMed Central

Claridge, Shelley A.; Thomas, John C.; Silverman, Miles A.; Schwartz, Jeffrey J.; Yang, Yanlian; Wang, Chen; Weiss, Paul S.

2014-01-01

Single-molecule measurements of complex biological structures such as proteins are an attractive route for determining structures of the large number of important biomolecules that have proved refractory to analysis through standard techniques such as X-ray crystallography and nuclear magnetic resonance. We use a custom-built low-current scanning tunneling microscope to image peptide structure at the single-molecule scale in a model peptide that forms β sheets, a structural motif common in protein misfolding diseases. We successfully differentiate between histidine and alanine amino acid residues, and further differentiate side chain orientations in individual histidine residues, by correlating features in scanning tunneling microscope images with those in energy-optimized models. Beta sheets containing histidine residues are used as a model system due to the role histidine plays in transition metal binding associated with amyloid oligomerization in Alzheimer’s and other diseases. Such measurements are a first step toward analyzing peptide and protein structures at the single-molecule level. PMID:24219245

Differentiating immunoglobulin g4-related sclerosing cholangitis from hilar cholangiocarcinoma.

PubMed

Tabata, Taku; Kamisawa, Terumi; Hara, Seiichi; Kuruma, Sawako; Chiba, Kazuro; Kuwata, Go; Fujiwara, Takashi; Egashira, Hideto; Koizumi, Koichi; Fujiwara, Junko; Arakawa, Takeo; Momma, Kumiko; Kurata, Masanao; Honda, Goro; Tsuruta, Koji; Itoi, Takao

2013-03-01

Few studies have differentiated immunoglobulin G (IgG) 4-related sclerosing cholangitis (IgG4-SC) from hilar cholangiocarcinoma (CC). Thus, we sought to investigate useful features for differentiating IgG4-SC from hilar CC. We retrospectively compared clinical, serological, imaging, and histological features of six patients with IgG4-SC and 42 patients with hilar CC. In patients with hilar CC, obstructive jaundice was more frequent (p<0.01), serum total bilirubin levels were significantly higher (p<0.05), serum CA19-9 levels were significantly higher (p<0.01), and serum duke pancreatic monoclonal antigen type 2 levels were frequently elevated (p<0.05). However, in patients with IgG4-SC, the serum IgG (p<0.05) and IgG4 (p<0.01) levels were significantly higher and frequently elevated. The pancreas was enlarged in all IgG4-SC patients but only in 17% of hilar CC patients (p<0.01). Salivary and/or lacrimal gland swelling was detected in only 50% of IgG4-SC patients (p<0.01). Endoscopic retrograde cholangiography revealed that the hilar or hepatic duct was completely obstructed in 83% of hilar CC patients (p<0.01). Lower bile duct stenosis, apart from hilar bile duct stenosis, was more frequent in IgG4-SC patients (p<0.01). Bile duct wall thickening in areas without stenosis was more frequent in IgG4-SC patients (p<0.01). An integrated diagnostic approach based on clinical, serological, imaging, and histological findings is necessary to differentiate IgG4-SC from hilar CC.

Fat: friend or foe? A review of fat-containing masses within the head and neck.

PubMed

Kale, Hrishikesh A; Prabhu, Arpan V; Sinelnikov, Andrey; Branstetter, Barton

2016-11-01

Fat-containing lesions of the head and neck are commonly encountered in day-to-day practice. Our aim was to review the various imaging presentations of common and some uncommon fat-containing lesions within the head and neck with potential pitfalls and mimics. While most soft-tissue masses have a fairly similar density, the presence of fat in a mass lesion is easy to identify on both CT/MRI and can help narrow the differential. Case-based examples of lipomas, liposarcomas, lipoblastomas, dermoids, teratomas and other fatty lesions will be used to describe imaging features. While fat density can be helpful, differentiating benign from malignant fat-containing lesions can still pose a challenge. Lesions simulating pathology such as brown fat, fatty changes within organs and post-operative flaps are presented. Finally, examples of fatty lesions in atypical locations are shown to illustrate examples that should be kept in mind in any differential. The presence of fat in head and neck masses can aid radiologists in arriving at an accurate diagnosis. Knowledge of the imaging appearance of these fat-containing lesions and their mimics can help avoid unnecessary biopsy or surgery.

Reconstruction and feature selection for desorption electrospray ionization mass spectroscopy imagery

NASA Astrophysics Data System (ADS)

Gao, Yi; Zhu, Liangjia; Norton, Isaiah; Agar, Nathalie Y. R.; Tannenbaum, Allen

2014-03-01

Desorption electrospray ionization mass spectrometry (DESI-MS) provides a highly sensitive imaging technique for differentiating normal and cancerous tissue at the molecular level. This can be very useful, especially under intra-operative conditions where the surgeon has to make crucial decision about the tumor boundary. In such situations, the time it takes for imaging and data analysis becomes a critical factor. Therefore, in this work we utilize compressive sensing to perform the sparse sampling of the tissue, which halves the scanning time. Furthermore, sparse feature selection is performed, which not only reduces the dimension of data from about 104 to less than 50, and thus significantly shortens the analysis time. This procedure also identifies biochemically important molecules for further pathological analysis. The methods are validated on brain and breast tumor data sets.

Differentiation of deep venous thrombosis from femoral vein mixing artifact on routine abdominopelvic CT.

PubMed

Doshi, Ankur M; Hoffman, David; Kierans, Andrea S; Ream, Justin M; Rosenkrantz, Andrew B

2015-10-01

The objective of this study is to assess the performance of qualitative and quantitative imaging features for the differentiation of deep venous thrombosis (DVT) from mixing artifact on routine portal venous phase abdominopelvic CT. This retrospective study included 40 adult patients with a femoral vein filling defect on portal venous phase CT and a Duplex ultrasound (n = 36) or catheter venogram (n = 4) to confirm presence or absence of DVT. Two radiologists (R1, R2) assessed the femoral veins for various qualitative and quantitative features. 60% of patients were confirmed to have DVT and 40% had mixing artifact. Features with significantly greater frequency in DVT than mixing artifact (all p ≤ 0.006) were central location (R1 90% vs. 28%; R2 96% vs. 31%), sharp margin (R1 83% vs. 28%; R2 96% vs. 31%), venous expansion (R1 48% vs. 6%, R2 56% vs. 6%), and venous wall enhancement (R1 62% vs. 0%; R2 48% vs. 0%). DVT exhibited significantly lower mean attenuation than mixing artifact (R1 42.1 ± 20.2 vs. 57.1 ± 23.6 HU; R2 43.6 ± 19.4 vs. 58.8 ± 23.4 HU, p ≤ 0.031) and a significantly larger difference in vein diameter compared to the contralateral vein (R1 0.4 ± 0.4 vs. 0.1 ± 0.2 cm; R2 0.3 ± 0.4 vs. 0.0 ± 0.1 cm, p ≤ 0.026). At multivariable analysis, central location and sharp margin were significant independent predictors of DVT for both readers (p ≤ 0.013). Awareness of these qualitative and quantitative imaging features may improve radiologists' confidence for differentiating femoral vein DVT and mixing artifact on routine portal venous phase CT. However, given overlap with mixing artifact, larger studies remain warranted.

Speckle interferometry of asteroids

NASA Technical Reports Server (NTRS)

Drummond, Jack

1988-01-01

By studying the image two-dimensional power spectra or autocorrelations projected by an asteroid as it rotates, it is possible to locate its rotational pole and derive its three axes dimensions through speckle interferometry under certain assumptions of uniform, geometric scattering, and triaxial ellipsoid shape. However, in cases where images can be reconstructed, the need for making the assumptions is obviated. Furthermore, the ultimate goal for speckle interferometry of image reconstruction will lead to mapping albedo features (if they exist) as impact areas or geological units. The first glimpses of the surface of an asteroid were obtained from images of 4 Vesta reconstructed from speckle interferometric observations. These images reveal that Vesta is quite Moon-like in having large hemispheric-scale albedo features. All of its lightcurves can be produced from a simple model developed from the images. Although undoubtedly more intricate than the model, Vesta's lightcurves can be matched by a model with three dark and four bright spots. The dark areas so dominate one hemisphere that a lightcurve minimum occurs when the maximum cross-section area is visible. The triaxial ellipsoid shape derived for Vesta is not consistent with the notion that the asteroid has an equilibrium shape in spite of its having apparently been differentiated.

Toward automatic phenotyping of retinal images from genetically determined mono- and dizygotic twins using amplitude modulation-frequency modulation methods

NASA Astrophysics Data System (ADS)

Soliz, P.; Davis, B.; Murray, V.; Pattichis, M.; Barriga, S.; Russell, S.

2010-03-01

This paper presents an image processing technique for automatically categorize age-related macular degeneration (AMD) phenotypes from retinal images. Ultimately, an automated approach will be much more precise and consistent in phenotyping of retinal diseases, such as AMD. We have applied the automated phenotyping to retina images from a cohort of mono- and dizygotic twins. The application of this technology will allow one to perform more quantitative studies that will lead to a better understanding of the genetic and environmental factors associated with diseases such as AMD. A method for classifying retinal images based on features derived from the application of amplitude-modulation frequency-modulation (AM-FM) methods is presented. Retinal images from identical and fraternal twins who presented with AMD were processed to determine whether AM-FM could be used to differentiate between the two types of twins. Results of the automatic classifier agreed with the findings of other researchers in explaining the variation of the disease between the related twins. AM-FM features classified 72% of the twins correctly. Visual grading found that genetics could explain between 46% and 71% of the variance.

Early Detection of Breast Cancer by Using Handycam Camera Manipulation as Thermal Camera Imaging with Images Processing Method

NASA Astrophysics Data System (ADS)

Riantana, R.; Arie, B.; Adam, M.; Aditya, R.; Nuryani; Yahya, I.

2017-02-01

One important thing to pay attention for detecting breast cancer is breast temperature changes. Indications symptoms of breast tissue abnormalities marked by a rise in temperature of the breast. Handycam in night vision mode interferences by external infrared can penetrate into the skin better and can make an infrared image becomes clearer. The program is capable to changing images from a camcorder into a night vision thermal image by breaking RGB into Grayscale matrix structure. The matrix rearranged in the new matrix with double data type so that it can be processed into contour color chart to differentiate the distribution of body temperature. In this program are also features of contrast scale setting of the image is processed so that the color can be set as desired. There was Also a contrast adjustment feature inverse scale that is useful to reverse the color scale so that colors can be changed opposite. There is improfile function used to retrieves the intensity values of pixels along a line what we want to show the distribution of intensity in a graph of relationship between the intensity and the pixel coordinates.

Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

NASA Astrophysics Data System (ADS)

Alshehhi, Rasha; Marpu, Prashanth Reddy

2017-04-01

Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

Spectropathology-corroborated multimodal quantitative imaging biomarkers for neuroretinal degeneration in diabetic retinopathy

PubMed Central

Guha Mazumder, Arpan; Chatterjee, Swarnadip; Chatterjee, Saunak; Gonzalez, Juan Jose; Bag, Swarnendu; Ghosh, Sambuddha; Mukherjee, Anirban; Chatterjee, Jyotirmoy

2017-01-01

Introduction Image-based early detection for diabetic retinopathy (DR) needs value addition due to lack of well-defined disease-specific quantitative imaging biomarkers (QIBs) for neuroretinal degeneration and spectropathological information at the systemic level. Retinal neurodegeneration is an early event in the pathogenesis of DR. Therefore, development of an integrated assessment method for detecting neuroretinal degeneration using spectropathology and QIBs is necessary for the early diagnosis of DR. Methods The present work explored the efficacy of intensity and textural features extracted from optical coherence tomography (OCT) images after selecting a specific subset of features for the precise classification of retinal layers using variants of support vector machine (SVM). Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance (NMR) spectroscopy were also performed to confirm the spectropathological attributes of serum for further value addition to the OCT, fundoscopy, and fluorescein angiography (FA) findings. The serum metabolomic findings were also incorporated for characterizing retinal layer thickness alterations and vascular asymmetries. Results Results suggested that OCT features could differentiate the retinal lesions indicating retinal neurodegeneration with high sensitivity and specificity. OCT, fundoscopy, and FA provided geometrical as well as optical features. NMR revealed elevated levels of ribitol, glycerophosphocholine, and uridine diphosphate N-acetyl glucosamine, while the FTIR of serum samples confirmed the higher expressions of lipids and β-sheet-containing proteins responsible for neoangiogenesis, vascular fragility, vascular asymmetry, and subsequent neuroretinal degeneration in DR. Conclusion Our data indicated that disease-specific spectropathological alterations could be the major phenomena behind the vascular attenuations observed through fundoscopy and FA, as well as the variations in the intensity and textural features observed in OCT images. Finally, we propose a model that uses spectropathology corroborated with specific QIBs for detecting neuroretinal degeneration in early diagnosis of DR. PMID:29200821

Visualization and tissue classification of human breast cancer images using ultrahigh-resolution OCT.

PubMed

Yao, Xinwen; Gan, Yu; Chang, Ernest; Hibshoosh, Hanina; Feldman, Sheldon; Hendon, Christine

2017-03-01

Breast cancer is one of the most common cancers, and recognized as the third leading cause of mortality in women. Optical coherence tomography (OCT) enables three dimensional visualization of biological tissue with micrometer level resolution at high speed, and can play an important role in early diagnosis and treatment guidance of breast cancer. In particular, ultra-high resolution (UHR) OCT provides images with better histological correlation. This paper compared UHR OCT performance with standard OCT in breast cancer imaging qualitatively and quantitatively. Automatic tissue classification algorithms were used to automatically detect invasive ductal carcinoma in ex vivo human breast tissue. Human breast tissues, including non-neoplastic/normal tissues from breast reduction and tumor samples from mastectomy specimens, were excised from patients at Columbia University Medical Center. The tissue specimens were imaged by two spectral domain OCT systems at different wavelengths: a home-built ultra-high resolution (UHR) OCT system at 800 nm (measured as 2.72 μm axial and 5.52 μm lateral) and a commercial OCT system at 1,300 nm with standard resolution (measured as 6.5 μm axial and 15 μm lateral), and their imaging performances were analyzed qualitatively. Using regional features derived from OCT images produced by the two systems, we developed an automated classification algorithm based on relevance vector machine (RVM) to differentiate hollow-structured adipose tissue against solid tissue. We further developed B-scan based features for RVM to classify invasive ductal carcinoma (IDC) against normal fibrous stroma tissue among OCT datasets produced by the two systems. For adipose classification, 32 UHR OCT B-scans from 9 normal specimens, and 28 standard OCT B-scans from 6 normal and 4 IDC specimens were employed. For IDC classification, 152 UHR OCT B-scans from 6 normal and 13 IDC specimens, and 104 standard OCT B-scans from 5 normal and 8 IDC specimens were employed. We have demonstrated that UHR OCT images can produce images with better feature delineation compared with images produced by 1,300 nm OCT system. UHR OCT images of a variety of tissue types found in human breast tissue were presented. With a limited number of datasets, we showed that both OCT systems can achieve a good accuracy in identifying adipose tissue. Classification in UHR OCT images achieved higher sensitivity (94%) and specificity (93%) of adipose tissue than the sensitivity (91%) and specificity (76%) in 1,300 nm OCT images. In IDC classification, similarly, we achieved better results with UHR OCT images, featured an overall accuracy of 84%, sensitivity of 89% and specificity of 71% in this preliminary study. In this study, we provided UHR OCT images of different normal and malignant breast tissue types, and qualitatively and quantitatively studied the texture and optical features from OCT images of human breast tissue at different resolutions. We developed an automated approach to differentiate adipose tissue, fibrous stroma, and IDC within human breast tissues. Our work may open the door toward automatic intraoperative OCT evaluation of early-stage breast cancer. Lasers Surg. Med. 49:258-269, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Classical and unusual imaging appearances of melorheostosis.

PubMed

Suresh, S; Muthukumar, T; Saifuddin, A

2010-08-01

This comprehensive review will discuss the classical and unusual radiological features of melorheostosis, which is an uncommon, non-hereditary, benign, sclerosing mesodermal disease with an incidence of 0.9 cases per million. The presentation of melorheostosis in the appendicular skeleton (more commonly involved) and in the axial skeleton (very few documented case reports) will be discussed. The aim of the review is to illustrate the associations and rare, but recognized, complications of the disorder. The role of cross-sectional imaging in the form of magnetic resonance imaging (MRI) and computed tomography (CT) in revealing the spectrum of disease manifestation and differentiation from other disease entities and malignancy will be explored.

Clinical applications of textural analysis in non-small cell lung cancer.

PubMed

Phillips, Iain; Ajaz, Mazhar; Ezhil, Veni; Prakash, Vineet; Alobaidli, Sheaka; McQuaid, Sarah J; South, Christopher; Scuffham, James; Nisbet, Andrew; Evans, Philip

2018-01-01

Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed "radiomics" and includes semantic and agnostic approaches. Textural analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue subtypes of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarize the current published data and understand the potential future role of TA in managing lung cancer.

High-order statistics of weber local descriptors for image representation.

PubMed

Han, Xian-Hua; Chen, Yen-Wei; Xu, Gang

2015-06-01

Highly discriminant visual features play a key role in different image classification applications. This study aims to realize a method for extracting highly-discriminant features from images by exploring a robust local descriptor inspired by Weber's law. The investigated local descriptor is based on the fact that human perception for distinguishing a pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus. Therefore, we firstly transform the original stimulus (the images in our study) into a differential excitation-domain according to Weber's law, and then explore a local patch, called micro-Texton, in the transformed domain as Weber local descriptor (WLD). Furthermore, we propose to employ a parametric probability process to model the Weber local descriptors, and extract the higher-order statistics to the model parameters for image representation. The proposed strategy can adaptively characterize the WLD space using generative probability model, and then learn the parameters for better fitting the training space, which would lead to more discriminant representation for images. In order to validate the efficiency of the proposed strategy, we apply three different image classification applications including texture, food images and HEp-2 cell pattern recognition, which validates that our proposed strategy has advantages over the state-of-the-art approaches.

Differential diagnosis of ventriculomegaly and brainstem kinking on fetal MRI.

PubMed

Amir, Tali; Poretti, Andrea; Boltshauser, Eugen; Huisman, Thierry A G M

2016-01-01

Fetal ventriculomegaly is a common and frequently leading neuroimaging finding in complex brain malformations. Here we report on pre- and postnatal neuroimaging findings in three fetuses with prenatal ventriculomegaly and brainstem kinking. We aim to identify key neuroimaging features that may allow the prenatal differentiation between diseases associated with fetal ventriculomegaly and brainstem kinking. All pre- and postnatal magnetic resonance imaging (MRI) data were qualitatively evaluated for infra- and supratentorial abnormalities. Data about clinical features and genetic findings were collected from clinical histories. In all three patients, fetal MRI showed ventriculomegaly and brainstem kinking. In two patients, postnatal MRI also showed supratentorial migration abnormalities and eye abnormalities were found. In these children, the diagnosis of α-dystroglycanopathy was genetically confirmed. In the third patient, basal ganglia had an abnormal shape on MRI suggesting a tubulinopathy. The differential diagnosis of prenatal ventriculomegaly and brainstem kinking includes α-dystroglycanopathies, X-linked hydrocephalus due to mutations in L1CAM, and tubulinopathies. The prenatal differentiation between these diseases may be difficult. The presence of ocular abnormalities on prenatal neuroimaging may favor α-dystroglycanopathies, while dysplastic basal ganglia may suggest a tubulinopathy. However, in some patients the final differentiation between these diseases is possible only postnatally. Copyright © 2015 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Hemiconvulsion-hemiplegia-epilepsy syndrome: characteristic early magnetic resonance imaging findings.

PubMed

Freeman, Jeremy L; Coleman, Lee T; Smith, Lindsay J; Shield, Lloyd K

2002-01-01

We report three patients with hemiconvulsion-hemiplegia-epilepsy syndrome who presented acutely and were shown to have striking neuroimaging findings suggestive of diffuse cytotoxic edema confined to one hemisphere, including extensive diffusion-weighted imaging abnormalities in two cases. Two patients subsequently developed progressive and extensive atrophy of the involved hemisphere. These findings are consistent with earlier descriptions of the classic neuroradiologic features of this syndrome and are helpful in the differential diagnosis of acute infantile hemiplegia. Further, the findings support the previously proposed pathogenetic mechanism of neuronal injury caused by status epilepticus.

[Calcifying tendinitis of the rotator cuff with focal umeral osteolysis. Imaging features].

PubMed

Mascarenhas, V V; Morais, F; Marques, H; Guerra, A; Carpinteiro, E; Gaspar, A

2015-01-01

Calcifying tendinitis occurs most commonly in the rotator cuff tendons, particularly involving the supraspinatus tendon insertion, and is often asymptomatic. Cortical erosion secondary to calcifying tendinitis has been reported in multiple locations, including in the rotator cuff tendons. The authors report two cases of symptomatic calcifying tendinitis involving the infraspinatus tendon with cortical erosion with correlative radiographic, and MR findings. The importance of considering this diagnosis when evaluating lytic lesions of the humerus and the imaging differential diagnosis of calcifying tendinitis and cortical erosion are discussed.

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps.

PubMed

Sadeghi-Naini, Ali; Suraweera, Harini; Tran, William Tyler; Hadizad, Farnoosh; Bruni, Giancarlo; Rastegar, Rashin Fallah; Curpen, Belinda; Czarnota, Gregory J

2017-10-20

This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic.

Structural brain imaging correlates of ASD and ADHD across the lifespan: a hypothesis-generating review on developmental ASD-ADHD subtypes.

PubMed

Rommelse, Nanda; Buitelaar, Jan K; Hartman, Catharina A

2017-02-01

We hypothesize that it is plausible that biologically distinct developmental ASD-ADHD subtypes are present, each characterized by a distinct time of onset of symptoms, progression and combination of symptoms. The aim of the present narrative review was to explore if structural brain imaging studies may shed light on key brain areas that are linked to both ASD and ADHD symptoms and undergo significant changes during development. These findings may possibly pinpoint to brain mechanisms underlying differential developmental ASD-ADHD subtypes. To this end we brought together the literature on ASD and ADHD structural brain imaging symptoms and particularly highlight the adolescent years and beyond. Findings indicate that the vast majority of existing MRI studies has been cross-sectional and conducted in children, and sometimes did include adolescents as well, but without explicitly documenting on this age group. MRI studies documenting on age effects in adults with ASD and/or ADHD are rare, and if age is taken into account, only linear effects are examined. Data from various studies suggest that a crucial distinctive feature underlying different developmental ASD-ADHD subtypes may be the differential developmental thinning patterns of the anterior cingulate cortex and related connections towards other prefrontal regions. These regions are crucial for the development of cognitive/effortful control and socio-emotional functioning, with impairments in these features as key to both ASD and ADHD.

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.

Imaging and examination strategies of normal male and female sex development and anatomy.

PubMed

Wünsch, Lutz; Schober, Justine M

2007-09-01

Over recent years a variety of new details on the developmental biology of sexual differentiation has been discovered. Moreover, important advances have been made in imaging and examination strategies for urogenital organs, and these have added new knowledge to our understanding of the 'normal' anatomy of the sexes. Both aspects contribute to the comprehension of phenotypic sex development, but they are not commonly presented in the same context. This will be attempted in this chapter, which aims to link discoveries in developmental biology to anatomical details shown by modern examination techniques. A review of the literature concerning the link between sexual development and imaging of urogenital organs was performed. Genes, proteins and pathways related to sexual differentiation were related to some organotypic features revealed by clinical examination techniques. Early 'organotypic' patterns can be identified in prostatic, urethral and genital development and followed into postnatal life. New imaging and endoscopy techniques allow for detailed descriptive anatomical studies, hopefully resulting in a broader understanding of sex development and a better genotype-phenotype correlation in defined disorders. Clinical description relying on imaging techniques should be related to knowledge of the genetic and endocrine factors influencing sex development in a specific and stepwise manner.

Post-Mortem Magnetic Resonance Imaging Appearances of Feticide in Perinatal Deaths.

PubMed

Shelmerdine, Susan C; Hickson, Melissa; Sebire, Neil J; Arthurs, Owen J

2018-06-06

The aim of this study was to characterise the imaging features seen in fetuses having undergone feticide by intracardiac potassium chloride injection compared to those of non-terminated fetuses at post-mortem magnetic resonance imaging (PMMRI). A case-control study was performed comparing PMMRI findings between two groups of patients - those having undergone feticide were matched to a control group of miscarried/stillborn fetuses. The groups were matched according to gestational age, weight, and time since death. Two independent readers reviewed the PMMRI for thoracic, abdominal, and musculoskeletal imaging features. The Fishers exact test was conducted for differences between the patient groups. Twenty-six cases of feticide (mean gestation 25 weeks [20-36]) and 75 non-terminated fetuses (mean gestation 26.7 weeks [19-36]) were compared. There was a higher proportion of feticide cases demonstrating pneumothorax (23.1 vs. 1.3%, p = 0.001), haemothorax (42.3 vs. 4%, p = 0.001), pneumopericardium (30.8 vs. 5.3%, p = 0.002), and haemopericardium (34.6 vs. 0%, p = 0.0001). Intracardiac gas and intra-abdominal findings were higher in the feticide group, but the differences were not statistically significant. Characteristic PMMRI features of feticide can help improve reporter confidence in differentiating iatrogenic from physiological/pathological processes. © 2018 S. Karger AG, Basel.

Biometric feature embedding using robust steganography technique

NASA Astrophysics Data System (ADS)

Rashid, Rasber D.; Sellahewa, Harin; Jassim, Sabah A.

2013-05-01

This paper is concerned with robust steganographic techniques to hide and communicate biometric data in mobile media objects like images, over open networks. More specifically, the aim is to embed binarised features extracted using discrete wavelet transforms and local binary patterns of face images as a secret message in an image. The need for such techniques can arise in law enforcement, forensics, counter terrorism, internet/mobile banking and border control. What differentiates this problem from normal information hiding techniques is the added requirement that there should be minimal effect on face recognition accuracy. We propose an LSB-Witness embedding technique in which the secret message is already present in the LSB plane but instead of changing the cover image LSB values, the second LSB plane will be changed to stand as a witness/informer to the receiver during message recovery. Although this approach may affect the stego quality, it is eliminating the weakness of traditional LSB schemes that is exploited by steganalysis techniques for LSB, such as PoV and RS steganalysis, to detect the existence of secrete message. Experimental results show that the proposed method is robust against PoV and RS attacks compared to other variants of LSB. We also discussed variants of this approach and determine capacity requirements for embedding face biometric feature vectors while maintain accuracy of face recognition.

Classification of breast abnormalities using artificial neural network

NASA Astrophysics Data System (ADS)

Zaman, Nur Atiqah Kamarul; Rahman, Wan Eny Zarina Wan Abdul; Jumaat, Abdul Kadir; Yasiran, Siti Salmah

2015-05-01

Classification is the process of recognition, differentiation and categorizing objects into groups. Breast abnormalities are calcifications which are tumor markers that indicate the presence of cancer in the breast. The aims of this research are to classify the types of breast abnormalities using artificial neural network (ANN) classifier and to evaluate the accuracy performance using receiver operating characteristics (ROC) curve. The methods used in this research are ANN for breast abnormalities classifications and Canny edge detector as a feature extraction method. Previously the ANN classifier provides only the number of benign and malignant cases without providing information for specific cases. However in this research, the type of abnormality for each image can be obtained. The existing MIAS MiniMammographic database classified the mammogram images into three features only namely characteristic of background tissues, class of abnormality and radius of abnormality. However, in this research three other features are added-in. These three features are number of spots, area and shape of abnormalities. Lastly the performance of the ANN classifier is evaluated using ROC curve. It is found that ANN has an accuracy of 97.9% which is considered acceptable.

SU-F-R-21: The Stability of Radiomics Features On 4D FDG-PET/CT Images

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

Ma, C

2016-06-15

Purpose: The aim of our study was to perform a stability analysis of 4D PET-derived features in non-small cell lung carcinoma (NSCLC) based on six different respiratory phases. Methods: The 4D FDG-PET/CT respiratory phases were labeled as T0%, T17%, T33%,T50%, T67%, T83% phases, with the T0% phase approximately corresponding to the normal end-inspiration. Lesions were manually delineated based on fused PET-CT, using a standardized clinical delineation protocol. Six texture parameters were analyzed. Results: Results showed that the majority of assessed features had a low stability such as Homogeneity (0.385–0.416), Dissimilarity (3.707–3.861), Angular two moments (0.013–0.019), Contrast (39.782–49.562), Entropy(4.683–5.002) and Inversemore » differential moment (0.317–0.362) on different respiratory phases. Conclusion: This study suggest that further research of quantitative PET imaging features is warranted with respect to respiratory motion.« less

Computer-aided detection of basal cell carcinoma through blood content analysis in dermoscopy images

NASA Astrophysics Data System (ADS)

Kharazmi, Pegah; Kalia, Sunil; Lui, Harvey; Wang, Z. Jane; Lee, Tim K.

2018-02-01

Basal cell carcinoma (BCC) is the most common type of skin cancer, which is highly damaging to the skin at its advanced stages and causes huge costs on the healthcare system. However, most types of BCC are easily curable if detected at early stage. Due to limited access to dermatologists and expert physicians, non-invasive computer-aided diagnosis is a viable option for skin cancer screening. A clinical biomarker of cancerous tumors is increased vascularization and excess blood flow. In this paper, we present a computer-aided technique to differentiate cancerous skin tumors from benign lesions based on vascular characteristics of the lesions. Dermoscopy image of the lesion is first decomposed using independent component analysis of the RGB channels to derive melanin and hemoglobin maps. A novel set of clinically inspired features and ratiometric measurements are then extracted from each map to characterize the vascular properties and blood content of the lesion. The feature set is then fed into a random forest classifier. Over a dataset of 664 skin lesions, the proposed method achieved an area under ROC curve of 0.832 in a 10-fold cross validation for differentiating basal cell carcinomas from benign lesions.

Florid reactive periostitis ossificans of the humerus: Case report and differential diagnosis of periosteal lesions of long bones

PubMed Central

Soni, Abha; Weil, Alec; Wei, Shi; Jaffe, Kenneth A; Siegal, Gene P

2015-01-01

A case of florid reactive periostitis ossificans (RPO) arising in a long bone is presented. This is a rare bone proliferation with a pronounced periosteal reaction. Less than 100 cases have been described in the literature with far fewer outside the bones of the hand, feet, fingers, and toes. Although the etiology is unknown, a relationship to preceding trauma is suggested. The imaging and histologic features show an overlap with other bone lesions including bizarre parosteal osteochondromatous proliferation, subungual exostosis, and malignant surface tumors of bone and cartilage which include, periosteal and parosteal osteosarcoma. It is important to recognize the clinical presentation and diagnostic features of RPO as a benign entity so that it is not mistaken for a more aggressive neoplasm. We present a case of a right distal humeral lesion that on histopathological review revealed florid RPO. This diagnosis was not suspected on imaging studies, but was made on open biopsy of the mass. The patient remains disease free, years postoperatively. In addition to presenting this unique case report, we review the pertinent literature, and offer a differential diagnosis and treatment strategy for its management. PMID:26301184

Differences in Multi-Modal Ultrasound Imaging between Triple Negative and Non-Triple Negative Breast Cancer.

PubMed

Li, Ziyao; Tian, Jiawei; Wang, Xiaowei; Wang, Ying; Wang, Zhenzhen; Zhang, Lei; Jing, Hui; Wu, Tong

2016-04-01

The objective of this study was to identify multi-modal ultrasound imaging parameters that could potentially help to differentiate between triple negative breast cancer (TNBC) and non-TNBC. Conventional ultrasonography, ultrasound strain elastography and 3-D ultrasound (3-D-US) findings from 50 TNBC and 179 non-TNBC patients were retrospectively reviewed. Immunohistochemical examination was used as the reference gold standard for cancer subtyping. Different ultrasound modalities were initially analyzed to define TNBC-related features. Subsequently, logistic regression analysis was applied to TNBC-related features to establish models for predicting TNBC. TNBCs often presented as micro-lobulated, markedly hypo-echoic masses with an abrupt interface (p = 0.015, 0.0015 and 0.004, compared with non-TNBCs, respectively) on conventional ultrasound, and showed a diminished retraction pattern phenomenon in the coronal plane (p = 0.035) on 3-D-US. Our findings suggest that B-mode ultrasound and 3-D-US in multi-modality ultrasonography could be a useful non-invasive technique for differentiating TNBCs from non-TNBCs. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

CT and MRI imaging of the brain in MELAS syndrome

PubMed Central

Pauli, Wojciech; Zarzycki, Artur; Krzyształowski, Adam; Walecka, Anna

2013-01-01

Summary Background: MELAS syndrome (mitochondrial myopathy, encephalopathy, lactic acidosis, stroke-like episodes) is a rare, multisystem disorder which belongs to a group of mitochondrial metabolic diseases. As other diseases in this group, it is inherited in the maternal line. Case Report: In this report, we discussed a case of a 10-year-old girl with clinical and radiological picture of MELAS syndrome. We would like to describe characteristic radiological features of MELAS syndrome in CT, MRI and MR spectroscopy of the brain and differential diagnosis. Conclusions: The rarity of this disorder and the complexity of its clinical presentation make MELAS patients among the most difficult to diagnose. Brain imaging studies require a wide differential diagnosis, primarily to distinguish between MELAS and ischemic stroke. Particularly helpful are the MRI and MR spectroscopy techniques. PMID:24115962

Detection of colorectal masses in CT colonography: application of deep residual networks for differentiating masses from normal colon anatomy

NASA Astrophysics Data System (ADS)

Näppi, Janne J.; Hironaka, Toru; Yoshida, Hiroyuki

2018-02-01

Even though the clinical consequences of a missed colorectal cancer far outweigh those of a missed polyp, there has been little work on computer-aided detection (CADe) for colorectal masses in CT colonography (CTC). One of the problems is that it is not clear how to manually design mathematical image-based features that could be used to differentiate effectively between masses and certain types of normal colon anatomy such as ileocecal valves (ICVs). Deep learning has demonstrated ability to automatically determine effective discriminating features in many image-based problems. Recently, residual networks (ResNets) were developed to address the practical problems of constructing deep network architectures for optimizing the performance of deep learning. In this pilot study, we compared the classification performance of a conventional 2D-convolutional ResNet (2D-ResNet) with that of a volumetric 3D-convolutional ResNet (3D-ResNet) in differentiating masses from normal colon anatomy in CTC. For the development and evaluation of the ResNets, 695 volumetric images of biopsy-proven colorectal masses, ICVs, haustral folds, and rectal tubes were sampled from 196 clinical CTC cases and divided randomly into independent training, validation, and test datasets. The training set was expanded by use of volumetric data augmentation. Our preliminary results on the 140 test samples indicate that it is feasible to train a deep volumetric 3D-ResNet for performing effective image-based discriminations in CTC. The 3D-ResNet slightly outperformed the 2D-ResNet in the discrimination of masses and normal colon anatomy, but the statistical difference between their very high classification accuracies was not significant. The highest classification accuracy was obtained by combining the mass-likelihood estimates of the 2D- and 3D-ResNets, which enabled correct classification of all of the masses.

Application of 3D and 2D quantitative shear wave elastography (SWE) to differentiate between benign and malignant breast masses.

PubMed

Tian, Jie; Liu, Qianqi; Wang, Xi; Xing, Ping; Yang, Zhuowen; Wu, Changjun

2017-01-20

As breast cancer tissues are stiffer than normal tissues, shear wave elastography (SWE) can locally quantify tissue stiffness and provide histological information. Moreover, tissue stiffness can be observed on three-dimensional (3D) colour-coded elasticity maps. Our objective was to evaluate the diagnostic performances of quantitative features in differentiating breast masses by two-dimensional (2D) and 3D SWE. Two hundred ten consecutive women with 210 breast masses were examined with B-mode ultrasound (US) and SWE. Quantitative features of 3D and 2D SWE were assessed, including elastic modulus standard deviation (E SD E ) measured on SWE mode images and E SD U measured on B-mode images, as well as maximum elasticity (E max ). Adding quantitative features to B-mode US improved the diagnostic performance (p < 0.05) and reduced false-positive biopsies (p < 0.0001). The area under the receiver operating characteristic curve (AUC) of 3D SWE was similar to that of 2D SWE for E SD E (p = 0.026) and E SD U (p = 0.159) but inferior to that of 2D SWE for E max (p = 0.002). Compared with E SD U , E SD E showed a higher AUC on 2D (p = 0.0038) and 3D SWE (p = 0.0057). Our study indicates that quantitative features of 3D and 2D SWE can significantly improve the diagnostic performance of B-mode US, especially 3D SWE E SD E , which shows considerable clinical value.

Weber-aware weighted mutual information evaluation for infrared-visible image fusion

NASA Astrophysics Data System (ADS)

Luo, Xiaoyan; Wang, Shining; Yuan, Ding

2016-10-01

A performance metric for infrared and visible image fusion is proposed based on Weber's law. To indicate the stimulus of source images, two Weber components are provided. One is differential excitation to reflect the spectral signal of visible and infrared images, and the other is orientation to capture the scene structure feature. By comparing the corresponding Weber component in infrared and visible images, the source pixels can be marked with different dominant properties in intensity or structure. If the pixels have the same dominant property label, the pixels are grouped to calculate the mutual information (MI) on the corresponding Weber components between dominant source and fused images. Then, the final fusion metric is obtained via weighting the group-wise MI values according to the number of pixels in different groups. Experimental results demonstrate that the proposed metric performs well on popular image fusion cases and outperforms other image fusion metrics.

Dedifferentiated Chondrosarcoma of the Larynx.

PubMed

Fidai, Shiraz S; Ginat, Daniel T; Langerman, Alexander J; Cipriani, Nicole A

2016-09-01

Primary dedifferentiated chondrosarcoma occurring in the larynx is a rare head and neck malignancy. The cases reported in the literature suggest male gender predilection and variable clinical outcomes ranging from disease-free survival to disease-related death. Although a calcified matrix is suggestive of chondrosarcoma, the dedifferentiated component is not readily appreciated on conventional imaging modalities and thorough tissue sampling is necessary for confirming the diagnosis. Histologically, there is an abrupt transition from a well-differentiated chondrosarcoma to a high-grade spindle cell component, which can show focal heterologous differentiation. These features are exemplified in this sine qua non radiology-pathology correlation article.

Quantitative analysis of thyroid tumors vascularity: A comparison between 3-D contrast-enhanced ultrasound and 3-D Power Doppler on benign and malignant thyroid nodules.

PubMed

Caresio, Cristina; Caballo, Marco; Deandrea, Maurilio; Garberoglio, Roberto; Mormile, Alberto; Rossetto, Ruth; Limone, Paolo; Molinari, Filippo

2018-05-15

To perform a comparative quantitative analysis of Power Doppler ultrasound (PDUS) and Contrast-Enhancement ultrasound (CEUS) for the quantification of thyroid nodules vascularity patterns, with the goal of identifying biomarkers correlated with the malignancy of the nodule with both imaging techniques. We propose a novel method to reconstruct the vascular architecture from 3-D PDUS and CEUS images of thyroid nodules, and to automatically extract seven quantitative features related to the morphology and distribution of vascular network. Features include three tortuosity metrics, the number of vascular trees and branches, the vascular volume density, and the main spatial vascularity pattern. Feature extraction was performed on 20 thyroid lesions (ten benign and ten malignant), of which we acquired both PDUS and CEUS. MANOVA (multivariate analysis of variance) was used to differentiate benign and malignant lesions based on the most significant features. The analysis of the extracted features showed a significant difference between the benign and malignant nodules for both PDUS and CEUS techniques for all the features. Furthermore, by using a linear classifier on the significant features identified by the MANOVA, benign nodules could be entirely separated from the malignant ones. Our early results confirm the correlation between the morphology and distribution of blood vessels and the malignancy of the lesion, and also show (at least for the dataset used in this study) a considerable similarity in terms of findings of PDUS and CEUS imaging for thyroid nodules diagnosis and classification. © 2018 American Association of Physicists in Medicine.

Evaluation of feature selection algorithms for classification in temporal lobe epilepsy based on MR images

NASA Astrophysics Data System (ADS)

Lai, Chunren; Guo, Shengwen; Cheng, Lina; Wang, Wensheng; Wu, Kai

2017-02-01

It's very important to differentiate the temporal lobe epilepsy (TLE) patients from healthy people and localize the abnormal brain regions of the TLE patients. The cortical features and changes can reveal the unique anatomical patterns of brain regions from the structural MR images. In this study, structural MR images from 28 normal controls (NC), 18 left TLE (LTLE), and 21 right TLE (RTLE) were acquired, and four types of cortical feature, namely cortical thickness (CTh), cortical surface area (CSA), gray matter volume (GMV), and mean curvature (MCu), were explored for discriminative analysis. Three feature selection methods, the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM), and the support vector machine-recursive feature elimination (SVM-RFE), were investigated to extract dominant regions with significant differences among the compared groups for classification using the SVM classifier. The results showed that the SVM-REF achieved the highest performance (most classifications with more than 92% accuracy), followed by the SCDRM, and the t-test. Especially, the surface area and gray volume matter exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical features were combined. Additionally, the dominant regions with higher classification weights were mainly located in temporal and frontal lobe, including the inferior temporal, entorhinal cortex, fusiform, parahippocampal cortex, middle frontal and frontal pole. It was demonstrated that the cortical features provided effective information to determine the abnormal anatomical pattern and the proposed method has the potential to improve the clinical diagnosis of the TLE.

Visual Recognition Software for Binary Classification and Its Application to Spruce Pollen Identification

PubMed Central

Tcheng, David K.; Nayak, Ashwin K.; Fowlkes, Charless C.; Punyasena, Surangi W.

2016-01-01

Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based “pollen spotting” model to segment pollen grains from the slide background. We next tested ARLO’s ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems. PMID:26867017

A SPAD-based 3D imager with in-pixel TDC for 145ps-accuracy ToF measurement

NASA Astrophysics Data System (ADS)

Vornicu, I.; Carmona-Galán, R.; Rodríguez-Vázquez, Á.

2015-03-01

The design and measurements of a CMOS 64 × 64 Single-Photon Avalanche-Diode (SPAD) array with in-pixel Time-to-Digital Converter (TDC) are presented. This paper thoroughly describes the imager at architectural and circuit level with particular emphasis on the characterization of the SPAD-detector ensemble. It is aimed to 2D imaging and 3D image reconstruction in low light environments. It has been fabricated in a standard 0.18μm CMOS process, i. e. without high voltage or low noise features. In these circumstances, we are facing a high number of dark counts and low photon detection efficiency. Several techniques have been applied to ensure proper functionality, namely: i) time-gated SPAD front-end with fast active-quenching/recharge circuit featuring tunable dead-time, ii) reverse start-stop scheme, iii) programmable time resolution of the TDC based on a novel pseudo-differential voltage controlled ring oscillator with fast start-up, iv) a global calibration scheme against temperature and process variation. Measurements results of individual SPAD-TDC ensemble jitter, array uniformity and time resolution programmability are also provided.

Optical coherence tomography of the prostate nerves

NASA Astrophysics Data System (ADS)

Chitchian, Shahab

Preservation of the cavernous nerves during prostate cancer surgery is critical in preserving a man's ability to have spontaneous erections following surgery. These microscopic nerves course along the surface of the prostate within a few millimeters of the prostate capsule, and they vary in size and location from one patient to another, making preservation of the nerves difficult during dissection and removal of a cancerous prostate gland. These observations may explain in part the wide variability in reported sexual potency rates (9--86%) following prostate cancer surgery. Any technology capable of providing improved identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery would be of great assistance in improving sexual function after surgery, and result in direct patient benefit. Optical coherence tomography (OCT) is a noninvasive optical imaging technique capable of performing high-resolution cross-sectional in vivo and in situ imaging of microstructures in biological tissues. OCT imaging of the cavernous nerves in the rat and human prostate has recently been demonstrated. However, improvements in the OCT system and the quality of the images for identification of the cavernous nerves is necessary before clinical use. The following chapters describe complementary approaches to improving identification and imaging of the cavernous nerves during OCT of the prostate gland. After the introduction to OCT imaging of the prostate gland, the optimal wavelength for deep imaging of the prostate is studied in Chapter 2. An oblique-incidence single point measurement technique using a normal-detector scanning system was implemented to determine the absorption and reduced scattering coefficients, mua and m's , of fresh canine prostate tissue, ex vivo, from the diffuse reflectance profile of near-IR light as a function of source-detector distance. The effective attenuation coefficient, mueff, and the Optical Penetration Depth (OPD) were then calculated for near-IR wavelengths of 1064 nm, 1307 nm, and 1555 nm. Chapters 3 and 4 describe locally adaptive denoising algorithms applied to reduce speckle noise in OCT images of the prostate taken by experimental and clinical systems, respectively. The dual-tree complex wavelet transform (CDWT) is a relatively recent enhancement to the discrete wavelet transform (DWT), with important additional properties: It is nearly shift invariant and directionally selective in two and higher dimensions. The CDWT algorithm was implemented for denoising of OCT images. In Chapter 5, 2-D OCT images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. To detect these nerves, three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The Gabor feature was applied with different standard deviations in the x and y directions. In the Daubechies wavelet feature, an 8-tap Daubechies orthonormal wavelet was implemented, and the low pass sub-band was chosen as the filtered image. Finally, Laws feature extraction was applied to the images. The features were segmented using a nearest-neighbor classifier. Morphological post-processing was used to remove small voids. In Chapter 6, a new algorithm based on thresholding and first-order derivative class of differential edge detection was implemented to see deeper in the OCT images. One of the main limitations in OCT imaging of the prostate tissue is the inability to image deep into opaque tissues. Currently, OCT is limited to an image depth of approximately 1 min in opaque tissues. Theoretical comparisons of detection performance for Fourier domain (FD) and time domain (TD) OCT have been previously reported. In Chapter 7, we compare several image quality metrics including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and equivalent number of looks (ENL) for TD-OCT and FD-OCT images taken of the rat prostate, in vivo. The results show that TD-OCT has inferior CNR, but superior SNR compared to FD-OCT, and that TD-OCT is better for deep imaging of opaque tissues. Finally, Chapter 8 summarizes the study and future directions for OCT imaging of the prostate gland are discussed.

Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Senthilkumar, T.; Jayas, D. S.; White, N. D. G.; Fields, P. G.; Gräfenhan, T.

2017-03-01

Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns.

Role of apparent diffusion coefficients with diffusion-weighted magnetic resonance imaging in differentiating between benign and malignant bone tumors.

PubMed

Wang, Tingting; Wu, Xiangru; Cui, Yanfen; Chu, Caiting; Ren, Gang; Li, Wenhua

2014-11-29

Benign and malignant bone tumors can present similar imaging features. This study aims to evaluate the significance of apparent diffusion coefficients (ADC) in differentiating between benign and malignant bone tumors. A total of 187 patients with 198 bone masses underwent diffusion-weighted (DW) magnetic resonance (MR) imaging. The ADC values in the solid components of the bone masses were assessed. Statistical differences between the mean ADC values in the different tumor types were determined by Student's t-test. Histological analysis showed that 84/198 (42.4%) of the bone masses were benign and 114/198 (57.6%) were malignant. There was a significant difference between the mean ADC values in the benign and malignant bone lesions (P<0.05). However, no significant difference was found in the mean ADC value between non-ossifying fibromas, osteofibrous dysplasia, and malignant bone tumors. When an ADC cutoff value≥1.10×10(-3) mm2/s was applied, malignant bone lesions were excluded with a sensitivity of 89.7%, a specificity of 84.5%, a positive predictive value of 82.6%, and a negative predictive value of 95.3%. The combination of DW imaging with ADC quantification and T2-weighted signal characteristics of the solid components in lesions can facilitate differentiation between benign and malignant bone tumors.

The utility of Skylab photo-interpreted earth resources data in studies of marine geology and coastal processes in Puerto Rico and the Virgin Islands

NASA Technical Reports Server (NTRS)

Trumbull, J. V. A. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Three Skylab earth resources passes over Puerto Rico and St. Croix on 6 June and 30 November 1973 and 18 January 1974 resulted in color photography and multispectral photography and scanner imagery. Bathymetric and turbid water features are differentiable by use of the multispectral data. Photography allows mapping of coral reefs, offshore sand deposits, areas of coastal erosion, and patterns of sediment transport. Bottom sediment types could not be differentiated. Patterns of bottom dwelling biologic communities are well portrayed but are difficult to differentiate from bathymetric detail. Effluent discharges and oil slicks are readily detected and are differentiated from other phenomena by the persistence of their images into the longer wavelength multispectral bands.

Detailed magnetic resonance imaging features of a case series of primary gliosarcoma.

PubMed

Sampaio, Luísa; Linhares, Paulo; Fonseca, José

2017-12-01

Objective We aimed to characterise the magnetic resonance imaging (MRI) features of a case series of primary gliosarcoma, with the inclusion of diffusion-weighted imaging and perfusion imaging with dynamic susceptibility contrast MRI. Materials and methods We conducted a retrospective study of cases of primary gliosarcoma from the Pathology Department database from January 2006 to December 2014. Clinical and demographic data were obtained. Two neuroradiologists, blinded to diagnosis, assessed tumour location, signal intensity in T1 and T2-weighted images, pattern of enhancement, diffusion-weighted imaging and dynamic susceptibility contrast MRI studies on preoperative MRI. Results Seventeen patients with primary gliosarcomas had preoperative MRI study: seven men and 10 women, with a mean age of 59 years (range 27-74). All lesions were well demarcated, supratentorial and solitary (frontal n = 5, temporal n = 4, parietal n = 3); 13 tumours abutted the dural surface (8/13 with dural enhancement); T1 and T2-weighted imaging patterns were heterogeneous and the majority of lesions (12/17) showed a rim-like enhancement pattern with focal nodularities/irregular thickness. Restricted diffusion (mean apparent diffusion coefficient values 0.64 × 10 -3 mm 2 /s) in the more solid/thick components was present in eight out of 11 patients with diffusion-weighted imaging study. Dynamic susceptibility contrast MRI study ( n = 8) consistently showed hyperperfusion in non-necrotic/cystic components on relative cerebral volume maps. Conclusions The main distinguishing features of primary gliosarcoma are supratentorial and peripheral location, well-defined boundaries and a rim-like pattern of enhancement with an irregular thick wall. Diffusion-weighted imaging and relative cerebral volume map analysis paralleled primary gliosarcoma with high-grade gliomas, thus proving helpful in differential diagnosis.

Imaging and Clinicopathologic Features of Esophageal Gastrointestinal Stromal Tumors

PubMed Central

Winant, Abbey J.; Gollub, Marc J.; Shia, Jinru; Antonescu, Christina; Bains, Manjit S.; Levine, Marc S.

2016-01-01

OBJECTIVE The purpose of this article is to describe the imaging and clinicopathologic characteristics of esophageal gastrointestinal stromal tumors (GISTs) and to emphasize the features that differentiate esophageal GISTs from esophageal leiomyomas. MATERIALS AND METHODS A pathology database search identified all surgically resected or biopsied esophageal GISTs, esophageal leiomyomas, and esophageal leiomyosarcomas from 1994 to 2012. Esophageal GISTs were included only if imaging studies (including CT, fluoroscopic, or 18F-FDG PET/CT scans) and clinical data were available. RESULTS Nineteen esophageal mesenchymal tumors were identified, including eight esophageal GISTs (42%), 10 esophageal leiomyomas (53%), and one esophageal leiomyosarcoma (5%). Four patients (50%) with esophageal GIST had symptoms, including dysphagia in three (38%), cough in one (13%), and chest pain in one (13%). One esophageal GIST appeared on barium study as a smooth submucosal mass. All esophageal GISTs appeared on CT as well-marginated predominantly distal lesions, isoattenuating to muscle, that moderately enhanced after IV contrast agent administration. Compared with esophageal leiomyomas, esophageal GISTs tended to be more distal, larger, and more heterogeneous and showed greater IV enhancement on CT. All esophageal GISTs showed marked avidity (mean maximum standardized uptake value, 16) on PET scans. All esophageal GISTs were positive for c-KIT (a cell-surface transmembrane tyrosine kinase also known as CD117) and CD34. On histopathology, six esophageal GISTs (75%) were of the spindle pattern and two (25%) were of a mixed spindle and epithelioid pattern. Five esophageal GISTs had exon 11 mutations (with imatinib sensitivity). Clinical outcome correlated with treatment strategy (resection plus adjuvant therapy or resection alone) rather than risk stratification. CONCLUSION Esophageal GISTs are unusual but clinically important mesenchymal neoplasms. Although esophageal GISTs and esophageal leiomyomas had overlapping imaging features, esophageal GISTs tended to be more distal, larger, more heterogeneous, and more enhancing on CT and were markedly FDG avid on PET. Given their malignant potential, esophageal GISTs should be included in the differential diagnosis of intramural esophageal neoplasms. PMID:25055264

Superior cervical ganglion mimicking retropharyngeal adenopathy in head and neck cancer patients: MRI features with anatomic, histologic, and surgical correlation.

PubMed

Loke, S C; Karandikar, A; Ravanelli, M; Farina, D; Goh, J P N; Ling, E A; Maroldi, R; Tan, T Y

2016-01-01

To describe the unique MRI findings of superior cervical ganglia (SCG) that may help differentiate them from retropharyngeal lymph nodes (RPLNs). A retrospective review of post-treatment NPC patients from 1999 to 2012 identified three patients previously irradiated for NPC that were suspected of having recurrent nodal disease in retropharyngeal lymph nodes during surveillance MRI. Subsequent surgical exploration revealed enlarged SCG only; no retropharyngeal nodal disease was found. A cadaveric head specimen was also imaged with a 3T MRI before and after dissection. In addition, SCG were also harvested from three cadaveric specimens and subjected to histologic analysis. The SCG were found at the level of the C2 vertebral body, medial to the ICA. They were ovoid on axial images and fusiform and elongated with tapered margins in the coronal plane. T2-weighted (T2W) signal was hyperintense. No central elevated T1-weighted (T1W) signal was seen within the ganglia in non-fat-saturated sequences to suggest the presence of a fatty hilum. Enhancement after gadolinium was present. A central "black dot" was seen on axial T2W and post-contrast images in two of the three SCG demonstrated. Histology showed the central black line was comprised of venules and interlacing neurites within the central portion of the ganglion. The SCG can be mistaken for enlarged RPLNs in post-treatment NPC patients. However, there are features which can help differentiate them from RPLNs, preventing unnecessary therapy. These imaging findings have not been previously described.

Extramedullary haematopoiesis: radiological imaging features.

PubMed

Roberts, A S; Shetty, A S; Mellnick, V M; Pickhardt, P J; Bhalla, S; Menias, C O

2016-09-01

Extramedullary haematopoiesis (EMH) is defined as the production of blood cells outside of the bone marrow, which occurs when there is inadequate production of blood cells. The most common causes of EMH are myelofibrosis, diffuse osseous metastatic disease replacing the bone marrow, leukaemia, sickle cell disease, and thalassemia. The purpose of this article is to review the common and uncommon imaging appearances of EMH by anatomical compartment. In the thorax, EMH most commonly presents as paravertebral fat-containing masses, and typically does not present a diagnostic dilemma; however, EMH in the abdomen most commonly manifests as hepatosplenomegaly with or without focal soft-tissue masses in the liver, spleen, perirenal space, and in the peritoneum. Hepatosplenomegaly, a non-specific feature, most often occurs without an associated focal mass, which makes suggestion of EMH difficult. EMH manifesting as visceral soft-tissue masses often requires biopsy as the differential diagnosis can include lymphoma, metastatic disease, and sarcoma. Many of these soft-tissue masses do not contain adipose elements, making the diagnosis of EMH difficult. Clinical history is crucial, as EMH would likely not otherwise be in the differential in patients with non-specific abdominal masses. Careful biopsy planning is necessary when EMH is a diagnostic consideration, given the propensity for haemorrhage. Understanding the typical imaging appearances of EMH based on its site of manifestation can help the radiologist when encountered with a finding that is diagnostic for EMH, and can help the radiologist suggest the need and plan appropriately for image-guided biopsy. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Cranio-orbital primary intraosseous haemangioma

PubMed Central

Gupta, T; Rose, G E; Manisali, M; Minhas, P; Uddin, J M; Verity, D H

2013-01-01

Purpose Primary intraosseous haemangioma (IOH) is a rare benign neoplasm presenting in the fourth and fifth decades of life. The spine and skull are the most commonly involved, orbital involvement is extremely rare. We describe six patients with cranio-orbital IOH, the largest case series to date. Patients and methods Retrospective review of six patients with histologically confirmed primary IOH involving the orbit. Clinical characteristics, imaging features, approach to management, and histopathological findings are described. Results Five patients were male with a median age of 56. Pain and diplopia were the most common presenting features. A characteristic ‘honeycomb' pattern on CT imaging was demonstrated in three of the cases. Complete surgical excision was performed in all cases with presurgical embolisation carried out in one case. In all the cases, histological studies identified cavernous vascular spaces within the bony tissue. These channels were lined by single layer of cytologically normal endothelial cells. Discussion IOCH of the cranio-orbital region is rare; in the absence of typical imaging features, the differential diagnosis includes chondroma, chondrosarcoma, bony metastasis, and lymphoma. Surgical excision may be necessary to exclude more sinister pathology. Intraoperative haemorrhage can be severe and may be reduced by preoperative embolisation. PMID:23989119

Quantitatively characterizing the microstructural features of breast ductal carcinoma tissues in different progression stages by Mueller matrix microscope.

PubMed

Dong, Yang; Qi, Ji; He, Honghui; He, Chao; Liu, Shaoxiong; Wu, Jian; Elson, Daniel S; Ma, Hui

2017-08-01

Polarization imaging has been recognized as a potentially powerful technique for probing the microstructural information and optical properties of complex biological specimens. Recently, we have reported a Mueller matrix microscope by adding the polarization state generator and analyzer (PSG and PSA) to a commercial transmission-light microscope, and applied it to differentiate human liver and cervical cancerous tissues with fibrosis. In this paper, we apply the Mueller matrix microscope for quantitative detection of human breast ductal carcinoma samples at different stages. The Mueller matrix polar decomposition and transformation parameters of the breast ductal tissues in different regions and at different stages are calculated and analyzed. For more quantitative comparisons, several widely-used image texture feature parameters are also calculated to characterize the difference in the polarimetric images. The experimental results indicate that the Mueller matrix microscope and the polarization parameters can facilitate the quantitative detection of breast ductal carcinoma tissues at different stages.

Imaging the morphological change of tissue structure during the early phase of esophageal tumor progression using multiphoton microscopy

NASA Astrophysics Data System (ADS)

Xu, Jian; Kang, Deyong; Xu, Meifang; Zhu, Xiaoqin; Zhuo, Shuangmu; Chen, Jianxin

2012-12-01

Esophageal cancer is a common malignancy with a very poor prognosis. Successful strategies for primary prevention and early detection are critically needed to control this disease. Multiphoton microscopy (MPM) is becoming a novel optical tool of choice for imaging tissue architecture and cellular morphology by two-photon excited fluorescence. In this study, we used MPM to image microstructure of human normal esophagus, carcinoma in situ (CIS), and early invasive carcinoma in order to establish the morphological features to differentiate these tissues. The diagnostic features such as the appearance of cancerous cells, the significant loss of stroma, the absence of the basement membrane were extracted to distinguish between normal and cancerous esophagus tissue. These results correlated well with the paired histological findings. With the advancement of clinically miniaturized MPM and the multi-photon probe, combining MPM with standard endoscopy will therefore allow us to make a real-time in vivo diagnosis of early esophageal cancer at the cellular level.

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

PubMed

Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

2017-11-13

Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 < 0.001). The AUCs were 0.84 (95% CI 0.78-0.89) for deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

Computed tomography and magnetic resonance imaging findings of intraorbital granular cell tumor (Abrikossoff's tumor): a case report.

PubMed

Yuan, Wei-Hsin; Lin, Tai-Chi; Lirng, Jiing-Feng; Guo, Wan-You; Chang, Fu-Pang; Ho, Donald Ming-Tak

2016-05-13

Granular cell tumors are rare neoplasms which can occur in any part of the body. Granular cell tumors of the orbit account for only 3 % of all granular cell tumor cases. Computed tomography and magnetic resonance imaging of the orbit have proven useful for diagnosing orbital tumors. However, the rarity of intraorbital granular cell tumors poses a significant diagnostic challenge for both clinicians and radiologists. We report a case of a 37-year-old Chinese woman with a rare intraocular granular cell tumor of her right eye presenting with diplopia, proptosis, and restriction of ocular movement. Preoperative orbital computed tomography and magnetic resonance imaging with contrast enhancement revealed an enhancing solid, ovoid, well-demarcated, retrobulbar nodule. In addition, magnetic resonance imaging features included an intraorbital tumor which was isointense relative to gray matter on T1-weighted imaging and hypointense on T2-weighted imaging. No diffusion restriction of water was noted on either axial diffusion-weighted images or apparent diffusion coefficient maps. Both computed tomography and magnetic resonance imaging features suggested an intraorbital hemangioma. However, postoperative pathology (together with immunohistochemistry) identified an intraorbital granular cell tumor. When intraorbital T2 hypointensity and free diffusion of water are observed on magnetic resonance imaging, a granular cell tumor should be included in the differential diagnosis of an intraocular tumor.

Computer-aided classification of lung nodules on computed tomography images via deep learning technique

PubMed Central

Hua, Kai-Lung; Hsu, Che-Hao; Hidayati, Shintami Chusnul; Cheng, Wen-Huang; Chen, Yu-Jen

2015-01-01

Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain. PMID:26346558

Phenotype detection in morphological mutant mice using deformation features.

PubMed

Roy, Sharmili; Liang, Xi; Kitamoto, Asanobu; Tamura, Masaru; Shiroishi, Toshihiko; Brown, Michael S

2013-01-01

Large-scale global efforts are underway to knockout each of the approximately 25,000 mouse genes and interpret their roles in shaping the mammalian embryo. Given the tremendous amount of data generated by imaging mutated prenatal mice, high-throughput image analysis systems are inevitable to characterize mammalian development and diseases. Current state-of-the-art computational systems offer only differential volumetric analysis of pre-defined anatomical structures between various gene-knockout mice strains. For subtle anatomical phenotypes, embryo phenotyping still relies on the laborious histological techniques that are clearly unsuitable in such big data environment. This paper presents a system that automatically detects known phenotypes and assists in discovering novel phenotypes in muCT images of mutant mice. Deformation features obtained from non-linear registration of mutant embryo to a normal consensus average image are extracted and analyzed to compute phenotypic and candidate phenotypic areas. The presented system is evaluated using C57BL/10 embryo images. All cases of ventricular septum defect and polydactyly, well-known to be present in this strain, are successfully detected. The system predicts potential phenotypic areas in the liver that are under active histological evaluation for possible phenotype of this mouse line.

Computer-aided classification of lung nodules on computed tomography images via deep learning technique.

PubMed

Hua, Kai-Lung; Hsu, Che-Hao; Hidayati, Shintami Chusnul; Cheng, Wen-Huang; Chen, Yu-Jen

2015-01-01

Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain.

Fuzzy similarity measures for ultrasound tissue characterization

NASA Astrophysics Data System (ADS)

Emara, Salem M.; Badawi, Ahmed M.; Youssef, Abou-Bakr M.

1995-03-01

Computerized ultrasound tissue characterization has become an objective means for diagnosis of diseases. It is difficult to differentiate diffuse liver diseases, namely cirrhotic and fatty liver from a normal one, by visual inspection from the ultrasound images. The visual criteria for differentiating diffused diseases is rather confusing and highly dependent upon the sonographer's experience. The need for computerized tissue characterization is thus justified to quantitatively assist the sonographer for accurate differentiation and to minimize the degree of risk from erroneous interpretation. In this paper we used the fuzzy similarity measure as an approximate reasoning technique to find the maximum degree of matching between an unknown case defined by a feature vector and a family of prototypes (knowledge base). The feature vector used for the matching process contains 8 quantitative parameters (textural, acoustical, and speckle parameters) extracted from the ultrasound image. The steps done to match an unknown case with the family of prototypes (cirr, fatty, normal) are: Choosing the membership functions for each parameter, then obtaining the fuzzification matrix for the unknown case and the family of prototypes, then by the linguistic evaluation of two fuzzy quantities we obtain the similarity matrix, then by a simple aggregation method and the fuzzy integrals we obtain the degree of similarity. Finally, we find that the similarity measure results are comparable to the neural network classification techniques and it can be used in medical diagnosis to determine the pathology of the liver and to monitor the extent of the disease.

Differentiating Immunoglobulin G4-Related Sclerosing Cholangitis from Hilar Cholangiocarcinoma

PubMed Central

Tabata, Taku; Hara, Seiichi; Kuruma, Sawako; Chiba, Kazuro; Kuwata, Go; Fujiwara, Takashi; Egashira, Hideto; Koizumi, Koichi; Fujiwara, Junko; Arakawa, Takeo; Momma, Kumiko; Kurata, Masanao; Honda, Goro; Tsuruta, Koji; Itoi, Takao

2013-01-01

Background/Aims Few studies have differentiated immunoglobulin G (IgG) 4-related sclerosing cholangitis (IgG4-SC) from hilar cholangiocarcinoma (CC). Thus, we sought to investigate useful features for differentiating IgG4-SC from hilar CC. Methods We retrospectively compared clinical, serological, imaging, and histological features of six patients with IgG4-SC and 42 patients with hilar CC. Results In patients with hilar CC, obstructive jaundice was more frequent (p<0.01), serum total bilirubin levels were significantly higher (p<0.05), serum CA19-9 levels were significantly higher (p<0.01), and serum duke pancreatic monoclonal antigen type 2 levels were frequently elevated (p<0.05). However, in patients with IgG4-SC, the serum IgG (p<0.05) and IgG4 (p<0.01) levels were significantly higher and frequently elevated. The pancreas was enlarged in all IgG4-SC patients but only in 17% of hilar CC patients (p<0.01). Salivary and/or lacrimal gland swelling was detected in only 50% of IgG4-SC patients (p<0.01). Endoscopic retrograde cholangiography revealed that the hilar or hepatic duct was completely obstructed in 83% of hilar CC patients (p<0.01). Lower bile duct stenosis, apart from hilar bile duct stenosis, was more frequent in IgG4-SC patients (p<0.01). Bile duct wall thickening in areas without stenosis was more frequent in IgG4-SC patients (p<0.01). Conclusions An integrated diagnostic approach based on clinical, serological, imaging, and histological findings is necessary to differentiate IgG4-SC from hilar CC. PMID:23560161

Differential effects of human SP-A1 and SP-A2 variants on phospholipid monolayers containing surfactant protein B

PubMed Central

Wang, Guirong; Taneva, Svetla; Keough, Kevin M.W.; Floros, Joanna

2010-01-01

Summary Surfactant protein A (SP-A), the most abundant protein in the lung alveolar surface, has multiple activities, including surfactant-related functions. SP-A is required for the formation of tubular myelin and the lung surface film. The human SP-A locus consists of two functional SP-A genes, SP-A1 and SP-A2, with a number of alleles characterized for each gene. We have found that the human in vitro expressed variants, SP-A1 (6A2) and SP-A2 (1A0), and the coexpressed SP-A1/SP-A2 (6A2/1A0) protein have a differential influence on the organization of phospholipid monolayers containing surfactant protein B (SP-B). Lipid films containing SP-B and SP-A2 (1A0) showed surface features similar to those observed in lipid films with SP-B and native human SP-A. Fluorescence images revealed the presence of characteristic fluorescent probe-excluding clusters coexisting with the traditional lipid liquid-expanded and liquid-condensed phase. Images of the films containing SP-B and SP-A1 (6A2) showed different distribution of the proteins. The morphology of lipid films containing SP-B and the coexpressed SP-A1/SP-A2 (6A2/1A0) combined features of the individual films containing the SP-A1 or SP-A2 variant. The results indicate that human SP-A1 and SP-A2 variants exhibit differential effects on characteristics of phospholipid monolayers containing SP-B. This may differentially impact surface film activity. PMID:17678872

Plantar fascia segmentation and thickness estimation in ultrasound images.

PubMed

Boussouar, Abdelhafid; Meziane, Farid; Crofts, Gillian

2017-03-01

Ultrasound (US) imaging offers significant potential in diagnosis of plantar fascia (PF) injury and monitoring treatment. In particular US imaging has been shown to be reliable in foot and ankle assessment and offers a real-time effective imaging technique that is able to reliably confirm structural changes, such as thickening, and identify changes in the internal echo structure associated with diseased or damaged tissue. Despite the advantages of US imaging, images are difficult to interpret during medical assessment. This is partly due to the size and position of the PF in relation to the adjacent tissues. It is therefore a requirement to devise a system that allows better and easier interpretation of PF ultrasound images during diagnosis. This study proposes an automatic segmentation approach which for the first time extracts ultrasound data to estimate size across three sections of the PF (rearfoot, midfoot and forefoot). This segmentation method uses artificial neural network module (ANN) in order to classify small overlapping patches as belonging or not-belonging to the region of interest (ROI) of the PF tissue. Features ranking and selection techniques were performed as a post-processing step for features extraction to reduce the dimension and number of the extracted features. The trained ANN classifies the image overlapping patches into PF and non-PF tissue, and then it is used to segment the desired PF region. The PF thickness was calculated using two different methods: distance transformation and area-length calculation algorithms. This new approach is capable of accurately segmenting the PF region, differentiating it from surrounding tissues and estimating its thickness. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Distributed encoding of spatial and object categories in primate hippocampal microcircuits

PubMed Central

Opris, Ioan; Santos, Lucas M.; Gerhardt, Greg A.; Song, Dong; Berger, Theodore W.; Hampson, Robert E.; Deadwyler, Sam A.

2015-01-01

The primate hippocampus plays critical roles in the encoding, representation, categorization and retrieval of cognitive information. Such cognitive abilities may use the transformational input-output properties of hippocampal laminar microcircuitry to generate spatial representations and to categorize features of objects, images, and their numeric characteristics. Four nonhuman primates were trained in a delayed-match-to-sample (DMS) task while multi-neuron activity was simultaneously recorded from the CA1 and CA3 hippocampal cell fields. The results show differential encoding of spatial location and categorization of images presented as relevant stimuli in the task. Individual hippocampal cells encoded visual stimuli only on specific types of trials in which retention of either, the Sample image, or the spatial position of the Sample image indicated at the beginning of the trial, was required. Consistent with such encoding, it was shown that patterned microstimulation applied during Sample image presentation facilitated selection of either Sample image spatial locations or types of images, during the Match phase of the task. These findings support the existence of specific codes for spatial and numeric object representations in primate hippocampus which can be applied on differentially signaled trials. Moreover, the transformational properties of hippocampal microcircuitry, together with the patterned microstimulation are supporting the practical importance of this approach for cognitive enhancement and rehabilitation, needed for memory neuroprosthetics. PMID:26500473

Use of acoustic classification of sidescan sonar data for mapping benthic habitat in the Northern Channel Islands, California

USGS Publications Warehouse

Cochrane, Guy R.; Lafferty, Kevin D.

2002-01-01

Highly reflective seafloor features imaged by sidescan sonar in nearshore waters off the Northern Channel Islands (California, USA) have been observed in subsequent submersible dives to be areas of thin sand covering bedrock. Adjacent areas of rocky seafloor, suitable as habitat for endangered species of abalone and rockfish, and encrusting organisms, cannot be differentiated from the areas of thin sand on the basis of acoustic backscatter (i.e. grey level) alone. We found second-order textural analysis of sidescan sonar data useful to differentiate the bottom types where data is not degraded by near-range distortion (caused by slant-range and ground-range corrections), and where data is not degraded by far-range signal attenuation. Hand editing based on submersible observations is necessary to completely convert the sidescan sonar image to a bottom character classification map suitable for habitat mapping.

A hybrid scanning force and light microscope for surface imaging and three-dimensional optical sectioning in differential interference contrast.

PubMed

Stemmer, A

1995-04-01

The design of a scanned-cantilever-type force microscope is presented which is fully integrated into an inverted high-resolution video-enhanced light microscope. This set-up allows us to acquire thin optical sections in differential interference contrast (DIC) or polarization while the force microscope is in place. Such a hybrid microscope provides a unique platform to study how cell surface properties determine, or are affected by, the three-dimensional dynamic organization inside the living cell. The hybrid microscope presented in this paper has proven reliable and versatile for biological applications. It is the only instrument that can image a specimen by force microscopy and high-power DIC without having either to translate the specimen or to remove the force microscope. Adaptation of the design features could greatly enhance the suitability of other force microscopes for biological work.

Probing the limits of the rigid-intensity-shift model in differential-phase-contrast scanning transmission electron microscopy

NASA Astrophysics Data System (ADS)

Clark, L.; Brown, H. G.; Paganin, D. M.; Morgan, M. J.; Matsumoto, T.; Shibata, N.; Petersen, T. C.; Findlay, S. D.

2018-04-01

The rigid-intensity-shift model of differential-phase-contrast imaging assumes that the phase gradient imposed on the transmitted probe by the sample causes the diffraction pattern intensity to shift rigidly by an amount proportional to that phase gradient. This behavior is seldom realized exactly in practice. Through a combination of experimental results, analytical modeling and numerical calculations, using as case studies electron microscope imaging of the built-in electric field in a p-n junction and nanoscale domains in a magnetic alloy, we explore the breakdown of rigid-intensity-shift behavior and how this depends on the magnitude of the phase gradient and the relative scale of features in the phase profile and the probe size. We present guidelines as to when the rigid-intensity-shift model can be applied for quantitative phase reconstruction using segmented detectors, and propose probe-shaping strategies to further improve the accuracy.

Comparison of different numerical treatments for x-ray phase tomography of soft tissue from differential phase projections

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

Pelliccia, Daniele; Vaz, Raquel; Svalbe, Imants

X-ray imaging of soft tissue is made difficult by their low absorbance. The use of x-ray phase imaging and tomography can significantly enhance the detection of these tissues and several approaches have been proposed to this end. Methods such as analyzer-based imaging or grating interferometry produce differential phase projections that can be used to reconstruct the 3D distribution of the sample refractive index. We report on the quantitative comparison of three different methods to obtain x-ray phase tomography with filtered back-projection from differential phase projections in the presence of noise. The three procedures represent different numerical approaches to solve themore » same mathematical problem, namely phase retrieval and filtered back-projection. It is found that obtaining individual phase projections and subsequently applying a conventional filtered back-projection algorithm produces the best results for noisy experimental data, when compared with other procedures based on the Hilbert transform. The algorithms are tested on simulated phantom data with added noise and the predictions are confirmed by experimental data acquired using a grating interferometer. The experiment is performed on unstained adult zebrafish, an important model organism for biomedical studies. The method optimization described here allows resolution of weak soft tissue features, such as muscle fibers.« less

On-the-spot lung cancer differential diagnosis by label-free, molecular vibrational imaging and knowledge-based classification

NASA Astrophysics Data System (ADS)

Gao, Liang; Li, Fuhai; Thrall, Michael J.; Yang, Yaliang; Xing, Jiong; Hammoudi, Ahmad A.; Zhao, Hong; Massoud, Yehia; Cagle, Philip T.; Fan, Yubo; Wong, Kelvin K.; Wang, Zhiyong; Wong, Stephen T. C.

2011-09-01

We report the development and application of a knowledge-based coherent anti-Stokes Raman scattering (CARS) microscopy system for label-free imaging, pattern recognition, and classification of cells and tissue structures for differentiating lung cancer from non-neoplastic lung tissues and identifying lung cancer subtypes. A total of 1014 CARS images were acquired from 92 fresh frozen lung tissue samples. The established pathological workup and diagnostic cellular were used as prior knowledge for establishment of a knowledge-based CARS system using a machine learning approach. This system functions to separate normal, non-neoplastic, and subtypes of lung cancer tissues based on extracted quantitative features describing fibrils and cell morphology. The knowledge-based CARS system showed the ability to distinguish lung cancer from normal and non-neoplastic lung tissue with 91% sensitivity and 92% specificity. Small cell carcinomas were distinguished from nonsmall cell carcinomas with 100% sensitivity and specificity. As an adjunct to submitting tissue samples to routine pathology, our novel system recognizes the patterns of fibril and cell morphology, enabling medical practitioners to perform differential diagnosis of lung lesions in mere minutes. The demonstration of the strategy is also a necessary step toward in vivo point-of-care diagnosis of precancerous and cancerous lung lesions with a fiber-based CARS microendoscope.

Differentiating neuromyelitis optica from other causes of longitudinally extensive transverse myelitis on spinal magnetic resonance imaging

PubMed Central

Pekcevik, Yeliz; Mitchell, Charles H; Mealy, Maureen A; Orman, Gunes; Lee, In H; Newsome, Scott D; Thompson, Carol B; Pardo, Carlos A; Calabresi, Peter A; Levy, Michael; Izbudak, Izlem

2016-01-01

Background Although spinal magnetic resonance imaging (MRI) findings of neuromyelitis optica (NMO) have been described, there is limited data available that help differentiate NMO from other causes of longitudinally extensive transverse myelitis (LETM). Objective To investigate the spinal MRI findings of LETM that help differentiate NMO at the acute stage from multiple sclerosis (MS) and other causes of LETM. Methods We enrolled 94 patients with LETM into our study. Bright spotty lesions (BSL), the lesion distribution and location were evaluated on axial T2-weighted images. Brainstem extension, cord expansion, T1 darkness and lesion enhancement were noted. We also reviewed the brain MRI of the patients during LETM. Results Patients with NMO had a greater amount of BSL and T1 dark lesions (p < 0.001 and 0.003, respectively). The lesions in NMO patients were more likely to involve greater than one-half of the spinal cord’s cross-sectional area; to enhance and be centrally-located, or both centrally- and peripherally-located in the cord. Of the 62 available brain MRIs, 14 of the 27 whom were NMO patients had findings that may be specific to NMO. Conclusions Certain spinal cord MRI features are more commonly seen in NMO patients and so obtaining brain MRI during LETM may support diagnosis. PMID:26209588

Non Lipomatous Benign Lesions Mimicking Soft-tissue Sarcomas: A Pictorial Essay.

PubMed

Coran, Alessandro; Orsatti, Giovanna; Crimì, Filippo; Rastrelli, Marco; DI Maggio, Antonio; Ponzoni, Alberto; Attar, Shady; Stramare, Roberto

2018-01-01

The incidental finding of soft tissue masses is a challenge for the radiologist. Benign and malignant lesions can be differentiated relying on patient history, symptoms and mostly with the help of imaging. Ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI) become fundamental in order to distinguish these lesions but the radiologist needs to know the main characteristics of benign soft tissue masses and sarcomas. Herein, we present a pictorial review of lesions mimicking soft tissue sarcomas features. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.

PubMed

Citak-Er, Fusun; Firat, Zeynep; Kovanlikaya, Ilhami; Ture, Ugur; Ozturk-Isik, Esin

2018-06-15

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort. Copyright © 2018. Published by Elsevier Ltd.

Imaging Active Giants and Comparisons to Doppler Imaging

NASA Astrophysics Data System (ADS)

Roettenbacher, Rachael

2018-04-01

In the outer layers of cool, giant stars, stellar magnetism stifles convection creating localized starspots, analogous to sunspots. Because they frequently cover much larger regions of the stellar surface than sunspots, starspots of giant stars have been imaged using a variety of techniques to understand, for example, stellar magnetism, differential rotation, and spot evolution. Active giants have been imaged using photometric, spectroscopic, and, only recently, interferometric observations. Interferometry has provided a way to unambiguously see stellar surfaces without the degeneracies experienced by other methods. The only facility presently capable of obtaining the sub-milliarcsecond resolution necessary to not only resolve some giant stars, but also features on their surfaces is the Center for High-Angular Resolution Astronomy (CHARA) Array. Here, an overview will be given of the results of imaging active giants and details on the recent comparisons of simultaneous interferometric and Doppler images.

Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis.

PubMed

Dey, Susmita; Sarkar, Ripon; Chatterjee, Kabita; Datta, Pallab; Barui, Ananya; Maity, Santi P

2017-04-01

Habitual smokers are known to be at higher risk for developing oral cancer, which is increasing at an alarming rate globally. Conventionally, oral cancer is associated with high mortality rates, although recent reports show the improved survival outcomes by early diagnosis of disease. An effective prediction system which will enable to identify the probability of cancer development amongst the habitual smokers, is thus expected to benefit sizable number of populations. Present work describes a non-invasive, integrated method for early detection of cellular abnormalities based on analysis of different cyto-morphological features of exfoliative oral epithelial cells. Differential interference contrast (DIC) microscopy provides a potential optical tool as this mode provides a pseudo three dimensional (3-D) image with detailed morphological and textural features obtained from noninvasive, label free epithelial cells. For segmentation of DIC images, gradient vector flow snake model active contour process has been adopted. To evaluate cellular abnormalities amongst habitual smokers, the selected morphological and textural features of epithelial cells are compared with the non-smoker (-ve control group) group and clinically diagnosed pre-cancer patients (+ve control group) using support vector machine (SVM) classifier. Accuracy of the developed SVM based classification has been found to be 86% with 80% sensitivity and 89% specificity in classifying the features from the volunteers having smoking habit. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of naïve Bayes and logistic regression for computer-aided diagnosis of breast masses using ultrasound imaging

NASA Astrophysics Data System (ADS)

Cary, Theodore W.; Cwanger, Alyssa; Venkatesh, Santosh S.; Conant, Emily F.; Sehgal, Chandra M.

2012-03-01

This study compares the performance of two proven but very different machine learners, Naïve Bayes and logistic regression, for differentiating malignant and benign breast masses using ultrasound imaging. Ultrasound images of 266 masses were analyzed quantitatively for shape, echogenicity, margin characteristics, and texture features. These features along with patient age, race, and mammographic BI-RADS category were used to train Naïve Bayes and logistic regression classifiers to diagnose lesions as malignant or benign. ROC analysis was performed using all of the features and using only a subset that maximized information gain. Performance was determined by the area under the ROC curve, Az, obtained from leave-one-out cross validation. Naïve Bayes showed significant variation (Az 0.733 +/- 0.035 to 0.840 +/- 0.029, P < 0.002) with the choice of features, but the performance of logistic regression was relatively unchanged under feature selection (Az 0.839 +/- 0.029 to 0.859 +/- 0.028, P = 0.605). Out of 34 features, a subset of 6 gave the highest information gain: brightness difference, margin sharpness, depth-to-width, mammographic BI-RADs, age, and race. The probabilities of malignancy determined by Naïve Bayes and logistic regression after feature selection showed significant correlation (R2= 0.87, P < 0.0001). The diagnostic performance of Naïve Bayes and logistic regression can be comparable, but logistic regression is more robust. Since probability of malignancy cannot be measured directly, high correlation between the probabilities derived from two basic but dissimilar models increases confidence in the predictive power of machine learning models for characterizing solid breast masses on ultrasound.

Automated image processing method for the diagnosis and classification of malaria on thin blood smears.

PubMed

Ross, Nicholas E; Pritchard, Charles J; Rubin, David M; Dusé, Adriano G

2006-05-01

Malaria is a serious global health problem, and rapid, accurate diagnosis is required to control the disease. An image processing algorithm to automate the diagnosis of malaria on thin blood smears is developed. The image classification system is designed to positively identify malaria parasites present in thin blood smears, and differentiate the species of malaria. Images are acquired using a charge-coupled device camera connected to a light microscope. Morphological and novel threshold selection techniques are used to identify erythrocytes (red blood cells) and possible parasites present on microscopic slides. Image features based on colour, texture and the geometry of the cells and parasites are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. A two-stage tree classifier using backpropogation feedforward neural networks distinguishes between true and false positives, and then diagnoses the species (Plasmodium falciparum, P. vivax, P. ovale or P. malariae) of the infection. Malaria samples obtained from the Department of Clinical Microbiology and Infectious Diseases at the University of the Witwatersrand Medical School are used for training and testing of the system. Infected erythrocytes are positively identified with a sensitivity of 85% and a positive predictive value (PPV) of 81%, which makes the method highly sensitive at diagnosing a complete sample provided many views are analysed. Species were correctly determined for 11 out of 15 samples.

Consensus Paper: Radiological Biomarkers of Cerebellar Diseases

PubMed Central

Baldarçara, Leonardo; Currie, Stuart; Hadjivassiliou, M.; Hoggard, Nigel; Jack, Allison; Jackowski, Andrea P.; Mascalchi, Mario; Parazzini, Cecilia; Reetz, Kathrin; Righini, Andrea; Schulz, Jörg B.; Vella, Alessandra; Webb, Sara Jane; Habas, Christophe

2016-01-01

Hereditary and sporadic cerebellar ataxias represent a vast and still growing group of diseases whose diagnosis and differentiation cannot only rely on clinical evaluation. Brain imaging including magnetic resonance (MR) and nuclear medicine techniques allows for characterization of structural and functional abnormalities underlying symptomatic ataxias. These methods thus constitute a potential source of radiological biomarkers, which could be used to identify these diseases and differentiate subgroups of them, and to assess their severity and their evolution. Such biomarkers mainly comprise qualitative and quantitative data obtained from MR including proton spectroscopy, diffusion imaging, tractography, voxel-based morphometry, functional imaging during task execution or in a resting state, and from SPETC and PET with several radiotracers. In the current article, we aim to illustrate briefly some applications of these neuroimaging tools to evaluation of cerebellar disorders such as inherited cerebellar ataxia, fetal developmental malformations, and immune-mediated cerebellar diseases and of neurodegenerative or early-developing diseases, such as dementia and autism in which cerebellar involvement is an emerging feature. Although these radiological biomarkers appear promising and helpful to better understand ataxia-related anatomical and physiological impairments, to date, very few of them have turned out to be specific for a given ataxia with atrophy of the cerebellar system being the main and the most usual alteration being observed. Consequently, much remains to be done to establish sensitivity, specificity, and reproducibility of available MR and nuclear medicine features as diagnostic, progression and surrogate biomarkers in clinical routine. PMID:25382714

Imaging findings in a case of Gorlin-Goltz syndrome: a survey using advanced modalities.

PubMed

Bronoosh, Pegah; Shakibafar, Ali Reza; Houshyar, Maneli; Nafarzade, Shima

2011-12-01

Gorlin-Goltz syndrome is an infrequent multi-systemic disease which is characterized by multiple keratocysts in the jaws, calcification of falx cerebri, and basal cell carcinomas. We report a case of Gorlin-Goltz syndrome in a 23-year-old man with emphasis on image findings of keratocyctic odontogenic tumors (KCOTs) on panoramic radiograph, computed tomography, magnetic resonance (MR) imaging, and Ultrasonography (US). In this case, pericoronal lesions were mostly orthokeratinized odontogenic cyst (OOC) concerning the MR and US study, which tended to recur less. The aim of this report was to clarify the characteristic imaging features of the syndrome-related keratocysts that can be used to differentiate KCOT from OOC. Also, our findings suggested that the recurrence rate of KCOTs might be predicted based on their association to teeth.

MR imaging of spinal infection.

PubMed

Tins, Bernhard J; Cassar-Pullicino, Victor N

2004-09-01

Magnetic resonance (MR) imaging plays a pivotal role in the diagnosis and management of spinal infection, enjoying a high sensitivity and specificity. A thorough understanding of spinal anatomy and the physicochemical pathological processes associated with infection is a desirable prerequisite allowing accurate interpretation of the disease process. Apart from confirmation of the disease, MR imaging is also best suited to excluding multifocal spinal involvement and the detection/exclusion of complications. It plays an essential role in the decision-making process concerning conservative versus surgical treatment and is also the best imaging method to monitor the effect of treatment. The MR features of infection confidently exclude tumor, degeneration, and so forth as the underlying process; differentiate pyogenic from granulomatous infections in most cases; and can suggest the rarer specific infective organisms. Copyright 2004 Thieme Medical Publishers, Inc.

White blood cells identification system based on convolutional deep neural learning networks.

PubMed

Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

2017-11-16

White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.

Characterisation of human thyroid epithelial cells immortalised in vitro by simian virus 40 DNA transfection.

PubMed Central

Lemoine, N. R.; Mayall, E. S.; Jones, T.; Sheer, D.; McDermid, S.; Kendall-Taylor, P.; Wynford-Thomas, D.

1989-01-01

Human primary thyroid follicular epithelial cells were transfected with a plasmid containing an origin-defective SV40 genome (SVori-) to produce several immortal cell lines. Two of the 10 cell lines analysed expressed specific features of thyroid epithelial function (iodide-trapping and thyroglobulin production). These two lines were characterised in detail and found to be growth factor-independent, capable of anchorage-independent growth at low frequency but non-tumorigenic in nude mice. These differentiated, These differentiated, partially transformed cell lines were shown to be suitable for gene transfer at high frequency using simple coprecipitation techniques. Images Figure 2 Figure 3 Figure 4 PMID:2557880

Differential optical absorption spectrometer for measurement of tropospheric pollutants

NASA Astrophysics Data System (ADS)

Evangelisti, F.; Baroncelli, A.; Bonasoni, P.; Giovanelli, G.; Ravegnani, F.

1995-05-01

Our institute has recently developed a differential optical absorption spectrometry system called the gas analyzer spectrometer correlating optical absorption differences (GASCOAD), which features as a detector a linear image sensor that uses an artificial light source for long-path tropospheric-pollution monitoring. The GASCOAD, its method of eliminating interference from background sky light, and subsequent spectral analysis are reported and discussed. The spectrometer was used from 7 to 22 February 1993 in Milan, a heavily polluted metropolitan area, to measure the concentrations of SO2, NO2, O3, and HNO2 averaged over a 1.7-km horizontal light path. The findings are reported and briefly discussed.

Computerized lung cancer malignancy level analysis using 3D texture features

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Huang, Xia; Tseng, Tzu-Liang; Zhang, Jianying; Qian, Wei

2016-03-01

Based on the likelihood of malignancy, the nodules are classified into five different levels in Lung Image Database Consortium (LIDC) database. In this study, we tested the possibility of using threedimensional (3D) texture features to identify the malignancy level of each nodule. Five groups of features were implemented and tested on 172 nodules with confident malignancy levels from four radiologists. These five feature groups are: grey level co-occurrence matrix (GLCM) features, local binary pattern (LBP) features, scale-invariant feature transform (SIFT) features, steerable features, and wavelet features. Because of the high dimensionality of our proposed features, multidimensional scaling (MDS) was used for dimension reduction. RUSBoost was applied for our extracted features for classification, due to its advantages in handling imbalanced dataset. Each group of features and the final combined features were used to classify nodules highly suspicious for cancer (level 5) and moderately suspicious (level 4). The results showed that the area under the curve (AUC) and accuracy are 0.7659 and 0.8365 when using the finalized features. These features were also tested on differentiating benign and malignant cases, and the reported AUC and accuracy were 0.8901 and 0.9353.

Functional video-based analysis of 3D cardiac structures generated from human embryonic stem cells.

PubMed

Nitsch, Scarlett; Braun, Florian; Ritter, Sylvia; Scholz, Michael; Schroeder, Insa S

2018-05-01

Human embryonic stem cells (hESCs) differentiated into cardiomyocytes (CM) often develop into complex 3D structures that are composed of various cardiac cell types. Conventional methods to study the electrophysiology of cardiac cells are patch clamp and microelectrode array (MEAs) analyses. However, these methods are not suitable to investigate the contractile features of 3D cardiac clusters that detach from the surface of the culture dishes during differentiation. To overcome this problem, we developed a video-based motion detection software relying on the optical flow by Farnebäck that we call cBRA (cardiac beat rate analyzer). The beating characteristics of the differentiated cardiac clusters were calculated based on the local displacement between two subsequent images. Two differentiation protocols, which profoundly differ in the morphology of cardiac clusters generated and in the expression of cardiac markers, were used and the resulting CM were characterized. Despite these differences, beat rates and beating variabilities could be reliably determined using cBRA. Likewise, stimulation of β-adrenoreceptors by isoproterenol could easily be identified in the hESC-derived CM. Since even subtle changes in the beating features are detectable, this method is suitable for high throughput cardiotoxicity screenings. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Quantitative analysis and feature recognition in 3-D microstructural data sets

NASA Astrophysics Data System (ADS)

Lewis, A. C.; Suh, C.; Stukowski, M.; Geltmacher, A. B.; Spanos, G.; Rajan, K.

2006-12-01

A three-dimensional (3-D) reconstruction of an austenitic stainless-steel microstructure was used as input for an image-based finite-element model to simulate the anisotropic elastic mechanical response of the microstructure. The quantitative data-mining and data-warehousing techniques used to correlate regions of high stress with critical microstructural features are discussed. Initial analysis of elastic stresses near grain boundaries due to mechanical loading revealed low overall correlation with their location in the microstructure. However, the use of data-mining and feature-tracking techniques to identify high-stress outliers revealed that many of these high-stress points are generated near grain boundaries and grain edges (triple junctions). These techniques also allowed for the differentiation between high stresses due to boundary conditions of the finite volume reconstructed, and those due to 3-D microstructural features.

Morphologic Features of Magnetic Resonance Imaging as a Surrogate of Capsular Contracture in Breast Cancer Patients With Implant-based Reconstructions.

PubMed

Tyagi, Neelam; Sutton, Elizabeth; Hunt, Margie; Zhang, Jing; Oh, Jung Hun; Apte, Aditya; Mechalakos, James; Wilgucki, Molly; Gelb, Emily; Mehrara, Babak; Matros, Evan; Ho, Alice

2017-02-01

Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. Currently, no objective methods are available for assessing CC. The goal of the present study was to identify image-based surrogates of CC using magnetic resonance imaging (MRI). We analyzed a retrospective data set of 50 patients who had undergone both a diagnostic MRI scan and a plastic surgeon's evaluation of the CC score (Baker's score) within a 6-month period after mastectomy and reconstructive surgery. The MRI scans were assessed for morphologic shape features of the implant and histogram features of the pectoralis muscle. The shape features, such as roundness, eccentricity, solidity, extent, and ratio length for the implant, were compared with the Baker score. For the pectoralis muscle, the muscle width and median, skewness, and kurtosis of the intensity were compared with the Baker score. Univariate analysis (UVA) using a Wilcoxon rank-sum test and multivariate analysis with the least absolute shrinkage and selection operator logistic regression was performed to determine significant differences in these features between the patient groups categorized according to their Baker's scores. UVA showed statistically significant differences between grade 1 and grade ≥2 for morphologic shape features and histogram features, except for volume and skewness. Only eccentricity, ratio length, and volume were borderline significant in differentiating grade ≤2 and grade ≥3. Features with P<.1 on UVA were used in the multivariate least absolute shrinkage and selection operator logistic regression analysis. Multivariate analysis showed a good level of predictive power for grade 1 versus grade ≥2 CC (area under the receiver operating characteristic curve 0.78, sensitivity 0.78, and specificity 0.82) and for grade ≤2 versus grade ≥3 CC (area under the receiver operating characteristic curve 0.75, sensitivity 0.75, and specificity 0.79). The morphologic shape features described on MR images were associated with the severity of CC. MRI has the potential to further improve the diagnostic ability of the Baker score in breast cancer patients who undergo implant reconstruction. Copyright © 2016 Elsevier Inc. All rights reserved.

Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach.

PubMed

Romeo, Valeria; Maurea, Simone; Cuocolo, Renato; Petretta, Mario; Mainenti, Pier Paolo; Verde, Francesco; Coppola, Milena; Dell'Aversana, Serena; Brunetti, Arturo

2018-01-17

Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal lesions (NAL) may be challenging. Texture analysis (TA) can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest. To assess the diagnostic efficacy of TA-derived parameters extracted from MR images in characterizing LRA, LPA, and NAL using a machine-learning approach. Retrospective, observational study. Sixty MR examinations, including 20 LRA, 20 LPA, and 20 NAL. Unenhanced T 1 -weighted in-phase (IP) and out-of-phase (OP) as well as T 2 -weighted (T 2 -w) MR images acquired at 3T. Adrenal lesions were manually segmented, placing a spherical volume of interest on IP, OP, and T 2 -w images. Different selection methods were trained and tested using the J48 machine-learning classifiers. The feature selection method that obtained the highest diagnostic performance using the J48 classifier was identified; the diagnostic performance was also compared with that of a senior radiologist by means of McNemar's test. A total of 138 TA-derived features were extracted; among these, four features were selected, extracted from the IP (Short_Run_High_Gray_Level_Emphasis), OP (Mean_Intensity and Maximum_3D_Diameter), and T 2 -w (Standard_Deviation) images; the J48 classifier obtained a diagnostic accuracy of 80%. The expert radiologist obtained a diagnostic accuracy of 73%. McNemar's test did not show significant differences in terms of diagnostic performance between the J48 classifier and the expert radiologist. Machine learning conducted on MR TA-derived features is a potential tool to characterize adrenal lesions. 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Deep Learning in Label-free Cell Classification

PubMed Central

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

2016-01-01

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells. PMID:26975219

Deep Learning in Label-free Cell Classification

NASA Astrophysics Data System (ADS)

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

2016-03-01

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

[Differential diagnosis of the MDCT features between lung adenocarcinoma preinvasive lesions and minimally invasive adenocarcinoma appearing as ground-glass nodules].

PubMed

Liu, Jia; Li, Wenwu; Huang, Yong; Mu, Dianbin; Yu, Haiying; Li, Shanshan

2015-08-01

The aim of this study was to retrospectively investigate the multi-detector computed tomography (MDCT) features of preinvasive lesions and minimally invasive adenocarcinoma (MIA) appearing as ground-glass nodules (GGNs), and to analyze their significance in differential diagnosis. The pathological data and MDCT images of 111 GGNs in 93 patients were reviewed and analyzed retrospectively, to identify the differentiating CT features between preinvasive lesions and MIA and to evaluate their differentiating accuracy. In the 93 patients included in the study, there were 27 cases with preinvasive lesions (38 GGNs) and 66 cases with MIA (73 GGNs). No statistically significant difference was observed in terms of the gender, age and number of lesions between the two groups. There were significant differences (P<0.05) in the size of lesion, size of solid portion, content of solid portion, and morphological characteristics of the lesion edge between preinvasive lesions and MIA. ROC curve analysis showed that the optimal cut-off value of lesion size for differentiating preinvasive lesions from MIA was 13.0 mm (sensitivity, 83.0%; specificity, 80.0%), and that of solid portion size was 2.0 mm (sensitivity, 90.0%; specificity, 97.0%) and that of solid proportion was 12.0% (sensitivity, 88.0%; specificity, 97.0%). The analysis of CT morphological features showed that there were significant differences in the terms of lesion nature (pGGO, mGGO), presence or absence of lobulated sign and spiculated sign (P<0.05) between preinvasive lesions and MIA, but there were no significant differences in terms of the lesion edge, the presence or absence of vacuole sign, bubble lucency and pleural retraction (P>0.05). Preinvasive lesions can be accurately distinguished from MIA by the size of lesion, size of solid portion,solid proportion and morphological characteristics of the lesion edge. The size of lesion, size of solid portion, content of solid proportion and morphological characteristics of the lesion edge are of significance in the differential diagnosis of preinvasive lesions and minimally invasive adenocarcinoma of the lung.

Cholesteatoma in the Sellar Region Presenting as Hypopituitarism and Diabetes Insipidus

PubMed Central

Kong, Xiangyi; Wu, Huanwen; Ma, Wenbin; Li, Yongning; Xing, Bing; Kong, Yanguo; Wang, Renzhi

2016-01-01

Abstract Clinically significant sellar cysts unrelated to pituitary adenomas are uncommon. Intracranial cholesteatomas are also rare and are most common in the middle ear and mastoid region. We report an even rarer case of cholesteatoma in the sellar region—a challenging diagnosis guided by clinical presentations, radiological signs, and biopsy, aiming at emphasize the importance of considering cholesteatoma when making differential diagnoses of sellar lesions. We present a case of cholesteatoma in the sellar region in a 56-year-old man with hypopituitarism, diabetes insipidus, and cystic imaging findings. It was difficult to make an accurate diagnosis before surgery. We present detailed analysis of the patient's disease course and review pertinent literature. The patient underwent a surgical exploration and tumor resection through a transsphenoidal approach. Pathologic results revealed a cholesteatoma. The patient's symptoms improved a lot after surgery, and the postoperative period was uneventful. Taken together, the lesion's imaging appearance, pathological characteristics, and clinical features were all unique features that lead to a diagnosis of cholesteatoma. As we did not see such reports by Pubmed and EMBASE, we believe this is the first reported case of sellar cholesteatoma presenting in this manner. This article emphasized that cholesteatomas, although rare, should be considered part of the differential diagnosis of sellar lesions. PMID:26962793

A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Arevalo, John; Basavanhally, Ajay; Madabhushi, Anant; González, Fabio

2015-01-01

Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amounts of data to find building-blocks that better represent the information in it. Digitized histopathology images represents a very good testbed for representation learning since it involves large amounts of high complex, visual data. This paper presents a comparative evaluation of different supervised and unsupervised representation learning architectures to specifically address open questions on what type of learning architectures (deep or shallow), type of learning (unsupervised or supervised) is optimal. In this paper we limit ourselves to addressing these questions in the context of distinguishing between anaplastic and non-anaplastic medulloblastomas from routine haematoxylin and eosin stained images. The unsupervised approaches evaluated were sparse autoencoders and topographic reconstruct independent component analysis, and the supervised approach was convolutional neural networks. Experimental results show that shallow architectures with more neurons are better than deeper architectures without taking into account local space invariances and that topographic constraints provide useful invariant features in scale and rotations for efficient tumor differentiation.

Klatskin-mimicking lesions--a case series and literature review.

PubMed

Dumitrascu, Traian; Ionescu, Mihnea; Ciurea, Silviu; Herlea, Vlad; Lupescu, Ioana; Popescu, Irinel

2010-01-01

Obstruction of the hepatic hilum in patients without prior surgery is generally due to hilar adenocarcinoma (Klatskin tumor). However, not all the hilar strictures are malignant. Although uncommon, benign strictures of the proximal bile duct should be taken into consideration in differential diagnosis of Klatskin tumors, since the incidence could reach up to 25% of patients with presumed Klatskin tumor diagnosis. This group of benign proximal bile duct strictures (Klatskin-mimicking lesions) is usually represented by segmental fibrosis and non-specific chronic inflammation. The clinical and imaging features can not differentiate between benign and malignant strictures. Herein, we present a case series of three patients with benign proximal bile duct strictures (representing 4.1% of 73 patients resected with presumptive preoperative diagnosis of Klatskin tumor) and literature review. There are presented the clinical and biochemical features, imaging preoperative workup, surgical treatment and histological analysis of the specimen, along with postoperative outcome. For benign strictures of the hilum limited resections are curative. However, despite new diagnosis tools developed in the last years, patients with hilar obstructions still require unnecessary extensive resections due to impossibility of excluding the malignancy. In all cases of proximal bile duct obstruction presumed malignant, they should be managed accordingly, even with the risk of over-treatment for some benign lesions.

Calcium imaging shows differential sensitivity to cooling and communication in luminous transgenic plants.

PubMed

Campbell, A K; Trewavas, A J; Knight, M R

1996-03-01

Imaging of a recombinant bioluminescent Ca2+ indicator, aequorin, in an entire organism showed three novel features of Ca2+ signals in plants. First, cooling the plant from 25 degrees C to 2 degrees C demonstrated differential sensitivities between organs, the roots firing a Ca2+ signal at some 8-10 degrees C higher than the cotyledons. Secondly, prolonged cooling provoked Ca2+ oscillations, but only in the cotyledons. These oscillations occurred with a frequency of 100 s and damped down within 800 s. Thirdly, cooling the roots of mature plants triggered a Ca2+ signal in the leaves, as a result of organ-organ communication. However, warming and then recooling the roots did not generate a second Ca2+ signal in these leaves. This desensitisation was not due to down-regulation in the leaf since this was able to generate a Ca2+ signal of its own when cooled directly. Thus a combination of a recombinant bioluminescent indicator with photon counting imaging reveals startling new aspects of signalling in intact organs and whole organisms.

Beyond gastric adenocarcinoma: Multimodality assessment of common and uncommon gastric neoplasms

PubMed Central

Richman, Danielle M.; Tirumani, Sree Harsha; Hornick, Jason L.; Fuchs, Charles S.; Howard, Stephanie; Krajewski, Katherine; Ramaiya, Nikhil; Rosenthal, Michael

2016-01-01

Despite advances in molecular biology, imaging, and treatment, gastric neoplasms remain a significant cause of morbidity and mortality; gastric adenocarcinoma is the fifth most common malignancy and third most common cause of death worldwide (Brenner et al., Methods Mol Biol 472:467–477, 2009; Howson et al. Epidemiol Rev 8:1–27, 1986; Roder, Gastric Cancer 5(Suppl 1):5–11, 2002; Ferlay et al., GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet]. International Agency for Research on Cancer, 2013). Because of both the frequency at which malignant gastric tumors occur as well as the worldwide impact, gastric neoplasms remain important lesions to identify and characterize on all imaging modalities. Despite the varied histologies and behaviors of these neoplasms, many have similar imaging features. Nonetheless, the treatment, management, and prognosis of gastric neoplasms vary by pathology, so it is essential for the radiologist to make every effort to differentiate between these lesions and raise the less common entities as differential diagnostic considerations when appropriate. PMID:27645897

Neuroradiology and histopathology in two cases of adult medulloblastoma.

PubMed

Romero-Rojas, Alfredo E; Diaz-Perez, Julio A; Raju, Sharat; Lozano-Castillo, Alfonso

2014-04-01

Medulloblastoma (MB) is the most common central nervous system neoplasm in children and only rarely presents in the adult population. Recent molecular biology findings have characterized MB as a heterogeneous neoplasm distinguished by well-defined tumour subsets each with specific histologic and molecular features. Available immunohistochemical stains can now be used to differentiate the distinct molecular types of MB. This report analyzed the histopathologic and neuroradiologic features of two new cases of adult MB. Imaging studies in these patients revealed the morphological appearance of high-grade, well-circumscribed heterogeneous tumours with necrosis, located laterally within the posterior cranial fossa. Histopathology of resected samples demonstrated high-grade tumours (WHO grade IV) containing sheets of undifferentiated neural cells with high mitotic activity and evidence of necrosis. The histopathologic and molecular characteristics of these cases of MB are reviewed for potential applications in new molecular methods of imaging.

Three-dimensional morphological imaging of human induced pluripotent stem cells by using low-coherence quantitative phase microscopy

NASA Astrophysics Data System (ADS)

Yamauchi, Toyohiko; Kakuno, Yumi; Goto, Kentaro; Fukami, Tadashi; Sugiyama, Norikazu; Iwai, Hidenao; Mizuguchi, Yoshinori; Yamashita, Yutaka

2014-03-01

There is an increasing need for non-invasive imaging techniques in the field of stem cell research. Label-free techniques are the best choice for assessment of stem cells because the cells remain intact after imaging and can be used for further studies such as differentiation induction. To develop a high-resolution label-free imaging system, we have been working on a low-coherence quantitative phase microscope (LC-QPM). LC-QPM is a Linnik-type interference microscope equipped with nanometer-resolution optical-path-length control and capable of obtaining three-dimensional volumetric images. The lateral and vertical resolutions of our system are respectively 0.5 and 0.93 μm and this performance allows capturing sub-cellular morphological features of live cells without labeling. Utilizing LC-QPM, we reported on three-dimensional imaging of membrane fluctuations, dynamics of filopodia, and motions of intracellular organelles. In this presentation, we report three-dimensional morphological imaging of human induced pluripotent stem cells (hiPS cells). Two groups of monolayer hiPS cell cultures were prepared so that one group was cultured in a suitable culture medium that kept the cells undifferentiated, and the other group was cultured in a medium supplemented with retinoic acid, which forces the stem cells to differentiate. The volumetric images of the 2 groups show distinctive differences, especially in surface roughness. We believe that our LC-QPM system will prove useful in assessing many other stem cell conditions.

Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach.

PubMed

Maity, Maitreya; Dhane, Dhiraj; Mungle, Tushar; Maiti, A K; Chakraborty, Chandan

2017-10-26

Web-enabled e-healthcare system or computer assisted disease diagnosis has a potential to improve the quality and service of conventional healthcare delivery approach. The article describes the design and development of a web-based distributed healthcare management system for medical information and quantitative evaluation of microscopic images using machine learning approach for malaria. In the proposed study, all the health-care centres are connected in a distributed computer network. Each peripheral centre manages its' own health-care service independently and communicates with the central server for remote assistance. The proposed methodology for automated evaluation of parasites includes pre-processing of blood smear microscopic images followed by erythrocytes segmentation. To differentiate between different parasites; a total of 138 quantitative features characterising colour, morphology, and texture are extracted from segmented erythrocytes. An integrated pattern classification framework is designed where four feature selection methods viz. Correlation-based Feature Selection (CFS), Chi-square, Information Gain, and RELIEF are employed with three different classifiers i.e. Naive Bayes', C4.5, and Instance-Based Learning (IB1) individually. Optimal features subset with the best classifier is selected for achieving maximum diagnostic precision. It is seen that the proposed method achieved with 99.2% sensitivity and 99.6% specificity by combining CFS and C4.5 in comparison with other methods. Moreover, the web-based tool is entirely designed using open standards like Java for a web application, ImageJ for image processing, and WEKA for data mining considering its feasibility in rural places with minimal health care facilities.

Automatic grade classification of Barretts Esophagus through feature enhancement

NASA Astrophysics Data System (ADS)

Ghatwary, Noha; Ahmed, Amr; Ye, Xujiong; Jalab, Hamid

2017-03-01

Barretts Esophagus (BE) is a precancerous condition that affects the esophagus tube and has the risk of developing esophageal adenocarcinoma. BE is the process of developing metaplastic intestinal epithelium and replacing the normal cells in the esophageal area. The detection of BE is considered difficult due to its appearance and properties. The diagnosis is usually done through both endoscopy and biopsy. Recently, Computer Aided Diagnosis systems have been developed to support physicians opinion when facing difficulty in detection/classification in different types of diseases. In this paper, an automatic classification of Barretts Esophagus condition is introduced. The presented method enhances the internal features of a Confocal Laser Endomicroscopy (CLE) image by utilizing a proposed enhancement filter. This filter depends on fractional differentiation and integration that improve the features in the discrete wavelet transform of an image. Later on, various features are extracted from each enhanced image on different levels for the multi-classification process. Our approach is validated on a dataset that consists of a group of 32 patients with 262 images with different histology grades. The experimental results demonstrated the efficiency of the proposed technique. Our method helps clinicians for more accurate classification. This potentially helps to reduce the need for biopsies needed for diagnosis, facilitate the regular monitoring of treatment/development of the patients case and can help train doctors with the new endoscopy technology. The accurate automatic classification is particularly important for the Intestinal Metaplasia (IM) type, which could turn into deadly cancerous. Hence, this work contributes to automatic classification that facilitates early intervention/treatment and decreasing biopsy samples needed.

Different MR features for differentiation of intrahepatic mass-forming cholangiocarcinoma from hepatocellular carcinoma according to tumor size.

PubMed

Ni, Ting; Shang, Xiao-Sha; Wang, Wen-Tao; Hu, Xin-Xing; Zeng, Meng-Su; Rao, Sheng-Xiang

2018-06-05

To identify reliable magnetic resonance (MR) features for distinguishing mass-forming type of intrahepatic cholangiocarcinoma (IMCC) from hepatocellular carcinoma (HCC) based on tumor size. This retrospective study included 395 patients with pathologically confirmed IMCCs (n = 180) and HCCs (n = 215) who underwent pre-operative contrast-enhanced MRI including diffusion-weighted imaging (DWI). MR features were evaluated and clinical data were also recorded. All the characteristics were compared in small (≤3 cm) and large tumor (>3 cm) groups by univariate analysis and subsequently calculated by multivariable logistic regression analysis. Multivariable analysis revealed that rim arterial phase hyperenhancement [odds ratios (ORs) = 13.16], biliary dilation (OR = 23.42) and CA19-9 (OR = 21.45) were significant predictors of large IMCCs (n = 138), and washout appearance (OR = 0.036), enhancing capsule appearance (OR = 0.039), fat in mass (OR = 0.057), chronic liver disease (OR = 0.088) and alpha fetoprotein (OR = 0.019) were more frequently found in large HCCs (n = 143). For small IMCCs (n = 42) and HCCs (n = 72), rim arterial phase hyperenhancement (OR = 9.68), target appearance at DWI (OR = 12.51), alpha fetoprotein (OR = 0.12) and sex (OR = 0.20) were independent predictors in multivariate analysis. Valuable MR features and clinical factors varied for differential diagnosis of IMCCs and HCCs according to tumor size. Advances in knowledge: MR features for differential diagnosis of large IMCC and HCC (>3 cm) are in keeping with that recommended by LI-RADS. However, for small IMCCs and HCCs (≤3 cm), only rim enhancement on arterial phase and target appearance at DWI are reliable predictors.

Novel algorithm to identify and differentiate specific digital signature of breath sound in patients with diffuse parenchymal lung disease.

PubMed

Bhattacharyya, Parthasarathi; Mondal, Ashok; Dey, Rana; Saha, Dipanjan; Saha, Goutam

2015-05-01

Auscultation is an important part of the clinical examination of different lung diseases. Objective analysis of lung sounds based on underlying characteristics and its subsequent automatic interpretations may help a clinical practice. We collected the breath sounds from 8 normal subjects and 20 diffuse parenchymal lung disease (DPLD) patients using a newly developed instrument and then filtered off the heart sounds using a novel technology. The collected sounds were thereafter analysed digitally on several characteristics as dynamical complexity, texture information and regularity index to find and define their unique digital signatures for differentiating normality and abnormality. For convenience of testing, these characteristic signatures of normal and DPLD lung sounds were transformed into coloured visual representations. The predictive power of these images has been validated by six independent observers that include three physicians. The proposed method gives a classification accuracy of 100% for composite features for both the normal as well as lung sound signals from DPLD patients. When tested by independent observers on the visually transformed images, the positive predictive value to diagnose the normality and DPLD remained 100%. The lung sounds from the normal and DPLD subjects could be differentiated and expressed according to their digital signatures. On visual transformation to coloured images, they retain 100% predictive power. This technique may assist physicians to diagnose DPLD from visual images bearing the digital signature of the condition. © 2015 Asian Pacific Society of Respirology.

Differential diagnosis of idiopathic granulomatous mastitis and breast cancer using acoustic radiation force impulse imaging.

PubMed

Teke, Memik; Teke, Fatma; Alan, Bircan; Türkoğlu, Ahmet; Hamidi, Cihad; Göya, Cemil; Hattapoğlu, Salih; Gumus, Metehan

2017-01-01

Differentiation of idiopathic granulomatous mastitis (IGM) from carcinoma with routine imaging methods, such as ultrasonography (US) and mammography, is difficult. Therefore, we evaluated the value of a newly developed noninvasive technique called acoustic radiation force impulse imaging in differentiating IGM versus malignant lesions in the breast. Four hundred and eighty-six patients, who were referred to us with a presumptive diagnosis of a mass, underwent Virtual Touch tissue imaging (VTI; Siemens) and Virtual Touch tissue quantification (VTQ; Siemens) after conventional gray-scale US. US-guided percutaneous needle biopsy was then performed on 276 lesions with clinically and radiologically suspicious features. Malignant lesions (n = 122) and IGM (n = 48) were included in the final study group. There was a statistically significant difference in shear wave velocity marginal and internal values between the IGM and malignant lesions. The median marginal velocity for IGM and malignant lesions was 3.19 m/s (minimum-maximum 2.49-5.82) and 5.05 m/s (minimum-maximum 2.09-8.46), respectively (p < 0.001). The median internal velocity for IGM and malignant lesions was 2.76 m/s (minimum-maximum 1.14-4.12) and 4.79 m/s (minimum-maximum 2.12-8.02), respectively (p < 0.001). The combination of VTI and VTQ as a complement to conventional US provides viscoelastic properties of tissues, and thus has the potential to increase the specificity of US.

Design of a Steerable Two-beam System for Simultaneous On- and Off-axis Imaging with GUFI

NASA Astrophysics Data System (ADS)

Chambers, V. J.; Butler, R. F.; Goncharov, A. V.

2008-02-01

The GUFI (Galway Ultra Fast Imager) has been primarily developed for high throughput differential photometry, in order to study variability in challenging circumstances, such as near bright sources or within crowded fields. The instrument features a low light level charged coupled device (L3-CCD) that enhances detector speed and sensitivity but only covers small fields of view. This presents limitations on possible science targets when suitable differential photometry comparison stars are not in the immediate vicinity of the target. Conventional solutions for imaging larger portions of sky without sacrificing SNR include telescope focal reduction methods and large arrays of CCDs. Our alternative solution entails a two-path, `outrigger' optical design to image target and comparison stars separately. This new approach allows detection of variable targets that formerly were not reachable with smaller-field detectors. The mechanical design was originally generated with AutoCAD® drafting software before being compiled in, and vetted with an OSLO® optical design package. Through filters B, V and I, the limiting design aberration was chromatic focal shift that appeared most severe in the B-filter's bandpass range. However, the degree of image blurring caused by this aberration and others did not exceed the scale of that already produced by atmospheric turbulence. For each bandpass, the model's imaging performance met and exceeded expectations set by all design constraints.

Anatomical background noise power spectrum in differential phase contrast breast images

NASA Astrophysics Data System (ADS)

Garrett, John; Ge, Yongshuai; Li, Ke; Chen, Guang-Hong

2015-03-01

In x-ray breast imaging, the anatomical noise background of the breast has a significant impact on the detection of lesions and other features of interest. This anatomical noise is typically characterized by a parameter, β, which describes a power law dependence of anatomical noise on spatial frequency (the shape of the anatomical noise power spectrum). Large values of β have been shown to reduce human detection performance, and in conventional mammography typical values of β are around 3.2. Recently, x-ray differential phase contrast (DPC) and the associated dark field imaging methods have received considerable attention as possible supplements to absorption imaging for breast cancer diagnosis. However, the impact of these additional contrast mechanisms on lesion detection is not yet well understood. In order to better understand the utility of these new methods, we measured the β indices for absorption, DPC, and dark field images in 15 cadaver breast specimens using a benchtop DPC imaging system. We found that the measured β value for absorption was consistent with the literature for mammographic acquisitions (β = 3.61±0.49), but that both DPC and dark field images had much lower values of β (β = 2.54±0.75 for DPC and β = 1.44±0.49 for dark field). In addition, visual inspection showed greatly reduced anatomical background in both DPC and dark field images. These promising results suggest that DPC and dark field imaging may help provide improved lesion detection in breast imaging, particularly for those patients with dense breasts, in whom anatomical noise is a major limiting factor in identifying malignancies.

Statistical analysis of textural features for improved classification of oral histopathological images.

PubMed

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

2012-04-01

The objective of this paper is to provide an improved technique, which can assist oncopathologists in correct screening of oral precancerous conditions specially oral submucous fibrosis (OSF) with significant accuracy on the basis of collagen fibres in the sub-epithelial connective tissue. The proposed scheme is composed of collagen fibres segmentation, its textural feature extraction and selection, screening perfomance enhancement under Gaussian transformation and finally classification. In this study, collagen fibres are segmented on R,G,B color channels using back-probagation neural network from 60 normal and 59 OSF histological images followed by histogram specification for reducing the stain intensity variation. Henceforth, textural features of collgen area are extracted using fractal approaches viz., differential box counting and brownian motion curve . Feature selection is done using Kullback-Leibler (KL) divergence criterion and the screening performance is evaluated based on various statistical tests to conform Gaussian nature. Here, the screening performance is enhanced under Gaussian transformation of the non-Gaussian features using hybrid distribution. Moreover, the routine screening is designed based on two statistical classifiers viz., Bayesian classification and support vector machines (SVM) to classify normal and OSF. It is observed that SVM with linear kernel function provides better classification accuracy (91.64%) as compared to Bayesian classifier. The addition of fractal features of collagen under Gaussian transformation improves Bayesian classifier's performance from 80.69% to 90.75%. Results are here studied and discussed.

Cross Sectional Imaging of Solitary Lesions of the Neurocranium.

PubMed

Schäfer, Max-Ludwig; Koch, Arend; Streitparth, Florian; Wiener, Edzard

2017-12-01

Background  Although a wide range of processes along the neurocranium are of a benign nature, there are often difficulties in the differential diagnosis. Method  In the review CT/MRI scans of the head were evaluated retrospectively regarding solitary lesions along the neurocranium. The majority of the lesions were histologically proven. Results  The purpose of the review is to present typical pathologies of the neurocranium and provide a systematic overview based on 12 entities, their locations, prevalence and radiological characteristics. Conclusion  Processes, which primarily originate from the neurocranium have to be differentiated from secondary processes infiltrating the neurocranium. For this important diagnostic feature, MRI is typically essential, while the definitive diagnosis is often made on the basis of the medical history and the typical appearance on computer tomography. Key Points   · There are often difficulties in the precise differential diagnosis of solitary lesions along the neurocranium. Typical solitary pathologies of the neurocranium based on 12 entities were presented. Both magnetic resonance imaging and computed tomography are often essential for an exact differential diagnosis.. Citation Format · Schäfer M, Koch A, Streitparth F et al. Cross Sectional Diagnosis of Solitary Lesions of the Neurocranium. Fortschr Röntgenstr 2017; 189: 1135 - 1144. © Georg Thieme Verlag KG Stuttgart · New York.

Learning and Recognition of Clothing Genres From Full-Body Images.

PubMed

Hidayati, Shintami C; You, Chuang-Wen; Cheng, Wen-Huang; Hua, Kai-Lung

2018-05-01

According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel approach for automatically classifying clothing genres based on the visually differentiable style elements. A set of style elements, that are crucial for recognizing specific visual styles of clothing genres, were identified based on the clothing design theory. In addition, the corresponding salient visual features of each style element were identified and formulated with variables that can be computationally derived with various computer vision algorithms. To evaluate the performance of our algorithm, a dataset containing 3250 full-body shots crawled from popular online stores was built. Recognition results show that our proposed algorithms achieved promising overall precision, recall, and -score of 88.76%, 88.53%, and 88.64% for recognizing upperwear genres, and 88.21%, 88.17%, and 88.19% for recognizing lowerwear genres, respectively. The effectiveness of each style element and its visual features on recognizing clothing genres was demonstrated through a set of experiments involving different sets of style elements or features. In summary, our experimental results demonstrate the effectiveness of the proposed method in clothing genre recognition.

Diffusion-weighted imaging is helpful in the accurate non-invasive diagnosis of breast abscess: correlation with necrotic breast cancer.

PubMed

Wang, Cuiyan; Eghtedari, Mohammad; Yang, Wei Tse; Dogan, Basak Erguvan

2018-03-22

Clinical differentiation of atypical breast abscesses from necrotic tumour in premenopausal women is challenging and may delay appropriate therapy. In this case report, we present a 36-year-old woman with signs, symptoms and conventional imaging features of malignancy who underwent breast MRI. On diffusion-weighted imaging (DWI), profoundly low apparent diffusion coefficient values were a distinguishing sign of breast abscess from necrotic breast cancer, and helped manage the patient conservatively. We present a companion case of necrotic breast tumour highlighting significant differences in DWI. © BMJ Publishing Group Ltd (unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

SPG11 Presenting with Tremor

PubMed Central

Schneider, Susanne A.; Mummery, Catherine J.; Mehrabian, Mohadeseh; Houlden, Henry; Bain, Peter G.

2012-01-01

Background Hereditary spastic paraplegias (HSPs) are a clinically and genetically heterogeneous group of neurological diseases, which typically present with progressive lower extremity weakness and spasticity causing progressive walking difficulties. Complicating neurological or extraneurological features may be present. Case Report We describe a 19-year-old male who was referred because of an action tremor of the hands; he later developed walking difficulties. Callosal atrophy was present on his cerebral magnetic resonance imaging scan, prompting genetic testing for SPG11, which revealed homozygous mutations. Discussion The clinical features, differential diagnosis and management of SPG11, the most common form of autosomal recessive complicated HSP with a thin corpus callosum are discussed. PMID:23439843

Computed tomography imaging features of hepatic perivascular epithelioid cell tumor: A case report and literature review.

PubMed

Han, Xu; Sun, Mei-Yu; Liu, Jing-Hong; Zhang, Xiao-Yan; Wang, Meng-Yao; Fan, Rui; Qamar, Sahrish

2017-12-01

Perivascular epithelioid cell tumor (PEComa) is a rare tumor which is most frequently found in uterus. The tumor arising from liver is extremely uncommon. A 36-year-old female with abdominal distention, cramps, and low-grade fever for over 15 days. The patient had a history of gastric adenocarcinoma with ovarian, celiac lymph nodes, and retroperitoneal lymph nodes metastases. Computed tomography (CT) imaging demonstrated an ill-defined heterogeneous hypo-dense mass in segment 8 (S8) of the liver. Contrast-enhanced CT imaging showed marked enhancement in arterial phase, mild-to-moderate enhancement in portal and equilibrium phases. Tumor-feeding artery was demonstrated from the right hepatic artery by the three-dimensional reconstruction images. Biopsy was performed, and a diagnosis of PEComa was rendered. No intervention for this tumor before liver biopsy. We present a rare case of hepatic PEComa. The information we provided is useful for summarizing the CT features of this kind of tumors. It should be included in differential diagnoses from common hypervascular neoplasms of liver. The final diagnosis is established on histopathological and immunohistochemical studies that are the "gold standard."

Computed tomography imaging features of hepatic perivascular epithelioid cell tumor

PubMed Central

Han, Xu; Sun, Mei-Yu; Liu, Jing-Hong; Zhang, Xiao-Yan; Wang, Meng-Yao; Fan, Rui; Qamar, Sahrish

2017-01-01

Abstract Rationale: Perivascular epithelioid cell tumor (PEComa) is a rare tumor which is most frequently found in uterus. The tumor arising from liver is extremely uncommon. Patient concerns: A 36-year-old female with abdominal distention, cramps, and low-grade fever for over 15 days. The patient had a history of gastric adenocarcinoma with ovarian, celiac lymph nodes, and retroperitoneal lymph nodes metastases. Diagnoses: Computed tomography (CT) imaging demonstrated an ill-defined heterogeneous hypo-dense mass in segment 8 (S8) of the liver. Contrast-enhanced CT imaging showed marked enhancement in arterial phase, mild-to-moderate enhancement in portal and equilibrium phases. Tumor-feeding artery was demonstrated from the right hepatic artery by the three-dimensional reconstruction images. Biopsy was performed, and a diagnosis of PEComa was rendered. Interventions: No intervention for this tumor before liver biopsy. Lessons: We present a rare case of hepatic PEComa. The information we provided is useful for summarizing the CT features of this kind of tumors. It should be included in differential diagnoses from common hypervascular neoplasms of liver. The final diagnosis is established on histopathological and immunohistochemical studies that are the “gold standard.” PMID:29245304

A new method for detecting small and dim targets in starry background

NASA Astrophysics Data System (ADS)

Yao, Rui; Zhang, Yanning; Jiang, Lei

2011-08-01

Small visible optical space targets detection is one of the key issues in the research of long-range early warning and space debris surveillance. The SNR(Signal to Noise Ratio) of the target is very low because of the self influence of image device. Random noise and background movement also increase the difficulty of target detection. In order to detect small visible optical space targets effectively and rapidly, we bring up a novel detecting method based on statistic theory. Firstly, we get a reasonable statistical model of visible optical space image. Secondly, we extract SIFT(Scale-Invariant Feature Transform) feature of the image frames, and calculate the transform relationship, then use the transform relationship to compensate whole visual field's movement. Thirdly, the influence of star was wiped off by using interframe difference method. We find segmentation threshold to differentiate candidate targets and noise by using OTSU method. Finally, we calculate statistical quantity to judge whether there is the target for every pixel position in the image. Theory analysis shows the relationship of false alarm probability and detection probability at different SNR. The experiment result shows that this method could detect target efficiently, even the target passing through stars.

Lung aeration on post-mortem magnetic resonance imaging is a useful marker of live birth versus stillbirth.

PubMed

Barber, Joy L; Sebire, Neil J; Chitty, Lyn S; Taylor, Andrew M; Arthurs, Owen J

2015-05-01

Aim of this study was to investigate whether lung assessment on post-mortem magnetic resonance imaging (PMMR) can reliably differentiate between live birth and stillbirth. We retrospectively assessed PMMR imaging features of a group of late foetal terminations following fetocide and stillbirths (without witnessed breathing) and early infant deaths (breathed spontaneously before death). PMMR images were reviewed for evidence of lung aeration and other features, blinded to the clinical and autopsy data. Nineteen infant deaths (mean age 3.0 ± 6.5 post-natal weeks) and 23 foetal terminations or stillbirths (mean age 32.6 ± 10.2-week gestation) were compared. Subjective appearances of lung aeration on PMMR were the best indicator of live birth, with a sensitivity of 89.5% (95% confidence interval 68.6, 97.1%) and specificity of 95.6% (79.0, 99.2%) and positive and negative predictive values of 94.4% and 91.7%, respectively. Lung aeration on PMMR appears to have high overall accuracy for confirmation of live birth versus intrauterine foetal death but now requires validating in a larger cohort of perinatal deaths.

Differentiating characteristic microstructural features of cancerous tissues using Mueller matrix microscope.

PubMed

Wang, Ye; He, Honghui; Chang, Jintao; Zeng, Nan; Liu, Shaoxiong; Li, Migao; Ma, Hui

2015-12-01

Polarized light imaging can provide rich microstructural information of samples, and has been applied to the detections of various abnormal tissues. In this paper, we report a polarized light microscope based on Mueller matrix imaging by adding the polarization state generator and analyzer (PSG and PSA) to a commercial transmission optical microscope. The maximum errors for the absolute values of Mueller matrix elements are reduced to 0.01 after calibration. This Mueller matrix microscope has been used to examine human cervical and liver cancerous tissues with fibrosis. Images of the transformed Mueller matrix parameters provide quantitative assessment on the characteristic features of the pathological tissues. Contrast mechanism of the experimental results are backed up by Monte Carlo simulations based on the sphere-cylinder birefringence model, which reveal the relationship between the pathological features in the cancerous tissues at the cellular level and the polarization parameters. Both the experimental and simulated data indicate that the microscopic transformed Mueller matrix parameters can distinguish the breaking down of birefringent normal tissues for cervical cancer, or the formation of birefringent surrounding structures accompanying the inflammatory reaction for liver cancer. With its simple structure, fast measurement and high precision, polarized light microscope based on Mueller matrix shows a good diagnosis application prospect. Copyright © 2015 Elsevier Ltd. All rights reserved.

Low-grade central osteosarcoma of distal femur, resembling fibrous dysplasia

PubMed Central

Vasiliadis, Haris S; Arnaoutoglou, Christina; Plakoutsis, Sotiris; Doukas, Michalis; Batistatou, Anna; Xenakis, Theodoros A

2013-01-01

We report a case of a 32 year-old male, admitted for a lytic lesion of the distal femur. One month after the first X-ray, clinical and imaging deterioration was evident. Open biopsy revealed fibrous dysplasia. Three months later, the lytic lesion had spread to the whole distal third of the femur reaching the articular cartilage. The malignant clinical and imaging features necessitated excision of the lesion and reconstruction with a custom-made total knee arthroplasty. Intra-operatively, no obvious soft tissue infiltration was evident. Nevertheless, an excision of the distal 15.5 cm of the femur including 3.0 cm of the surrounding muscles was finally performed. The histological examination of the excised specimen revealed central low-grade osteosarcoma. Based on the morphological features of the excised tumor, allied to the clinical findings, the diagnosis of low-grade central osteosarcoma was finally made although characters of a fibrous dysplasia were apparent. Central low-grade osteosarcoma is a rare, well-differentiated sub-type of osteosarcoma, with clinical, imaging, and histological features similar to benign tumours. Thus, initial misdiagnosis is usual with the condition commonly mistaken for fibrous dysplasia. Central low-grade osteosarcoma is usually treated with surgery alone, with rare cases of distal metastases. However, regional recurrence is quite frequent after close margin excision. PMID:24147271

Characterization and recognition of mixed emotional expressions in thermal face image

NASA Astrophysics Data System (ADS)

Saha, Priya; Bhattacharjee, Debotosh; De, Barin K.; Nasipuri, Mita

2016-05-01

Facial expressions in infrared imaging have been introduced to solve the problem of illumination, which is an integral constituent of visual imagery. The paper investigates facial skin temperature distribution on mixed thermal facial expressions of our created face database where six are basic expressions and rest 12 are a mixture of those basic expressions. Temperature analysis has been performed on three facial regions of interest (ROIs); periorbital, supraorbital and mouth. Temperature variability of the ROIs in different expressions has been measured using statistical parameters. The temperature variation measurement in ROIs of a particular expression corresponds to a vector, which is later used in recognition of mixed facial expressions. Investigations show that facial features in mixed facial expressions can be characterized by positive emotion induced facial features and negative emotion induced facial features. Supraorbital is a useful facial region that can differentiate basic expressions from mixed expressions. Analysis and interpretation of mixed expressions have been conducted with the help of box and whisker plot. Facial region containing mixture of two expressions is generally less temperature inducing than corresponding facial region containing basic expressions.

Image texture segmentation using a neural network

NASA Astrophysics Data System (ADS)

Sayeh, Mohammed R.; Athinarayanan, Ragu; Dhali, Pushpuak

1992-09-01

In this paper we use a neural network called the Lyapunov associative memory (LYAM) system to segment image texture into different categories or clusters. The LYAM system is constructed by a set of ordinary differential equations which are simulated on a digital computer. The clustering can be achieved by using a single tuning parameter in the simplest model. Pattern classes are represented by the stable equilibrium states of the system. Design of the system is based on synthesizing two local energy functions, namely, the learning and recall energy functions. Before the implementation of the segmentation process, a Gauss-Markov random field (GMRF) model is applied to the raw image. This application suitably reduces the image data and prepares the texture information for the neural network process. We give a simple image example illustrating the capability of the technique. The GMRF-generated features are also used for a clustering, based on the Euclidean distance.

Direct Imaging Detection of Methane in the Atmosphere of GJ 504 b

NASA Technical Reports Server (NTRS)

Janson, Markus; Brandt, Timothy; Kuzuhara, Masayuki; Spiegel, David; Thalmann, Christian; Currie, Thayne; Bonnefoy, Mickael; Zimmerman, Neil; Sorahana, Satoko; Kotani, Takayuki;

A note on the stability and discriminability of graph-based features for classification problems in digital pathology

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Xu, Jun; Madabhushi, Anant

2015-01-01

Nuclear architecture or the spatial arrangement of individual cancer nuclei on histopathology images has been shown to be associated with different grades and differential risk for a number of solid tumors such as breast, prostate, and oropharyngeal. Graph-based representations of individual nuclei (nuclei representing the graph nodes) allows for mining of quantitative metrics to describe tumor morphology. These graph features can be broadly categorized into global and local depending on the type of graph construction method. While a number of local graph (e.g. Cell Cluster Graphs) and global graph (e.g. Voronoi, Delaunay Triangulation, Minimum Spanning Tree) features have been shown to associated with cancer grade, risk, and outcome for different cancer types, the sensitivity of the preceding segmentation algorithms in identifying individual nuclei can have a significant bearing on the discriminability of the resultant features. This therefore begs the question as to which features while being discriminative of cancer grade and aggressiveness are also the most resilient to the segmentation errors. These properties are particularly desirable in the context of digital pathology images, where the method of slide preparation, staining, and type of nuclear segmentation algorithm employed can all dramatically affect the quality of the nuclear graphs and corresponding features. In this paper we evaluated the trade off between discriminability and stability of both global and local graph-based features in conjunction with a few different segmentation algorithms and in the context of two different histopathology image datasets of breast cancer from whole-slide images (WSI) and tissue microarrays (TMA). Specifically in this paper we investigate a few different performance measures including stability, discriminability and stability vs discriminability trade off, all of which are based on p-values from the Kruskal-Wallis one-way analysis of variance for local and global graph features. Apart from identifying the set of local and global features that satisfied the trade off between stability and discriminability, our most interesting finding was that a simple segmentation method was sufficient to identify the most discriminant features for invasive tumour detection in TMAs, whereas for tumour grading in WSI, the graph based features were more sensitive to the accuracy of the segmentation algorithm employed.

Precision Topography of Pluvial Features in Nevada as Analogs for Possible Pluvial Landforms on Mars

NASA Astrophysics Data System (ADS)

Zimbelman, J. R.; Garry, W. B.; Irwin, R. P.

2009-12-01

Topographic measurements with better than 2 cm horizontal and 4 cm vertical precision were obtained for pluvial features in Nevada using a Trimble R8 Differential Global Positioning System (DGPS), making use of both real-time kinematic and post-processed kinematic techniques. We collected ten transects across shorelines in the southern end of Surprise Valley, near the California border in NW Nevada, on April 15-17, 2008, plus five transects of shorelines and eight transects of a wavecut scarp in Long Valley, near the Utah border in NE Nevada, on May 5-7, 2009. Each transect consists of topographic points keyed to field notes and photographs. In Surprise Valley, the highstand shoreline was noted at 1533.4 m elevation in 8 of the 10 transects, and several prominent intermediate shorelines could be correlated between two or more transects. In Long Valley, the well preserved highstand shoreline elevation of 1908.7 m correlated (within 0.6 m) to the base of the wavecut scarp along a horizontal distance of 1.2 km. These results demonstrate that adherence to a geopotential elevation level is one of the strongest indicators that a possible shoreline feature is the result of pluvial processes, and that elevation levels of features can be clearly detected and documented with precise topographic measurements. The High Resolution Imaging Science Experiment (HiRISE) is returning images of Mars that show potential shoreline features in remarkable detail (e.g., image PSP_009998_2165, 32 cm/pixel, showing a possible shoreline in NW Arabia). Our results from studying shorelines in Nevada will provide a basis for evaluating the plausibility of possible shoreline features on Mars, the implications of which are significant for the overall history of Mars.

Simulating Photo-Refraction Images of Keratoconus and Near-Sightedness Eyes

NASA Astrophysics Data System (ADS)

Baker, Kevin; Lewis, James W. L.; Chen, Ying-Ling

2004-11-01

Keratoconus is an abnormal condition of the eye resulting from cone-shaped features on the cornea that degrade the quality of vision. These corneal features result from thinning and subsequent bulging due to intraocular pressure. The abnormal corneal curvature increases the refractive power asymmetrically and can be misdiagnosed by examiners as astigmatism and nearsightedness. Since corrective treatment is possible, early detection of this condition is desirable. Photo-refraction (PR) detects the retinal irradiance reflected from a single light source and is an inexpensive method used to identify refractive errors. For near- (far-) sighted eye, a crescent appears on the same (opposite) side of the light source. The capability of a PR device to detect keratoconus and to differentiate this condition from myopia was investigated. Using a commercial optical program, synthetic eye models were constructed for both near-sighted and keratoconus eyes. PR images of various eye conditions were calculated. The keratoconus cone shapes were modeled with typical published cone locations and sizes. The results indicate significant differences between the images of keratoconus and near-sighted eyes.

Gravitational lensing by ring-like structures

NASA Astrophysics Data System (ADS)

Lake, Ethan; Zheng, Zheng

2017-02-01

We study a class of gravitational lensing systems consisting of an inclined ring/belt, with and without an added point mass at the centre. We show that a common feature of such systems are so-called pseudo-caustics, across which the magnification of a point source changes discontinuously and yet remains finite. Such a magnification change can be associated with either a change in image multiplicity or a sudden change in the size of a lensed image. The existence of pseudo-caustics and the complex interplay between them and the formal caustics (which correspond to points of infinite magnification) can lead to interesting consequences, such as truncated or open caustics and a non-conservation of total image parity. The origin of the pseudo-caustics is found to be the non-differentiability of the solutions to the lens equation across the ring/belt boundaries, with the pseudo-caustics corresponding to ring/belt boundaries mapped into the source plane. We provide a few illustrative examples to understand the pseudo-caustic features, and in a separate paper consider a specific astronomical application of our results to study microlensing by extrasolar asteroid belts.

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images.

PubMed

Joutsijoki, Henry; Haponen, Markus; Rasku, Jyrki; Aalto-Setälä, Katriina; Juhola, Martti

2016-01-01

The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using a k-NN classifier showing improved accuracy compared to earlier studies.

Imaging findings in a case of Gorlin-Goltz syndrome: a survey using advanced modalities

PubMed Central

Shakibafar, Ali Reza; Houshyar, Maneli; Nafarzade, Shima

2011-01-01

Gorlin-Goltz syndrome is an infrequent multi-systemic disease which is characterized by multiple keratocysts in the jaws, calcification of falx cerebri, and basal cell carcinomas. We report a case of Gorlin-Goltz syndrome in a 23-year-old man with emphasis on image findings of keratocyctic odontogenic tumors (KCOTs) on panoramic radiograph, computed tomography, magnetic resonance (MR) imaging, and Ultrasonography (US). In this case, pericoronal lesions were mostly orthokeratinized odontogenic cyst (OOC) concerning the MR and US study, which tended to recur less. The aim of this report was to clarify the characteristic imaging features of the syndrome-related keratocysts that can be used to differentiate KCOT from OOC. Also, our findings suggested that the recurrence rate of KCOTs might be predicted based on their association to teeth. PMID:22232727

Integrated quantitative phase and birefringence microscopy for imaging malaria-infected red blood cells.

PubMed

Li, Chengshuai; Chen, Shichao; Klemba, Michael; Zhu, Yizheng

2016-09-01

A dual-modality birefringence/phase imaging system is presented. The system features a crystal retarder that provides polarization mixing and generates two interferometric carrier waves in a single signal spectrum. The retardation and orientation of sample birefringence can then be measured simultaneously based on spectral multiplexing interferometry. Further, with the addition of a Nomarski prism, the same setup can be used for quantitative differential interference contrast (DIC) imaging. Sample phase can then be obtained with two-dimensional integration. In addition, birefringence-induced phase error can be corrected using the birefringence data. This dual-modality approach is analyzed theoretically with Jones calculus and validated experimentally with malaria-infected red blood cells. The system generates not only corrected DIC and phase images, but a birefringence map that highlights the distribution of hemozoin crystals.

Diagnosis of a sigmoid volvulus in pregnancy: ultrasonography and magnetic resonance imaging findings

PubMed Central

Palmucci, Stefano; Lanza, Maria Letizia; Gulino, Fabrizio; Scilletta, Beniamino; Ettorre, Giovanni Carlo

2014-01-01

Sigmoid volvulus complicating pregnancy is a rare, non-obstetric cause of abdominal pain that requires prompt surgical intervention (decompression) to avoid intestinal ischemia and perforation. We report the case of a 31-week pregnant woman with abdominal pain and subsequent development of constipation. Preoperative diagnosis was achieved using magnetic resonance imaging and ultrasonography: the large bowel distension and a typical whirl sign - near a sigmoid colon transition point - suggested the diagnosis of sigmoid volvulus. The decision to refer the patient for emergency laparotomy was adopted without any ionizing radiation exposure, and the pre-operative diagnosis was confirmed after surgery. Imaging features of sigmoid volvulus and differential diagnosis from other non-obstetric abdominal emergencies in pregnancy are discussed in our report, with special emphasis on the diagnostic capabilities of ultrasonography and magnetic resonance imaging. PMID:24967020

Diagnosis of a sigmoid volvulus in pregnancy: ultrasonography and magnetic resonance imaging findings.

PubMed

Palmucci, Stefano; Lanza, Maria Letizia; Gulino, Fabrizio; Scilletta, Beniamino; Ettorre, Giovanni Carlo

2014-02-01

Sigmoid volvulus complicating pregnancy is a rare, non-obstetric cause of abdominal pain that requires prompt surgical intervention (decompression) to avoid intestinal ischemia and perforation. We report the case of a 31-week pregnant woman with abdominal pain and subsequent development of constipation. Preoperative diagnosis was achieved using magnetic resonance imaging and ultrasonography: the large bowel distension and a typical whirl sign - near a sigmoid colon transition point - suggested the diagnosis of sigmoid volvulus. The decision to refer the patient for emergency laparotomy was adopted without any ionizing radiation exposure, and the pre-operative diagnosis was confirmed after surgery. Imaging features of sigmoid volvulus and differential diagnosis from other non-obstetric abdominal emergencies in pregnancy are discussed in our report, with special emphasis on the diagnostic capabilities of ultrasonography and magnetic resonance imaging.

Computed tomography in children with community-acquired pneumonia.

PubMed

Andronikou, Savvas; Goussard, Pierre; Sorantin, Erich

2017-10-01

Diagnostic imaging plays a significant role in both the diagnosis and treatment of complications of pneumonia in children and chest radiography is the imaging modality of choice. Computed tomography (CT) on the other hand, is not currently a first-line imaging tool for children with suspected uncomplicated community-acquired pneumonia and is largely reserved for when complications of pneumonia are suspected or there is difficulty in differentiating pneumonia from other pathology. This review outlines the situations where CT needs to be considered in children with pneumonia, describes the imaging features of the parenchymal and pleural complications of pneumonia, discusses how CT may have a wider role in developing countries where human immunodeficiency virus (HIV) and tuberculosis are prevalent, makes note of the role of CT scanning for identifying missed foreign body aspiration and, lastly, addresses radiation concerns.

Integrated quantitative phase and birefringence microscopy for imaging malaria-infected red blood cells

NASA Astrophysics Data System (ADS)

Li, Chengshuai; Chen, Shichao; Klemba, Michael; Zhu, Yizheng

2016-09-01

A dual-modality birefringence/phase imaging system is presented. The system features a crystal retarder that provides polarization mixing and generates two interferometric carrier waves in a single signal spectrum. The retardation and orientation of sample birefringence can then be measured simultaneously based on spectral multiplexing interferometry. Further, with the addition of a Nomarski prism, the same setup can be used for quantitative differential interference contrast (DIC) imaging. Sample phase can then be obtained with two-dimensional integration. In addition, birefringence-induced phase error can be corrected using the birefringence data. This dual-modality approach is analyzed theoretically with Jones calculus and validated experimentally with malaria-infected red blood cells. The system generates not only corrected DIC and phase images, but a birefringence map that highlights the distribution of hemozoin crystals.

Pitfalls in soft tissue sarcoma imaging: chronic expanding hematomas.

PubMed

Jahed, Kiarash; Khazai, Behnaz; Umpierrez, Monica; Subhawong, Ty K; Singer, Adam D

2018-01-01

Solid or nodular enhancement is typical of soft tissue sarcomas although high grade soft tissue sarcomas and those with internal hemorrhage often appear heterogeneous with areas of nonenhancement and solid or nodular enhancement. These MRI findings often prompt an orthopedic oncology referral, a biopsy or surgery. However, not all masses with these imaging findings are malignant. We report the multimodality imaging findings of two surgically proven chronic expanding hematomas (CEH) with imaging features that mimicked sarcomas. A third case of nonenhancing CEH of the lower extremity is also presented as a comparison. It is important that in the correct clinical scenario with typical imaging findings, the differential diagnosis of a chronic expanding hematoma be included in the workup of these patients. An image-guided biopsy of nodular tissue within such masses that proves to be negative for malignancy should not necessarily be considered discordant. A correct diagnosis may prevent a morbid unnecessary surgery and may indicate the need for a conservative noninvasive follow-up with imaging.

Imaging of cardiovascular structures using near-infrared femtosecond multiphoton laser scanning microscopy.

PubMed

Schenke-Layland, Katja; Riemann, Iris; Stock, Ulrich A; König, Karsten

2005-01-01

Multiphoton imaging represents a novel and very promising medical diagnostic technology for the high-resolution analysis of living biological tissues. We performed multiphoton imaging to analyzed structural features of extracellular matrix (ECM) components, e.g., collagen and elastin, of vital pulmonary and aortic heart valves. High-resolution autofluorescence images of collagenous and elastic fibers were demonstrated using multifluorophore, multiphoton excitation at two different wavelengths and optical sectioning, without the requirement of embedding, fixation, or staining. Collagenous structures were selectively imaged by detection of second harmonic generation (SHG). Additionally, routine histology and electron microscopy were integrated to verify the observed results. In comparison with pulmonary tissues, aortic heart valve specimens show very similar matrix formations. The quality of the resulting three-dimensional (3-D) images enabled the differentiation between collagenous and elastic fibers. These experimental results indicate that multiphoton imaging with near-infrared (NIR) femtosecond laser pulses may prove to be a useful tool for the nondestructive monitoring and characterization of cardiovascular structures. Copyright 2005 Society of Photo-Optical Instrumentation Engineers.

Three recipes for improving the image quality with optical long-baseline interferometers: BFMC, LFF, and DPSC

NASA Astrophysics Data System (ADS)

Millour, Florentin A.; Vannier, Martin; Meilland, Anthony

2012-07-01

We present here three recipes for getting better images with optical interferometers. Two of them, Low- Frequencies Filling and Brute-Force Monte Carlo were used in our participation to the Interferometry Beauty Contest this year and can be applied to classical imaging using V2 and closure phases. These two addition to image reconstruction provide a way of having more reliable images. The last recipe is similar in its principle as the self-calibration technique used in radio-interferometry. We call it also self-calibration, but it uses the wavelength-differential phase as a proxy of the object phase to build-up a full-featured complex visibility set of the observed object. This technique needs a first image-reconstruction run with an available software, using closure-phases and squared visibilities only. We used it for two scientific papers with great success. We discuss here the pros and cons of such imaging technique.

[Diagnostic imaging of high-grade astrocytoma: heterogeneity of clinical manifestation, image characteristics, and histopathological findings].

PubMed

Okajima, Kaoru; Ohta, Yoshio

2012-10-01

Recent developments in diagnostic radiology, which have enabled accurate differential diagnoses of brain tumors, have been well described in the last three decades. MR and PET imaging can also provide information to predict histological grades and prognoses that might influence treatment strategies. However, high-grade astrocytomas consist of many different subtypes that are associated with different imaging and histological characteristics. Hemorrhage and necrosis results in a variety of imaging features, and infiltrative tumor growth entrapping normal neurons may cause different clinical manifestations. We reviewed patients with high-grade astrocytomas that showed various imaging characteristics, with special emphasis on initial symptoms and histological features. Clinicopathological characteristics of astrocytomas were also compared with other malignant tumors. Neurological deficits were not notable in patients with grade 3-4 astrocytomas when they showed infiltrative tumor growth, while brain metastases with compact cellular proliferation caused more neurological symptoms. Infiltrative tumors did not show any enhancing masses on MR imaging, but these tumors may show intratumor heterogeneity. Seizures were reported to be more frequent in low-grade glioma and in secondary glioblastoma. Tumor heterogeneity was also reported in molecular genetic profile, and investigators identified some subsets of astrocytomas. They investigated IHD1/2 mutation, EGFR amplification, TP53 mutation, Ki-67 index, etc. In summary, high-grade astrocytomas are not homogenous groups of tumors, and this is associated with the heterogeneity of clinical manifestation, image characteristics, and histopathological findings. Molecular studies may explain the tumor heterogeneity in the near future.

Analysis of short single rest/activation epoch fMRI by self-organizing map neural network

NASA Astrophysics Data System (ADS)

Erberich, Stephan G.; Dietrich, Thomas; Kemeny, Stefan; Krings, Timo; Willmes, Klaus; Thron, Armin; Oberschelp, Walter

2000-04-01

Functional magnet resonance imaging (fMRI) has become a standard non invasive brain imaging technique delivering high spatial resolution. Brain activation is determined by magnetic susceptibility of the blood oxygen level (BOLD effect) during an activation task, e.g. motor, auditory and visual tasks. Usually box-car paradigms have 2 - 4 rest/activation epochs with at least an overall of 50 volumes per scan in the time domain. Statistical test based analysis methods need a large amount of repetitively acquired brain volumes to gain statistical power, like Student's t-test. The introduced technique based on a self-organizing neural network (SOM) makes use of the intrinsic features of the condition change between rest and activation epoch and demonstrated to differentiate between the conditions with less time points having only one rest and one activation epoch. The method reduces scan and analysis time and the probability of possible motion artifacts from the relaxation of the patients head. Functional magnet resonance imaging (fMRI) of patients for pre-surgical evaluation and volunteers were acquired with motor (hand clenching and finger tapping), sensory (ice application), auditory (phonological and semantic word recognition task) and visual paradigms (mental rotation). For imaging we used different BOLD contrast sensitive Gradient Echo Planar Imaging (GE-EPI) single-shot pulse sequences (TR 2000 and 4000, 64 X 64 and 128 X 128, 15 - 40 slices) on a Philips Gyroscan NT 1.5 Tesla MR imager. All paradigms were RARARA (R equals rest, A equals activation) with an epoch width of 11 time points each. We used the self-organizing neural network implementation described by T. Kohonen with a 4 X 2 2D neuron map. The presented time course vectors were clustered by similar features in the 2D neuron map. Three neural networks were trained and used for labeling with the time course vectors of one, two and all three on/off epochs. The results were also compared by using a Kolmogorov-Smirnov statistical test of all 66 time points. To remove non- periodical time courses from training an auto-correlation function and bandwidth limiting Fourier filtering in combination with Gauss temporal smoothing was used. None of the trained maps, with one, two and three epochs, were significantly different which indicates that the feature space of only one on/off epoch is sufficient to differentiate between the rest and task condition. We found, that without pre-processing of the data no meaningful results can be achieved because of the huge amount of the non-activated and background voxels represents the majority of the features and is therefore learned by the SOM. Thus it is crucial to remove unnecessary capacity load of the neural network by selection of the training input, using auto-correlation function and/or Fourier spectrum analysis. However by reducing the time points to one rest and one activation epoch either strong auto- correlation or a precise periodical frequency is vanishing. Self-organizing maps can be used to separate rest and activation epochs of with only a 1/3 of the usually acquired time points. Because of the nature of the SOM technique, the pattern or feature separation, only the presence of a state change between the conditions is necessary for differentiation. Also the variance of the individual hemodynamic response function (HRF) and the variance of the spatial different regional cerebral blood flow (rCBF) is learned from the subject and not compared with a fixed model done by statistical evaluation. We found that reducing the information to only a few time points around the BOLD effect was not successful due to delays of rCBF and the insufficient extension of the BOLD feature in the time space. Especially for patient routine observation and pre-surgical planing a reduced scan time is of interest.

Low-temperature scanning tunneling microscopy of ring-like surface electronic structures around Co islands on InAs(110) surfaces.

PubMed

Muzychenko, D A; Schouteden, K; Savinov, S V; Maslova, N S; Panov, V I; Van Haesendonck, C

2009-08-01

We report on the experimental observation by scanning tunneling microscopy at low temperature of ring-like features that appear around Co metal islands deposited on a clean (110) oriented surface of cleaved p-type InAs crystals. These features are visible in spectroscopic images within a certain range of negative tunneling bias voltages due to the presence of a negative differential conductance in the current-voltage dependence. A theoretical model is introduced, which takes into account non-equilibrium effects in the small tunneling junction area. In the framework of this model the appearance of the ring-like features is explained in terms of interference effects between electrons tunneling directly and indirectly (via a Co island) between the tip and the InAs surface.

Imaging pigment chemistry in melanocytic conjunctival lesions with pump-probe microscopy

NASA Astrophysics Data System (ADS)

Wilson, Jesse W.; Vajzovic, Lejla; Robles, Francisco E.; Cummings, Thomas J.; Mruthyunjaya, Prithvi; Warren, Warren S.

2013-03-01

We extend nonlinear pump-probe microscopy, recently demonstrated to image the microscopic distribution of eumelanin and pheomelanin in unstained skin biopsy sections, to the case of melanocytic conjunctival lesions. The microscopic distribution of pigmentation chemistry serves as a functional indicator of melanocyte activity. In these conjunctival specimens (benign nevi, primary acquired melanoses, and conjunctival melanoma), we have observed pump-probe spectroscopic signatures of eumelanin, pheomelanin, hemoglobin, and surgical ink, in addition to important structural features that differentiate benign from malignant lesions. We will also discuss prospects for an in vivo `optical biopsy' to provide additional information before having to perform invasive procedures.

Multimodal optical coherence tomography for in vivo imaging of brain tissue structure and microvascular network at glioblastoma

NASA Astrophysics Data System (ADS)

Yashin, Konstantin S.; Kiseleva, Elena B.; Gubarkova, Ekaterina V.; Matveev, Lev A.; Karabut, Maria M.; Elagin, Vadim V.; Sirotkina, Marina A.; Medyanik, Igor A.; Kravets, L. Y.; Gladkova, Natalia D.

2017-02-01

In the case of infiltrative brain tumors the surgeon faces difficulties in determining their boundaries to achieve total resection. The aim of the investigation was to evaluate the performance of multimodal OCT (MM OCT) for differential diagnostics of normal brain tissue and glioma using an experimental model of glioblastoma. The spectral domain OCT device that was used for the study provides simultaneously two modes: cross-polarization and microangiographic OCT. The comparative analysis of the both OCT modalities images from tumorous and normal brain tissue areas concurrently with histologic correlation shows certain difference between when accordingly to morphological and microvascular tissue features.

Herpes simplex encephalitis with thalamic, brainstem and cerebellar involvement.

PubMed

Garg, Meenal; Kulkarni, Shilpa; Udwadia Hegde, Anaita

2018-04-01

Herpes simplex virus encephalitis is a common and treatable cause of acute encephalitis in all age groups. Certain radiological features such as temporal parenchymal involvement facilitate the diagnosis. The use of herpes simplex virus polymerase chain reaction has expanded the clinical and imaging spectrum. We report the case of a young patient who presented with a movement disorder and predominant involvement of thalami, brainstem and cerebellum on magnetic resonance imaging, and was diagnosed with herpes simplex virus encephalitis. Differentiation from Japanese encephalitis may be difficult in these patients, especially in endemic areas, and may necessitate the use of relevant investigations in all patients.

Radiological Findings in a case of Advance staged Mesothelioma

PubMed Central

Aziz, Fahad

2009-01-01

Chest X Ray is the initial screening test for the mesothelioma like all other the chest diseases. But computed tomography (CT) is the imaging technique of choice for charactering pleural masses. CT also gives important information regarding invasion of the chest wall and surrounding structures. Certain CT features help differentiate benign from malignant processes. This short article highlights the salient CT appearance of mesothelioma; the most common pleural tumor. PMID:22263002

A new 3D texture feature based computer-aided diagnosis approach to differentiate pulmonary nodules

NASA Astrophysics Data System (ADS)

Han, Fangfang; Wang, Huafeng; Song, Bowen; Zhang, Guopeng; Lu, Hongbing; Moore, William; Zhao, Hong; Liang, Zhengrong

2013-02-01

To distinguish malignant pulmonary nodules from benign ones is of much importance in computer-aided diagnosis of lung diseases. Compared to many previous methods which are based on shape or growth assessing of nodules, this proposed three-dimensional (3D) texture feature based approach extracted fifty kinds of 3D textural features from gray level, gradient and curvature co-occurrence matrix, and more derivatives of the volume data of the nodules. To evaluate the presented approach, the Lung Image Database Consortium public database was downloaded. Each case of the database contains an annotation file, which indicates the diagnosis results from up to four radiologists. In order to relieve partial-volume effect, interpolation process was carried out to those volume data with image slice thickness more than 1mm, and thus we had categorized the downloaded datasets to five groups to validate the proposed approach, one group of thickness less than 1mm, two types of thickness range from 1mm to 1.25mm and greater than 1.25mm (each type contains two groups, one with interpolation and the other without). Since support vector machine is based on statistical learning theory and aims to learn for predicting future data, so it was chosen as the classifier to perform the differentiation task. The measure on the performance was based on the area under the curve (AUC) of Receiver Operating Characteristics. From 284 nodules (122 malignant and 162 benign ones), the validation experiments reported a mean of 0.9051 and standard deviation of 0.0397 for the AUC value on average over 100 randomizations.

Extramedullary plasmacytoma in the carotid space: Expanding the differential diagnosis.

PubMed

Deshpande, Sneha Satish; Kane, Shubhada; Arya, Supreeta

2014-10-01

Plasma cell neoplasms have been classified into various types, with a range of clinical and radiological presentations. Extramedullary plasmacytoma (EMP) is a subset of plasma cell neoplasms which presents as an isolated non-osseous soft tissue mass. Though carotid space neoplasms are commonly encountered, EMP in the carotid space is rare and seldom considered in the initial differential diagnosis of a carotid space mass. These tumors can be treated by surgery or radiotherapy. On the other hand, the commonly encountered tumors in the carotid space are treated surgically. Also, it is mandatory to exclude multiple myeloma in the patients presenting with EMP. Hence, accurate and early diagnosis has therapeutic and prognostic implications. We report a rare case of EMP of the carotid space, describing the imaging features and the differential diagnoses with clues pointing to this rare entity.

[Imaging manifestations and pathologic basis for hepatic capsular retraction syndrome caused by benign and malignant liver tumors].

PubMed

Ou, Youkuan; Xiao, Enhua; Shang, Quanliang; Chen, Juan

2015-10-01

To investigate the imaging manifestations of CT, MRI and pathological basis for hepatic capsular retraction syndrome caused by benign and malignant liver tumors.
 CT or MRI images and pathological features for hepatic capsular retraction syndrome were retrospectively analyzed in 50 patients with benign and malignant liver tumors. Picture archive and communication system (PACS) was used to observe and compare the morphology, size, width, depth, edge of the capsular retraction and the status of liquid under the liver capsule. The structure, differentiation and proliferation of the tumor were analyzed under the microscope.
 There were malignant liver tumors in 44 patients and benign tumor in 6 patients. The smooth or rough for the edge of capsular retraction was significant difference between the benign tumors and the malignant tumors with three differentiated grades (all P<0.05). There were significant difference in the width and depth for capsule retraction with different amount of fibrous tissues (all P<0.05). The width and depth of capsule retraction were positively correlated to the size of the tumors (r=0.557, 0.309 respectively, both P<0.05).
 Benign and malignant hepatic tumors may appear capsule retraction syndrome, but there are morphological differences between them. The differences are closely related with the lesion size, differentiated degree of tumor and fibrous tissue proliferation.

The influence of stimulus format on drawing—a functional imaging study of decision making in portrait drawing

PubMed Central

Miall, R.C.; Nam, Se-Ho; Tchalenko, J.

2014-01-01

To copy a natural visual image as a line drawing, visual identification and extraction of features in the image must be guided by top-down decisions, and is usually influenced by prior knowledge. In parallel with other behavioral studies testing the relationship between eye and hand movements when drawing, we report here a functional brain imaging study in which we compared drawing of faces and abstract objects: the former can be strongly guided by prior knowledge, the latter less so. To manipulate the difficulty in extracting features to be drawn, each original image was presented in four formats including high contrast line drawings and silhouettes, and as high and low contrast photographic images. We confirmed the detailed eye–hand interaction measures reported in our other behavioral studies by using in-scanner eye-tracking and recording of pen movements with a touch screen. We also show that the brain activation pattern reflects the changes in presentation formats. In particular, by identifying the ventral and lateral occipital areas that were more highly activated during drawing of faces than abstract objects, we found a systematic increase in differential activation for the face-drawing condition, as the presentation format made the decisions more challenging. This study therefore supports theoretical models of how prior knowledge may influence perception in untrained participants, and lead to experience-driven perceptual modulation by trained artists. PMID:25128710

Differential diagnosis of primary nasopharyngeal lymphoma and nasopharyngeal carcinoma focusing on CT, MRI, and PET/CT.

PubMed

Cho, Kyu-Sup; Kang, Dae-Woon; Kim, Hak-Jin; Lee, Jong-Kil; Roh, Hwan-Jung

2012-04-01

No study has done a comparative analysis of radiologic imaging findings between primary nasopharyngeal lymphoma (PNL) and nasopharyngeal carcinoma (NPC). The purpose of this study was to analyze computed tomography (CT) and magnetic resonance (MR) images and to evaluate the maximum standardized uptake value (SUV max) of positron emission tomography (PET)/CT between PNL and NPC, knowing the imaging features that distinguish PNL from NPC. Cross-sectional study. University tertiary care facility. The authors analyzed the features on CT, MR imaging, and PET/CT of 16 patients diagnosed with PNL and 32 patients diagnosed with NPC histopathologically. Patients with PNL had a larger tumor volume and showed symmetry of tumor shape than did patients with NPC. Patients with PNL also had higher tumor homogeneity than NPC patients on CT, T2-weighted, and postcontrast MR images. All PNL patients showed a high degree of enhancement without invasion to the adjacent deep structure. The involvement of the Waldeyer ring was significantly higher in PNL patients. Cervical and retropharyngeal lymphadenopathy and PET/CT SUV max showed no significant difference between PNL and NPC. If the images present a bulky, symmetric nasopharyngeal mass with marked homogeneity, a high degree of enhancement, and a higher Waldeyer ring involvement combined with no invasion into the deep structure, PNL should be considered over NPC.

Cranial dystonia, blepharospasm and hemifacial spasm: clinical features and treatment, including the use of botulinum toxin.

PubMed Central

Kraft, S P; Lang, A E

1988-01-01

Blepharospasm, the most frequent feature of cranial dystonia, and hemifacial spasm are two involuntary movement disorders that affect facial muscles. The cause of blepharospasm and other forms of cranial dystonia is not known. Hemifacial spasm is usually due to compression of the seventh cranial nerve at its exit from the brain stem. Cranial dystonia may result in severe disability. Hemifacial spasm tends to be much less disabling but may cause considerable distress and embarrassment. Patients affected with these disorders are often mistakenly considered to have psychiatric problems. Although the two disorders are quite distinct pathophysiologically, therapy with botulinum toxin has proven very effective in both. We review the clinical features, proposed pathophysiologic features, differential diagnosis and treatment, including the use of botulinum toxin, of cranial dystonia and hemifacial spasm. Images Fig. 2 Fig. 3 PMID:3052771

Mammographic mass classification based on possibility theory

NASA Astrophysics Data System (ADS)

Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana

2017-03-01

Shape and margin features are very important for differentiating between benign and malignant masses in mammographic images. In fact, benign masses are usually round and oval and have smooth contours. However, malignant tumors have generally irregular shape and appear lobulated or speculated in margins. This knowledge suffers from imprecision and ambiguity. Therefore, this paper deals with the problem of mass classification by using shape and margin features while taking into account the uncertainty linked to the degree of truth of the available information and the imprecision related to its content. Thus, in this work, we proposed a novel mass classification approach which provides a possibility based representation of the extracted shape features and builds a possibility knowledge basis in order to evaluate the possibility degree of malignancy and benignity for each mass. For experimentation, the MIAS database was used and the classification results show the great performance of our approach in spite of using simple features.

Streamlining machine learning in mobile devices for remote sensing

NASA Astrophysics Data System (ADS)

Coronel, Andrei D.; Estuar, Ma. Regina E.; Garcia, Kyle Kristopher P.; Dela Cruz, Bon Lemuel T.; Torrijos, Jose Emmanuel; Lim, Hadrian Paulo M.; Abu, Patricia Angela R.; Victorino, John Noel C.

2017-09-01

Mobile devices have been at the forefront of Intelligent Farming because of its ubiquitous nature. Applications on precision farming have been developed on smartphones to allow small farms to monitor environmental parameters surrounding crops. Mobile devices are used for most of these applications, collecting data to be sent to the cloud for storage, analysis, modeling and visualization. However, with the issue of weak and intermittent connectivity in geographically challenged areas of the Philippines, the solution is to provide analysis on the phone itself. Given this, the farmer gets a real time response after data submission. Though Machine Learning is promising, hardware constraints in mobile devices limit the computational capabilities, making model development on the phone restricted and challenging. This study discusses the development of a Machine Learning based mobile application using OpenCV libraries. The objective is to enable the detection of Fusarium oxysporum cubense (Foc) in juvenile and asymptomatic bananas using images of plant parts and microscopic samples as input. Image datasets of attached, unattached, dorsal, and ventral views of leaves were acquired through sampling protocols. Images of raw and stained specimens from soil surrounding the plant, and sap from the plant resulted to stained and unstained samples respectively. Segmentation and feature extraction techniques were applied to all images. Initial findings show no significant differences among the different feature extraction techniques. For differentiating infected from non-infected leaves, KNN yields highest average accuracy, as opposed to Naive Bayes and SVM. For microscopic images using MSER feature extraction, KNN has been tested as having a better accuracy than SVM or Naive-Bayes.

Intramuscular Lipoma: A Review of the Literature

PubMed Central

McTighe, Shane; Chernev, Ivan

2014-01-01

Lipomas are the most common type of soft tissue mesenchymal tumors. They are typically located subcutaneously and consist of mature fatty tissue. When they occur under the enclosing fascia, they are called deep-seated lipomas. Infrequently, lipomas can arise inside the muscle and are called intramuscular lipomas. Intramuscular lipomas have been commonly investigated and categorized in the same group as other deep-seated and superficial lipomatous lesions. Their clinical, histological and imaging characteristics may resemble well-differentiated liposarcomas, further adding to the difficulties in the differential diagnosis. This article summarizes the available literature and describes the typical epidemiological, pathological and clinical features of intramuscular lipomas, as well as delineating their treatment and prognosis. PMID:25568733

Orbital superconductivity, defects, and pinned nematic fluctuations in the doped iron chalcogenide FeSe 0.45 Te 0.55

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

Sarkar, Saheli; Van Dyke, John; Sprau, Peter O.

We demonstrate that the differential conductance, dI/dV, measured via spectroscopic imaging scanning tunneling microscopy in the doped iron chalcogenide FeSe0.45Te0.55, possesses a series of characteristic features that allow one to extract the orbital structure of the superconducting gaps. This yields nearly isotropic superconducting gaps on the two holelike Fermi surfaces, and a strongly anisotropic gap on the electronlike Fermi surface. Moreover, we show that the pinning of nematic fluctuations by defects can give rise to a dumbbell-like spatial structure of the induced impurity bound states, and explains the related C-2 symmetry in the Fourier transformed differential conductance.

Differential Entropy Preserves Variational Information of Near-Infrared Spectroscopy Time Series Associated With Working Memory.

PubMed

Keshmiri, Soheil; Sumioka, Hidenubo; Yamazaki, Ryuji; Ishiguro, Hiroshi

2018-01-01

Neuroscience research shows a growing interest in the application of Near-Infrared Spectroscopy (NIRS) in analysis and decoding of the brain activity of human subjects. Given the correlation that is observed between the Blood Oxygen Dependent Level (BOLD) responses that are exhibited by the time series data of functional Magnetic Resonance Imaging (fMRI) and the hemoglobin oxy/deoxy-genation that is captured by NIRS, linear models play a central role in these applications. This, in turn, results in adaptation of the feature extraction strategies that are well-suited for discretization of data that exhibit a high degree of linearity, namely, slope and the mean as well as their combination, to summarize the informational contents of the NIRS time series. In this article, we demonstrate that these features are inefficient in capturing the variational information of NIRS data, limiting the reliability and the adequacy of the conclusion on their results. Alternatively, we propose the linear estimate of differential entropy of these time series as a natural representation of such information. We provide evidence for our claim through comparative analysis of the application of these features on NIRS data pertinent to several working memory tasks as well as naturalistic conversational stimuli.

Utility of Clinical Parameters and Multiparametric MRI as Predictive Factors for Differentiating Uterine Sarcoma From Atypical Leiomyoma.

PubMed

Bi, Qiu; Xiao, Zhibo; Lv, Fajin; Liu, Yao; Zou, Chunxia; Shen, Yiqing

2018-02-05

The objective of this study was to find clinical parameters and qualitative and quantitative magnetic resonance imaging (MRI) features for differentiating uterine sarcoma from atypical leiomyoma (ALM) preoperatively and to calculate predictive values for uterine sarcoma. Data from 60 patients with uterine sarcoma and 88 patients with ALM confirmed by surgery and pathology were collected. Clinical parameters, qualitative MRI features, diffusion-weighted imaging with apparent diffusion coefficient values, and quantitative parameters of dynamic contrast-enhanced MRI of these two tumor types were compared. Predictive values for uterine sarcoma were calculated using multivariable logistic regression. Patient clinical manifestations, tumor locations, margins, T2-weighted imaging signals, mean apparent diffusion coefficient values, minimum apparent diffusion coefficient values, and time-signal intensity curves of solid tumor components were obvious significant parameters for distinguishing between uterine sarcoma and ALM (all P <.001). Abnormal vaginal bleeding, tumors located mainly in the uterine cavity, ill-defined tumor margins, and mean apparent diffusion coefficient values of <1.272 × 10 -3  mm 2 /s were significant preoperative predictors of uterine sarcoma. When the overall scores of these four predictors were greater than or equal to 7 points, the sensitivity, the specificity, the accuracy, and the positive and negative predictive values were 88.9%, 99.9%, 95.7%, 97.0%, and 95.1%, respectively. The use of clinical parameters and multiparametric MRI as predictive factors was beneficial for diagnosing uterine sarcoma preoperatively. These findings could be helpful for guiding treatment decisions. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

[Clinicopathological study of non-Langerhans cell histiocytosis in central nervous system].

PubMed

Zhang, Tingting; Fu, Yongjuan; Lu, Dehong; Li, Cunjiang; Piao, Yueshan

2015-09-01

To explore the clinicopathological features and imaging characteristics of non-Langerhans cell histiocytosis in central nerve system, thus to facilitate the diagnosis and differential diagnosis. A total of ten cases were enrolled in the study, with seven cases of Rosai-Dorfman disease (RDD) and three cases of xanthoma disseminatum (XD). Data on the clinicopathological features, imaging, immunophenotype and prognosis were collected and analyzed. Seven patients with RDD, 5 males and 2 females with the mean age of 46.7 years old, all presented as dural-based or intraparenchymal hypo- to isointense lesions on T1 and T2 with post-contrast enhancement. The polymorphous admixture of histiocytes, lymphocytes and plasma cells was observed in a fibrous stroma, with emperipolesis of some histiocytes. The immunohistostaining of CD11c, CD68, MAC387 and S-100 was positive in the histiocytes, while the staining of CD1α was negative. Five patients recovered after the operation, while one patient died of the disease. All the 3 XD patients were female, with the median age of 20.7 years old. All XD patients presented as multiple intraparenchymal hypointense lesions on T1 and hyperintense lesions on T2 with post-contrast enhancement. The infiltration of foam-like histiocytes, a few Touton giant cells, lymphocytes and eosnophils was observed in all XD patients. The immunohistostaining of CD68 and CD11c was positive in the histiocytes and that of MAC387 partly positive, while the staining of S-100 and CD1α was negative. One XD patient survived well, while another one died of the disease. The diagnosis of RDD and XD should be based on their typical morphology and immunophenotype and should be differentiated from Langerhans cell histiocytosis and other non-Langerhans cell histiocytosis. Non-Langerhans cell histiocytosis in central nerve system often presents untypical clinical presentation and imaging features, thus the communication and cooperation between clinician and pathologist is needed.

Reliable structural information from multiscale decomposition with the Mellor-Brady filter

NASA Astrophysics Data System (ADS)

Szilágyi, Tünde; Brady, Michael

2009-08-01

Image-based medical diagnosis typically relies on the (poorly reproducible) subjective classification of textures in order to differentiate between diseased and healthy pathology. Clinicians claim that significant benefits would arise from quantitative measures to inform clinical decision making. The first step in generating such measures is to extract local image descriptors - from noise corrupted and often spatially and temporally coarse resolution medical signals - that are invariant to illumination, translation, scale and rotation of the features. The Dual-Tree Complex Wavelet Transform (DT-CWT) provides a wavelet multiresolution analysis (WMRA) tool e.g. in 2D with good properties, but has limited rotational selectivity. Also, it requires computationally-intensive steering due to the inherently 1D operations performed. The monogenic signal, which is defined in n >= 2D with the Riesz transform gives excellent orientation information without the need for steering. Recent work has suggested the Monogenic Riesz-Laplace wavelet transform as a possible tool for integrating these two concepts into a coherent mathematical framework. We have found that the proposed construction suffers from a lack of rotational invariance and is not optimal for retrieving local image descriptors. In this paper we show: 1. Local frequency and local phase from the monogenic signal are not equivalent, especially in the phase congruency model of a "feature", and so they are not interchangeable for medical image applications. 2. The accuracy of local phase computation may be improved by estimating the denoising parameters while maximizing a new measure of "featureness".

Coding and quantification of a facial expression for pain in lambs.

PubMed

Guesgen, M J; Beausoleil, N J; Leach, M; Minot, E O; Stewart, M; Stafford, K J

2016-11-01

Facial expressions are routinely used to assess pain in humans, particularly those who are non-verbal. Recently, there has been an interest in developing coding systems for facial grimacing in non-human animals, such as rodents, rabbits, horses and sheep. The aims of this preliminary study were to: 1. Qualitatively identify facial feature changes in lambs experiencing pain as a result of tail-docking and compile these changes to create a Lamb Grimace Scale (LGS); 2. Determine whether human observers can use the LGS to differentiate tail-docked lambs from control lambs and differentiate lambs before and after docking; 3. Determine whether changes in facial action units of the LGS can be objectively quantified in lambs before and after docking; 4. Evaluate effects of restraint of lambs on observers' perceptions of pain using the LGS and on quantitative measures of facial action units. By comparing images of lambs before (no pain) and after (pain) tail-docking, the LGS was devised in consultation with scientists experienced in assessing facial expression in other species. The LGS consists of five facial action units: Orbital Tightening, Mouth Features, Nose Features, Cheek Flattening and Ear Posture. The aims of the study were addressed in two experiments. In Experiment I, still images of the faces of restrained lambs were taken from video footage before and after tail-docking (n=4) or sham tail-docking (n=3). These images were scored by a group of five naïve human observers using the LGS. Because lambs were restrained for the duration of the experiment, Ear Posture was not scored. The scores for the images were averaged to provide one value per feature per period and then scores for the four LGS action units were averaged to give one LGS score per lamb per period. In Experiment II, still images of the faces nine lambs were taken before and after tail-docking. Stills were taken when lambs were restrained and unrestrained in each period. A different group of five human observers scored the images from Experiment II. Changes in facial action units were also quantified objectively by a researcher using image measurement software. In both experiments LGS scores were analyzed using a linear MIXED model to evaluate the effects of tail docking on observers' perception of facial expression changes. Kendall's Index of Concordance was used to measure reliability among observers. In Experiment I, human observers were able to use the LGS to differentiate docked lambs from control lambs. LGS scores significantly increased from before to after treatment in docked lambs but not control lambs. In Experiment II there was a significant increase in LGS scores after docking. This was coupled with changes in other validated indicators of pain after docking in the form of pain-related behaviour. Only two components, Mouth Features and Orbital Tightening, showed significant quantitative changes after docking. The direction of these changes agree with the description of these facial action units in the LGS. Restraint affected people's perceptions of pain as well as quantitative measures of LGS components. Freely moving lambs were scored lower using the LGS over both periods and had a significantly smaller eye aperture and smaller nose and ear angles than when they were held. Agreement among observers for LGS scores were fair overall (Experiment I: W=0.60; Experiment II: W=0.66). This preliminary study demonstrates changes in lamb facial expression associated with pain. The results of these experiments should be interpreted with caution due to low lamb numbers. Copyright © 2016 Elsevier B.V. All rights reserved.

ESC-Track: A computer workflow for 4-D segmentation, tracking, lineage tracing and dynamic context analysis of ESCs.

PubMed

Fernández-de-Manúel, Laura; Díaz-Díaz, Covadonga; Jiménez-Carretero, Daniel; Torres, Miguel; Montoya, María C

2017-05-01

Embryonic stem cells (ESCs) can be established as permanent cell lines, and their potential to differentiate into adult tissues has led to widespread use for studying the mechanisms and dynamics of stem cell differentiation and exploring strategies for tissue repair. Imaging live ESCs during development is now feasible due to advances in optical imaging and engineering of genetically encoded fluorescent reporters; however, a major limitation is the low spatio-temporal resolution of long-term 3-D imaging required for generational and neighboring reconstructions. Here, we present the ESC-Track (ESC-T) workflow, which includes an automated cell and nuclear segmentation and tracking tool for 4-D (3-D + time) confocal image data sets as well as a manual editing tool for visual inspection and error correction. ESC-T automatically identifies cell divisions and membrane contacts for lineage tree and neighborhood reconstruction and computes quantitative features from individual cell entities, enabling analysis of fluorescence signal dynamics and tracking of cell morphology and motion. We use ESC-T to examine Myc intensity fluctuations in the context of mouse ESC (mESC) lineage and neighborhood relationships. ESC-T is a powerful tool for evaluation of the genealogical and microenvironmental cues that maintain ESC fitness.

New three-dimensional visualization system based on angular image differentiation

NASA Astrophysics Data System (ADS)

Montes, Juan D.; Campoy, Pascual

1995-03-01

This paper presents a new auto-stereoscopic system capable of reproducing static or moving 3D images by projection with horizontal parallax or with horizontal and vertical parallaxes. The working principle is based on the angular differentiation of the images which are projected onto the back side of the new patented screen. The most important features of this new system are: (1) Images can be seen by naked eye, without the use of glasses or any other aid. (2) The 3D view angle is not restricted by the angle of the optics making up the screen. (3) Fine tuning is not necessary, independently of the parallax and of the size of the 3D view angle. (4) Coherent light is not necessary neither in capturing the image nor in its reproduction, but standard cameras and projectors. (5) Since the images are projected, the size and depth of the reproduced scene is unrestricted. (6) Manufacturing cost is not excessive, due to the use of optics of large focal length, to the lack of fine tuning and to the use of the same screen several reproduction systems. (7) This technology can be used for any projection system: slides, movies, TV cannons,... A first prototype of static images has been developed and tested with a 3D view angle of 90 degree(s) and a photographic resolution over a planar screen of 900 mm, of diagonal length. Present developments have success on a dramatic size reduction of the projecting system and of its cost. Simultaneous tasks have been carried out on the development of a prototype of 3D moving images.

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features.

PubMed

Ion-Mărgineanu, Adrian; Kocevar, Gabriel; Stamile, Claudio; Sima, Diana M; Durand-Dubief, Françoise; Van Huffel, Sabine; Sappey-Marinier, Dominique

2017-01-01

Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features. Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N -acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests. Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71-72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features. Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms.

Computer-assisted bladder cancer grading: α-shapes for color space decomposition

NASA Astrophysics Data System (ADS)

Niazi, M. K. K.; Parwani, Anil V.; Gurcan, Metin N.

2016-03-01

According to American Cancer Society, around 74,000 new cases of bladder cancer are expected during 2015 in the US. To facilitate the bladder cancer diagnosis, we present an automatic method to differentiate carcinoma in situ (CIS) from normal/reactive cases that will work on hematoxylin and eosin (H and E) stained images of bladder. The method automatically determines the color deconvolution matrix by utilizing the α-shapes of the color distribution in the RGB color space. Then, variations in the boundary of transitional epithelium are quantified, and sizes of nuclei in the transitional epithelium are measured. We also approximate the "nuclear to cytoplasmic ratio" by computing the ratio of the average shortest distance between transitional epithelium and nuclei to average nuclei size. Nuclei homogeneity is measured by computing the kurtosis of the nuclei size histogram. The results show that 30 out of 34 (88.2%) images were correctly classified by the proposed method, indicating that these novel features are viable markers to differentiate CIS from normal/reactive bladder.

[Shoulder instability].

PubMed

Sailer, J; Imhof, H

2004-06-01

Shoulder instability is a common clinical feature leading to recurrent pain and limited range of motion within the glenohumeral joint. Instability can be due a single traumatic event, general joint laxity or repeated episodes of microtrauma. Differentiation between traumatic and atraumatic forms of shoulder instability requires careful history and a systemic clinical examination. Shoulder laxity has to be differentiated from true instability followed by the clinical assessment of direction and degree of glenohumeral translation. Conventional radiography and CT are used for the diagnosis of bony lesions. MR imaging and MR arthrography help in the detection of soft tissue affection, especially of the glenoid labrum and the capsuloligamentous complex. The most common lesion involving the labrum is the anterior labral tear, associated with capsuloperiostal stripping (Bankart lesion). A number of variants of the Bankart lesion have been described, such as ALPSA, SLAP or HAGL lesions. The purpose of this review is to highlight different forms of shoulder instability and its associated radiological findings with a focus on MR imaging.

Imaging of non-neoplastic duodenal diseases. A pictorial review with emphasis on MDCT.

PubMed

Juanpere, Sergi; Valls, Laia; Serra, Isabel; Osorio, Margarita; Gelabert, Arantxa; Maroto, Albert; Pedraza, Salvador

2018-04-01

A wide spectrum of abnormalities can affect the duodenum, ranging from congenital anomalies to traumatic and inflammatory entities. The location of the duodenum and its close relationship with other organs make it easy to miss or misinterpret duodenal abnormalities on cross-sectional imaging. Endoscopy has largely supplanted fluoroscopy for the assessment of the duodenal lumen. Cross-sectional imaging modalities, especially multidetector computed tomography (MDCT) and magnetic resonance imaging (MRI), enable comprehensive assessment of the duodenum and surrounding viscera. Although overlapping imaging findings can make it difficult to differentiate between some lesions, characteristic features may suggest a specific diagnosis in some cases. Familiarity with pathologic conditions that can affect the duodenum and with the optimal MDCT and MRI techniques for studying them can help ensure diagnostic accuracy in duodenal diseases. The goal of this pictorial review is to illustrate the most common non-malignant duodenal processes. Special emphasis is placed on MDCT features and their endoscopic correlation as well as on avoiding the most common pitfalls in the evaluation of the duodenum. • Cross-sectional imaging modalities enable comprehensive assessment of duodenum diseases. • Causes of duodenal obstruction include intraluminal masses, inflammation and hematomas. • Distinguishing between tumour and groove pancreatitis can be challenging by cross-sectional imaging. • Infectious diseases of the duodenum are difficult to diagnose, as the findings are not specific. • The most common cause of nonvariceal upper gastrointestinal bleeding is peptic ulcer disease.

Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study.

PubMed

Li, Feng

2015-07-01

This review paper is based on our research experience in the past 30 years. The importance of radiologists' role is discussed in the development or evaluation of new medical images and of computer-aided detection (CAD) schemes in chest radiology. The four main topics include (1) introducing what diseases can be included in a research database for different imaging techniques or CAD systems and what imaging database can be built by radiologists, (2) understanding how radiologists' subjective judgment can be combined with technical objective features to improve CAD performance, (3) sharing our experience in the design of successful observer performance studies, and (4) finally, discussing whether the new images and CAD systems can improve radiologists' diagnostic ability in chest radiology. In conclusion, advanced imaging techniques and detection/classification of CAD systems have a potential clinical impact on improvement of radiologists' diagnostic ability, for both the detection and the differential diagnosis of various lung diseases, in chest radiology.

Image-guided decision support system for pulmonary nodule classification in 3D thoracic CT images

NASA Astrophysics Data System (ADS)

Kawata, Yoshiki; Niki, Noboru; Ohmatsu, Hironobu; Kusumoto, Masahiro; Kakinuma, Ryutaro; Mori, Kiyoshi; Yamada, Kozo; Nishiyama, Hiroyuki; Eguchi, Kenji; Kaneko, Masahiro; Moriyama, Noriyuki

2004-05-01

The purpose of this study is to develop an image-guided decision support system that assists decision-making in clinical differential diagnosis of pulmonary nodules. This approach retrieves and displays nodules that exhibit morphological and internal profiles consistent to the nodule in question. It uses a three-dimensional (3-D) CT image database of pulmonary nodules for which diagnosis is known. In order to build the system, there are following issues that should be solved: 1) to categorize the nodule database with respect to morphological and internal features, 2) to quickly search nodule images similar to an indeterminate nodule from a large database, and 3) to reveal malignancy likelihood computed by using similar nodule images. Especially, the first problem influences the design of other issues. The successful categorization of nodule pattern might lead physicians to find important cues that characterize benign and malignant nodules. This paper focuses on an approach to categorize the nodule database with respect to nodule shape and CT density patterns inside nodule.

Optical Coherence Tomography of the Tympanic Membrane and Middle Ear: A Review.

PubMed

Tan, Hsern Ern Ivan; Santa Maria, Peter Luke; Wijesinghe, Philip; Francis Kennedy, Brendan; Allardyce, Benjamin James; Eikelboom, Robert Henry; Atlas, Marcus David; Dilley, Rodney James

2018-05-01

Objective To evaluate the recent developments in optical coherence tomography (OCT) for tympanic membrane (TM) and middle ear (ME) imaging and to identify what further development is required for the technology to be integrated into common clinical use. Data Sources PubMed, Embase, Google Scholar, Scopus, and Web of Science. Review Methods A comprehensive literature search was performed for English language articles published from January 1966 to January 2018 with the keywords "tympanic membrane or middle ear,"optical coherence tomography," and "imaging." Conclusion Conventional imaging techniques cannot adequately resolve the microscale features of TM and ME, sometimes necessitating diagnostic exploratory surgery in challenging otologic pathology. As a high-resolution noninvasive imaging technique, OCT offers promise as a diagnostic aid for otologic conditions, such as otitis media, cholesteatoma, and conductive hearing loss. Using OCT vibrometry to image the nanoscale vibrations of the TM and ME as they conduct acoustic waves may detect the location of ossicular chain dysfunction and differentiate between stapes fixation and incus-stapes discontinuity. The capacity of OCT to image depth and thickness at high resolution allows 3-dimensional volumetric reconstruction of the ME and has potential use for reconstructive tympanoplasty planning and the follow-up of ossicular prostheses. Implications for Practice To achieve common clinical use beyond these initial discoveries, future in vivo imaging devices must feature low-cost probe or endoscopic designs and faster imaging speeds and demonstrate superior diagnostic utility to computed tomography and magnetic resonance imaging. While such technology has been available for OCT, its translation requires focused development through a close collaboration between engineers and clinicians.

T2*-based MR imaging (gradient echo or susceptibility-weighted imaging) in midline and off-midline intracranial germ cell tumors: a pilot study.

PubMed

Morana, Giovanni; Alves, Cesar Augusto; Tortora, Domenico; Finlay, Jonathan L; Severino, Mariasavina; Nozza, Paolo; Ravegnani, Marcello; Pavanello, Marco; Milanaccio, Claudia; Maghnie, Mohamad; Rossi, Andrea; Garrè, Maria Luisa

2018-01-01

The role of T2*-based MR imaging in intracranial germ cell tumors (GCTs) has not been fully elucidated. The aim of this study was to evaluate the susceptibility-weighted imaging (SWI) or T2* gradient echo (GRE) features of germinomas and non-germinomatous germ cell tumors (NGGCTs) in midline and off-midline locations. We retrospectively evaluated all consecutive pediatric patients referred to our institution between 2005 and 2016, for newly diagnosed, treatment-naïve intracranial GCT, who underwent MRI, including T2*-based MR imaging (T2* GRE sequences or SWI). Standard pre- and post-contrast T1- and T2-weighted imaging characteristics along with T2*-based MR imaging features of all lesions were evaluated. Diagnosis was performed in accordance with the SIOP CNS GCT protocol criteria. Twenty-four subjects met the inclusion criteria (17 males and 7 females). There were 17 patients with germinomas, including 5 basal ganglia primaries, and 7 patients with secreting NGGCT. All off-midline germinomas presented with SWI or GRE hypointensity; among midline GCT, all NGGCTs showed SWI or GRE hypointensity whereas all but one pure germinoma were isointense or hyperintense to normal parenchyma. A significant difference emerged on T2*-based MR imaging among midline germinomas, NGGCTs, and off-midline germinomas (p < 0.001). Assessment of the SWI or GRE characteristics of intracranial GCT may potentially assist in differentiating pure germinomas from NGGCT and in the characterization of basal ganglia involvement. T2*-based MR imaging is recommended in case of suspected intracranial GCT.

The metaplastic variant of Warthin tumor of the parotid gland: dynamic multislice computerized tomography and magnetic resonance imaging findings with histopathologic correlation in a case.

PubMed

Yerli, Hasan; Avci, Suat; Aydin, Erdinc; Arikan, Unser

2010-03-01

Metaplastic Warthin tumor is a rarely seen subtype of Warthin tumor. It can resemble squamous carcinomas histopathologically, because it contains atypical squamous cells on the necrotic surface. Making a diagnosis can become easier by knowing this entity of Warthin tumor well and by correlating the radiologic findings with pathology. In this case presentation, imaging features of a metaplastic Warthin tumor are presented together with its histopathologic findings. When a solid mass with peripheral enhancing cystic-necrotic component and well defined contour and capsule that shows early enhancement and washout is identified with imaging methods in parotid gland, metaplastic Warthin tumor should be indicated in the differential diagnosis before the histopathologic evaluation. Copyright 2010 Mosby, Inc. All rights reserved.

Thermographic analysis of waveguide-irradiated insect pupae

NASA Astrophysics Data System (ADS)

Olsen, Richard G.; Hammer, Wayne C.

1982-01-01

Pupae of the insect Tenebrio molitor L. were thermographically imaged during waveguide irradiation through longitudinal slots. T. molitor pupae have been subjects of microwave-induced teratology for a number of years, but until now the smallness of the insect has prevented detailed dosimetry. High-resolution thermographic imaging equipment was used to obtain the magnitude and spatial distribution of absorbed microwave energy at three frequencies, 1.3, 5.95, and 10 GHz. The detail of the thermal images obtained is sufficient to show the differential heating of structures as small as a single insect leg. Results show that the electrical properties of the head, thorax, and abdomen are sufficiently different to seriously impair the usefulness of any theoretical dosimetric model of homogeneous composition. Some general features of correlation with a slab model in waveguide are given.

Quantitative three-dimensional photoacoustic tomography of the finger joints: an in vivo study

NASA Astrophysics Data System (ADS)

Sun, Yao; Sobel, Eric; Jiang, Huabei

2009-11-01

We present for the first time in vivo full three-dimensional (3-D) photoacoustic tomography (PAT) of the distal interphalangeal joint in a human subject. Both absorbed energy density and absorption coefficient images of the joint are quantitatively obtained using our finite-element-based photoacoustic image reconstruction algorithm coupled with the photon diffusion equation. The results show that major anatomical features in the joint along with the side arteries can be imaged with a 1-MHz transducer in a spherical scanning geometry. In addition, the cartilages associated with the joint can be quantitatively differentiated from the phalanx. This in vivo study suggests that the 3-D PAT method described has the potential to be used for early diagnosis of joint diseases such as osteoarthritis and rheumatoid arthritis.

Regularized iterative integration combined with non-linear diffusion filtering for phase-contrast x-ray computed tomography.

PubMed

Burger, Karin; Koehler, Thomas; Chabior, Michael; Allner, Sebastian; Marschner, Mathias; Fehringer, Andreas; Willner, Marian; Pfeiffer, Franz; Noël, Peter

2014-12-29

Phase-contrast x-ray computed tomography has a high potential to become clinically implemented because of its complementarity to conventional absorption-contrast.In this study, we investigate noise-reducing but resolution-preserving analytical reconstruction methods to improve differential phase-contrast imaging. We apply the non-linear Perona-Malik filter on phase-contrast data prior or post filtered backprojected reconstruction. Secondly, the Hilbert kernel is replaced by regularized iterative integration followed by ramp filtered backprojection as used for absorption-contrast imaging. Combining the Perona-Malik filter with this integration algorithm allows to successfully reveal relevant sample features, quantitatively confirmed by significantly increased structural similarity indices and contrast-to-noise ratios. With this concept, phase-contrast imaging can be performed at considerably lower dose.

Deep Learning in Label-free Cell Classification

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

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individualmore » cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.« less

Pathologic and Radiologic Correlation of Adult Cystic Lung Disease: A Comprehensive Review

PubMed Central

Parimi, Vamsi; Taddonio, Michale; Kane, Joshua Robert; Yeldandi, Anjana

2017-01-01

The presence of pulmonary parenchymal cysts on computed tomography (CT) imaging presents a significant diagnostic challenge. The diverse range of possible etiologies can usually be differentiated based on the clinical setting and radiologic features. In fact, the advent of high-resolution CT has facilitated making a diagnosis solely on analysis of CT image patterns, thus averting the need for a biopsy. While it is possible to make a fairly specific diagnosis during early stages of disease evolution by its characteristic radiological presentation, distinct features may progress to temporally converge into relatively nonspecific radiologic presentations sometimes necessitating histological examination to make a diagnosis. The aim of this review study is to provide both the pathologist and the radiologist with an overview of the diseases most commonly associated with cystic lung lesions primarily in adults by illustration and description of pathologic and radiologic features of each entity. Brief descriptions and characteristic radiologic features of the various disease entities are included and illustrative examples are provided for the common majority of them. In this article, we also classify pulmonary cystic disease with an emphasis on the pathophysiology behind cyst formation in an attempt to elucidate the characteristics of similar cystic appearances seen in various disease entities. PMID:28270943

Deep Learning in Label-free Cell Classification

DOE PAGES

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; ...

2016-03-15

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individualmore » cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.« less

Unsupervised symmetrical trademark image retrieval in soccer telecast using wavelet energy and quadtree decomposition

NASA Astrophysics Data System (ADS)

Ong, Swee Khai; Lim, Wee Keong; Soo, Wooi King

2013-04-01

Trademark, a distinctive symbol, is used to distinguish products or services provided by a particular person, group or organization from other similar entries. As trademark represents the reputation and credit standing of the owner, it is important to differentiate one trademark from another. Many methods have been proposed to identify, classify and retrieve trademarks. However, most methods required features database and sample sets for training prior to recognition and retrieval process. In this paper, a new feature on wavelet coefficients, the localized wavelet energy, is introduced to extract features of trademarks. With this, unsupervised content-based symmetrical trademark image retrieval is proposed without the database and prior training set. The feature analysis is done by an integration of the proposed localized wavelet energy and quadtree decomposed regional symmetrical vector. The proposed framework eradicates the dependence on query database and human participation during the retrieval process. In this paper, trademarks for soccer games sponsors are the intended trademark category. Video frames from soccer telecast are extracted and processed for this study. Reasonably good localization and retrieval results on certain categories of trademarks are achieved. A distinctive symbol is used to distinguish products or services provided by a particular person, group or organization from other similar entries.

False positive or negative results of shear-wave elastography in differentiating benign from malignant breast masses: analysis of clinical and ultrasonographic characteristics.

PubMed

Kim, Mi Young; Choi, Nami; Yang, Jung-Hyun; Yoo, Young Bum; Park, Kyoung Sik

2015-10-01

Shear-wave elastography (SWE) has the potential to improve diagnostic performance of conventional ultrasound (US) in differentiating benign from malignant breast masses. To investigate false positive or negative results of SWE in differentiating benign from malignant breast masses and to analyze clinical and imaging characteristics of the masses with false SWE findings. From May to October 2013, 166 breast lesions of 164 consecutive women (mean age, 45.3 ± 10.1 years) who had been scheduled for biopsy were included. Conventional US and SWE were performed in all women before biopsy. Clinical, ultrasonographic morphologic features and SWE parameters (pattern classification and standard deviation [SD]) were recorded and compared with the histopathology results. Patient and lesion factors in the "true" and "false" groups were compared. Of the 166 masses, 118 (71.1%) were benign and 48 (28.9%) were malignant. False SWE features were more frequently observed in benign masses. False positive rates of benign masses and false negative rates of malignancy were 53% and 8.2%, respectively, using SWE pattern analysis and were 22.4% and 10.3%, respectively, using SD values. A lesion boundary of the masses on US (P = 0.039) and younger patient age (P = 0.047) were significantly associated with false SWE findings. These clinical and ultrasonographic features need to be carefully evaluated in performance and interpretation of SWE examinations. © The Foundation Acta Radiologica 2014.

Coupled Analysis of In Vitro and Histology Tissue Samples to Quantify Structure-Function Relationship

PubMed Central

Acar, Evrim; Plopper, George E.; Yener, Bülent

2012-01-01

The structure/function relationship is fundamental to our understanding of biological systems at all levels, and drives most, if not all, techniques for detecting, diagnosing, and treating disease. However, at the tissue level of biological complexity we encounter a gap in the structure/function relationship: having accumulated an extraordinary amount of detailed information about biological tissues at the cellular and subcellular level, we cannot assemble it in a way that explains the correspondingly complex biological functions these structures perform. To help close this information gap we define here several quantitative temperospatial features that link tissue structure to its corresponding biological function. Both histological images of human tissue samples and fluorescence images of three-dimensional cultures of human cells are used to compare the accuracy of in vitro culture models with their corresponding human tissues. To the best of our knowledge, there is no prior work on a quantitative comparison of histology and in vitro samples. Features are calculated from graph theoretical representations of tissue structures and the data are analyzed in the form of matrices and higher-order tensors using matrix and tensor factorization methods, with a goal of differentiating between cancerous and healthy states of brain, breast, and bone tissues. We also show that our techniques can differentiate between the structural organization of native tissues and their corresponding in vitro engineered cell culture models. PMID:22479315

Atypical progression of multiple myeloma with extensive extramedullary disease.

PubMed Central

Jowitt, S N; Jacobs, A; Batman, P A; Sapherson, D A

1994-01-01

Multiple myeloma is a neoplastic disorder caused by the proliferation of a transformed B lymphoid progenitor cell that gives rise to a clone of immunoglobulin-secreting cells. Other plasma cell tumours include solitary plasmacytoma of bone (SPB) and extramedullary plasmacytomas (EMP). Despite an apparent common origin there exist pathological and clinical differences between these neoplasms and the association between them is not completely understood. A case of IgG multiple myeloma that presented with typical clinical and laboratory features, including a bone marrow infiltrated by well differentiated plasma cells, is reported. The tumour had an unusual evolution, with the development of extensive extramedullary disease while maintaining mature histological features. Images PMID:8163701

Plantar fasciitis

PubMed Central

Tahririan, Mohammad Ali; Motififard, Mehdi; Tahmasebi, Mohammad Naghi; Siavashi, Babak

2012-01-01

Heel pain, mostly caused by plantar fasciitis (PF), is a common complaint of many patients who requiring professional orthopedic care and are mostly suffering from chronic pain beneath their heels. The present article reviews studies done by preeminent practitioners related to the anatomy of plantar fasciitis and their histo-pathological features, factors associated with PF, clinical features, imaging studies, differential diagnoses, and diverse treatment modalities for treatment of PF, with special emphasis on non-surgical treatment. Anti-inflammatory agents, plantar stretching, and orthosis proved to have highest priority; corticosteroid injection, night splints and extracorporeal shock wave therapy were of next priority, in patients with PF. In patients resistant to the mentioned treatments surgical intervention should be considered. PMID:23798950

Image processing and recognition for biological images.

PubMed

Uchida, Seiichi

2013-05-01

This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target. © 2013 The Author Development, Growth & Differentiation © 2013 Japanese Society of Developmental Biologists.

Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI

NASA Astrophysics Data System (ADS)

Niaf, Emilie; Rouvière, Olivier; Mège-Lechevallier, Florence; Bratan, Flavie; Lartizien, Carole

2012-06-01

This study evaluated a computer-assisted diagnosis (CADx) system for determining a likelihood measure of prostate cancer presence in the peripheral zone (PZ) based on multiparametric magnetic resonance (MR) imaging, including T2-weighted, diffusion-weighted and dynamic contrast-enhanced MRI at 1.5 T. Based on a feature set derived from grey-level images, including first-order statistics, Haralick features, gradient features, semi-quantitative and quantitative (pharmacokinetic modelling) dynamic parameters, four kinds of classifiers were trained and compared : nonlinear support vector machine (SVM), linear discriminant analysis, k-nearest neighbours and naïve Bayes classifiers. A set of feature selection methods based on t-test, mutual information and minimum-redundancy-maximum-relevancy criteria were also compared. The aim was to discriminate between the relevant features as well as to create an efficient classifier using these features. The diagnostic performances of these different CADx schemes were evaluated based on a receiver operating characteristic (ROC) curve analysis. The evaluation database consisted of 30 sets of multiparametric MR images acquired from radical prostatectomy patients. Using histologic sections as the gold standard, both cancer and nonmalignant (but suspicious) tissues were annotated in consensus on all MR images by two radiologists, a histopathologist and a researcher. Benign tissue regions of interest (ROIs) were also delineated in the remaining prostate PZ. This resulted in a series of 42 cancer ROIs, 49 benign but suspicious ROIs and 124 nonsuspicious benign ROIs. From the outputs of all evaluated feature selection methods on the test bench, a restrictive set of about 15 highly informative features coming from all MR sequences was discriminated, thus confirming the validity of the multiparametric approach. Quantitative evaluation of the diagnostic performance yielded a maximal area under the ROC curve (AUC) of 0.89 (0.81-0.94) for the discrimination of the malignant versus nonmalignant tissues and 0.82 (0.73-0.90) for the discrimination of the malignant versus suspicious tissues when combining the t-test feature selection approach with a SVM classifier. A preliminary comparison showed that the optimal CADx scheme mimicked, in terms of AUC, the human experts in differentiating malignant from suspicious tissues, thus demonstrating its potential for assisting cancer identification in the PZ.

Texture segmentation of non-cooperative spacecrafts images based on wavelet and fractal dimension

NASA Astrophysics Data System (ADS)

Wu, Kanzhi; Yue, Xiaokui

2011-06-01

With the increase of on-orbit manipulations and space conflictions, missions such as tracking and capturing the target spacecrafts are aroused. Unlike cooperative spacecrafts, fixing beacons or any other marks on the targets is impossible. Due to the unknown shape and geometry features of non-cooperative spacecraft, in order to localize the target and obtain the latitude, we need to segment the target image and recognize the target from the background. The data and errors during the following procedures such as feature extraction and matching can also be reduced. Multi-resolution analysis of wavelet theory reflects human beings' recognition towards images from low resolution to high resolution. In addition, spacecraft is the only man-made object in the image compared to the natural background and the differences will be certainly observed between the fractal dimensions of target and background. Combined wavelet transform and fractal dimension, in this paper, we proposed a new segmentation algorithm for the images which contains complicated background such as the universe and planet surfaces. At first, Daubechies wavelet basis is applied to decompose the image in both x axis and y axis, thus obtain four sub-images. Then, calculate the fractal dimensions in four sub-images using different methods; after analyzed the results of fractal dimensions in sub-images, we choose Differential Box Counting in low resolution image as the principle to segment the texture which has the greatest divergences between different sub-images. This paper also presents the results of experiments by using the algorithm above. It is demonstrated that an accurate texture segmentation result can be obtained using the proposed technique.

Morel-Lavallee Lesions-Review of Pathophysiology, Clinical Findings, Imaging Findings and Management.

PubMed

Diviti, Sreelatha; Gupta, Nishant; Hooda, Kusum; Sharma, Komal; Lo, Lawrence

2017-04-01

Morel-Lavallee lesion is a post-traumatic soft tissue degloving injury. This is commonly associated with sports injury caused by a shearing force resulting in separation of the hypodermis from the deeper fascia. Most common at the greater trochanter, these injuries also occur at flank, buttock, lumbar spine, scapula and the knee. Separation of the tissue planes result in a complex serosanguinous fluid collection with areas of fat within it. The imaging appearance is variable and non specific, potentially mimicking simple soft tissue haematoma, superficial bursitis or necrotic soft tissue neoplasms. If not treated in the acute or early sub acute settings, these collections are at risk for superinfection, overlying tissue necrosis and continued expansion. In this review article, we discuss the clinical presentation, pathophysiology, imaging features and differential diagnostic considerations of Morel-Lavallee lesions. Role of imaging in guiding prompt and appropriate treatment has also been discussed.

Video enhancement workbench: an operational real-time video image processing system

NASA Astrophysics Data System (ADS)

Yool, Stephen R.; Van Vactor, David L.; Smedley, Kirk G.

1993-01-01

Video image sequences can be exploited in real-time, giving analysts rapid access to information for military or criminal investigations. Video-rate dynamic range adjustment subdues fluctuations in image intensity, thereby assisting discrimination of small or low- contrast objects. Contrast-regulated unsharp masking enhances differentially shadowed or otherwise low-contrast image regions. Real-time removal of localized hotspots, when combined with automatic histogram equalization, may enhance resolution of objects directly adjacent. In video imagery corrupted by zero-mean noise, real-time frame averaging can assist resolution and location of small or low-contrast objects. To maximize analyst efficiency, lengthy video sequences can be screened automatically for low-frequency, high-magnitude events. Combined zoom, roam, and automatic dynamic range adjustment permit rapid analysis of facial features captured by video cameras recording crimes in progress. When trying to resolve small objects in murky seawater, stereo video places the moving imagery in an optimal setting for human interpretation.

Utility of fat-suppressed sequences in differentiation of aggressive vs typical asymptomatic haemangioma of the spine.

PubMed

Nabavizadeh, Seyed Ali; Mamourian, Alexander; Schmitt, James E; Cloran, Francis; Vossough, Arastoo; Pukenas, Bryan; Loevner, Laurie A; Mohan, Suyash

2016-01-01

While haemangiomas are common benign vascular lesions involving the spine, some behave in an aggressive fashion. We investigated the utility of fat-suppressed sequences to differentiate between benign and aggressive vertebral haemangiomas. Patients with the diagnosis of aggressive vertebral haemangioma and available short tau inversion-recovery or T2 fat saturation sequence were included in the study. 11 patients with typical asymptomatic vertebral body haemangiomas were selected as the control group. Region of interest signal intensity (SI) analysis of the entire haemangioma as well as the portion of each haemangioma with highest signal on fat-saturation sequences was performed and normalized to a reference normal vertebral body. A total of 8 patients with aggressive vertebral haemangioma and 11 patients with asymptomatic typical vertebral haemangioma were included. There was a significant difference between total normalized mean SI ratio (3.14 vs 1.48, p = 0.0002), total normalized maximum SI ratio (5.72 vs 2.55, p = 0.0003), brightest normalized mean SI ratio (4.28 vs 1.72, p < 0.0001) and brightest normalized maximum SI ratio (5.25 vs 2.45, p = 0.0003). Multiple measures were able to discriminate between groups with high sensitivity (>88%) and specificity (>82%). In addition to the conventional imaging features such as vertebral expansion and presence of extravertebral component, quantitative evaluation of fat-suppression sequences is also another imaging feature that can differentiate aggressive haemangioma and typical asymptomatic haemangioma. The use of quantitative fat-suppressed MRI in vertebral haemangiomas is demonstrated. Quantitative fat-suppressed MRI can have a role in confirming the diagnosis of aggressive haemangiomas. In addition, this application can be further investigated in future studies to predict aggressiveness of vertebral haemangiomas in early stages.

New Techniques for High-Contrast Imaging with ADI: The ACORNS-ADI SEEDS Data Reduction Pipeline

NASA Technical Reports Server (NTRS)

Brandt, Timothy D.; McElwain, Michael W.; Turner, Edwin L.; Abe, L.; Brandner, W.; Carson, J.; Egner, S.; Feldt, M.; Golota, T.; Grady, C. A.;

Thermal Analysis of Unusual Local-scale Features on the Surface of Vesta

NASA Technical Reports Server (NTRS)

Tosi, F.; Capria, M. T.; DeSanctis, M. C.; Capaccioni, F.; Palomba, E.; Zambon, F.; Ammannito, E.; Blewett, D. T.; Combe, J.-Ph.; Denevi, B. W.;

Large-field-of-view wide-spectrum artificial reflecting superposition compound eyes

NASA Astrophysics Data System (ADS)

Huang, Chi-Chieh

The study of the imaging principles of natural compound eyes has become an active area of research and has fueled the advancement of modern optics with many attractive design features beyond those available with conventional technologies. Most prominent among all compound eyes is the reflecting superposition compound eyes (RSCEs) found in some decapods. They are extraordinary imaging systems with numerous optical features such as minimum chromatic aberration, wide-angle field of view (FOV), high sensitivity to light and superb acuity to motion. Inspired by their remarkable visual system, we were able to implement the unique lens-free, reflection-based imaging mechanisms into a miniaturized, large-FOV optical imaging device operating at the wide visible spectrum to minimize chromatic aberration without any additional post-image processing. First, two micro-transfer printing methods, a multiple and a shear-assisted transfer printing technique, were studied and discussed to realize life-sized artificial RSCEs. The processes exploited the differential adhesive tendencies of the microstructures formed between a donor and a transfer substrate to accomplish an efficient release and transfer process. These techniques enabled conformal wrapping of three-dimensional (3-D) microstructures, initially fabricated in two-dimensional (2-D) layouts with standard fabrication technology onto a wide range of surfaces with complex and curvilinear shapes. Final part of this dissertation was focused on implementing the key operational features of the natural RSCEs into large-FOV, wide-spectrum artificial RSCEs as an optical imaging device suitable for the wide visible spectrum. Our devices can form real, clear images based on reflection rather than refraction, hence avoiding chromatic aberration due to dispersion by the optical materials. Compared to the performance of conventional refractive lenses of comparable size, our devices demonstrated minimum chromatic aberration, exceptional FOV up to 165o without distortion, modest spherical aberrations and comparable imaging quality without any post-image processing. Together with an augmenting cruciform pattern surrounding each focused image, our devices possessed enhanced, dynamic motion-tracking capability ideal for diverse applications in military, security, search and rescue, night navigation, medical imaging and astronomy. In the future, due to its reflection-based operating principles, it can be further extended into mid- and far-infrared for more demanding applications.

White matter abnormalities in major depressive disorder with melancholic and atypical features: A diffusion tensor imaging study.

PubMed

Ota, Miho; Noda, Takamasa; Sato, Noriko; Hattori, Kotaro; Hori, Hiroaki; Sasayama, Daimei; Teraishi, Toshiya; Nagashima, Anna; Obu, Satoko; Higuchi, Teruhiko; Kunugi, Hiroshi

2015-06-01

The DSM-IV recognizes some subtypes of major depressive disorder (MDD). It is known that the effectiveness of antidepressants differs among the MDD subtypes, and thus the differentiation of the subtypes is important. However, little is known as to structural brain changes in MDD with atypical features (aMDD) in comparison with MDD with melancholic features (mMDD), which prompted us to examine possible differences in white matter integrity assessed with diffusion tensor imaging (DTI) between these two subtypes. Subjects were 21 patients with mMDD, 24 with aMDD, and 37 age- and sex-matched healthy volunteers whose DTI data were obtained by 1.5 tesla magnetic resonance imaging. We compared fractional anisotropy and mean diffusivity value derived from DTI data on a voxel-by-voxel basis among the two diagnostic groups and healthy subjects. There were significant decreases of fractional anisotropy and increases of mean diffusivity in patients with MDD compared with healthy subjects in the corpus callosum, inferior fronto-occipital fasciculus, and left superior longitudinal fasciculus. However, we detected no significant difference in any brain region between mMDD and aMDD. Our results suggest that patients with MDD had reduced white matter integrity in some regions; however, there was no major difference between aMDD and mMDD. © 2014 The Authors. Psychiatry and Clinical Neurosciences © 2014 Japanese Society of Psychiatry and Neurology.

The significance of spectrophotometric image analysis for diagnosis of the melanocytic skin tumours in association with their thickness.

PubMed

Sakalauskienė, K; Valiukevičienė, S; Raišutis, R; Linkevičiūtė, G

2018-05-23

Cutaneous melanoma is a melanocytic skin tumour, which has very poor prognosis while it is highly resistant to treatment and tends to metastasize. Thickness of melanoma is one of the most important biomarker for stage of disease, prognosis and surgery planning. In this study, we hypothesized that the analysis of spectrophotometric (SIAscope) images can provide the information about skin tumour thickness. The intensity of blood displacement, "erythematous blush", collagen holes, intensity of collagen, dermal and epidermal melanin were estimated in SIAgraphs. Tumour thicknesses were evaluated non-invasively in ultrasound images before excision. The diagnosis and Breslow index of each tumour were evaluated during routine histological examination. The logistic regression analysis of two thicknesses groups of melanocytic tumours (≤1 mm, n = 72 and >1 mm, n = 30), using six SIAscopic features lead to achieve the areas under the ROC curves of 0.9 and 0.96 respectively. Overall the sensitivity and specificity of SIAscopy observed in this study is 81.4% and 86.4% respectively. The features of SIAgraphs individually are not enough specific for melanoma diagnosis with different thickness. Promising results were observed for differentiation of melanocytic skin tumour, using all 6 SIAscopic features, which correspond to the distribution, location and concentration of skin chromophores. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The MRI appearances of cancellous allograft bone chips after the excision of bone tumours.

PubMed

Kang, S; Han, I; Hong, S H; Cho, H S; Kim, W; Kim, H-S

2015-01-01

Cancellous allograft bone chips are commonly used in the reconstruction of defects in bone after removal of benign tumours. We investigated the MRI features of grafted bone chips and their change over time, and compared them with those with recurrent tumour. We retrospectively reviewed 66 post-operative MRIs from 34 patients who had undergone curettage and grafting with cancellous bone chips to fill the defect after excision of a tumour. All grafts showed consistent features at least six months after grafting: homogeneous intermediate or low signal intensities with or without scattered hyperintense foci (speckled hyperintensities) on T1 images; high signal intensities with scattered hypointense foci (speckled hypointensities) on T2 images, and peripheral rim enhancement with or without central heterogeneous enhancements on enhanced images. Incorporation of the graft occurred from the periphery to the centre, and was completed within three years. Recurrent lesions consistently showed the same signal intensities as those of pre-operative MRIs of the primary lesions. There were four misdiagnoses, three of which were chondroid tumours. We identified typical MRI features and clarified the incorporation process of grafted cancellous allograft bone chips. The most important characteristics of recurrent tumours were that they showed the same signal intensities as the primary tumours. It might sometimes be difficult to differentiate grafted cancellous allograft bone chips from a recurrent chondroid tumour. ©2015 The British Editorial Society of Bone & Joint Surgery.

Accurate label-free 3-part leukocyte recognition with single cell lens-free imaging flow cytometry.

PubMed

Li, Yuqian; Cornelis, Bruno; Dusa, Alexandra; Vanmeerbeeck, Geert; Vercruysse, Dries; Sohn, Erik; Blaszkiewicz, Kamil; Prodanov, Dimiter; Schelkens, Peter; Lagae, Liesbet

2018-05-01

Three-part white blood cell differentials which are key to routine blood workups are typically performed in centralized laboratories on conventional hematology analyzers operated by highly trained staff. With the trend of developing miniaturized blood analysis tool for point-of-need in order to accelerate turnaround times and move routine blood testing away from centralized facilities on the rise, our group has developed a highly miniaturized holographic imaging system for generating lens-free images of white blood cells in suspension. Analysis and classification of its output data, constitutes the final crucial step ensuring appropriate accuracy of the system. In this work, we implement reference holographic images of single white blood cells in suspension, in order to establish an accurate ground truth to increase classification accuracy. We also automate the entire workflow for analyzing the output and demonstrate clear improvement in the accuracy of the 3-part classification. High-dimensional optical and morphological features are extracted from reconstructed digital holograms of single cells using the ground-truth images and advanced machine learning algorithms are investigated and implemented to obtain 99% classification accuracy. Representative features of the three white blood cell subtypes are selected and give comparable results, with a focus on rapid cell recognition and decreased computational cost. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Diagnosis of breast cancer biopsies using quantitative phase imaging

NASA Astrophysics Data System (ADS)

Majeed, Hassaan; Kandel, Mikhail E.; Han, Kevin; Luo, Zelun; Macias, Virgilia; Tangella, Krishnarao; Balla, Andre; Popescu, Gabriel

2015-03-01

The standard practice in the histopathology of breast cancers is to examine a hematoxylin and eosin (H&E) stained tissue biopsy under a microscope. The pathologist looks at certain morphological features, visible under the stain, to diagnose whether a tumor is benign or malignant. This determination is made based on qualitative inspection making it subject to investigator bias. Furthermore, since this method requires a microscopic examination by the pathologist it suffers from low throughput. A quantitative, label-free and high throughput method for detection of these morphological features from images of tissue biopsies is, hence, highly desirable as it would assist the pathologist in making a quicker and more accurate diagnosis of cancers. We present here preliminary results showing the potential of using quantitative phase imaging for breast cancer screening and help with differential diagnosis. We generated optical path length maps of unstained breast tissue biopsies using Spatial Light Interference Microscopy (SLIM). As a first step towards diagnosis based on quantitative phase imaging, we carried out a qualitative evaluation of the imaging resolution and contrast of our label-free phase images. These images were shown to two pathologists who marked the tumors present in tissue as either benign or malignant. This diagnosis was then compared against the diagnosis of the two pathologists on H&E stained tissue images and the number of agreements were counted. In our experiment, the agreement between SLIM and H&E based diagnosis was measured to be 88%. Our preliminary results demonstrate the potential and promise of SLIM for a push in the future towards quantitative, label-free and high throughput diagnosis.

Intraoperative handheld probe for 3D imaging of pediatric benign vocal fold lesions using optical coherence tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Benboujja, Fouzi; Garcia, Jordan; Beaudette, Kathy; Strupler, Mathias; Hartnick, Christopher J.; Boudoux, Caroline

2016-02-01

Excessive and repetitive force applied on vocal fold tissue can induce benign vocal fold lesions. Children affected suffer from chronic hoarseness. In this instance, the vibratory ability of the folds, a complex layered microanatomy, becomes impaired. Histological findings have shown that lesions produce a remodeling of sup-epithelial vocal fold layers. However, our understanding of lesion features and development is still limited. Indeed, conventional imaging techniques do not allow a non-invasive assessment of sub-epithelial integrity of the vocal fold. Furthermore, it remains challenging to differentiate these sub-epithelial lesions (such as bilateral nodules, polyps and cysts) from a clinical perspective, as their outer surfaces are relatively similar. As treatment strategy differs for each lesion type, it is critical to efficiently differentiate sub-epithelial alterations involved in benign lesions. In this study, we developed an optical coherence tomography (OCT) based handheld probe suitable for pediatric laryngological imaging. The probe allows for rapid three-dimensional imaging of vocal fold lesions. The system is adapted to allow for high-resolution intra-operative imaging. We imaged 20 patients undergoing direct laryngoscopy during which we looked at different benign pediatric pathologies such as bilateral nodules, cysts and laryngeal papillomatosis and compared them to healthy tissue. We qualitatively and quantitatively characterized laryngeal pathologies and demonstrated the added advantage of using 3D OCT imaging for lesion discrimination and margin assessment. OCT evaluation of the integrity of the vocal cord could yield to a better pediatric management of laryngeal diseases.

The Precise and Efficient Identification of Medical Order Forms Using Shape Trees

NASA Astrophysics Data System (ADS)

Henker, Uwe; Petersohn, Uwe; Ultsch, Alfred

A powerful and flexible technique to identify, classify and process documents using images from a scanning process is presented. The types of documents can be described to the system as a set of differentiating features in a case base using shape trees. The features are filtered and abstracted from an extremely reduced scanner image of the document. Classification rules are stored with the cases to enable precise recognition and further mark reading and Optical Character Recognition (OCR) process. The method is implemented in a system which actually processes the majority of requests for medical lab procedures in Germany. A large practical experiment with data from practitioners was performed. An average of 97% of the forms were correctly identified; none were identified incorrectly. This meets the quality requirements for most medical applications. The modular description of the recognition process allows for a flexible adaptation of future changes to the form and content of the document’s structures.

Computerized assessment of body image in anorexia nervosa and bulimia nervosa: comparison with standardized body image assessment tool.

PubMed

Caspi, Asaf; Amiaz, Revital; Davidson, Noa; Czerniak, Efrat; Gur, Eitan; Kiryati, Nahum; Harari, Daniel; Furst, Miriam; Stein, Daniel

2017-02-01

Body image disturbances are a prominent feature of eating disorders (EDs). Our aim was to test and evaluate a computerized assessment of body image (CABI), to compare the body image disturbances in different ED types, and to assess the factors affecting body image. The body image of 22 individuals undergoing inpatient treatment with restricting anorexia nervosa (AN-R), 22 with binge/purge AN (AN-B/P), 20 with bulimia nervosa (BN), and 41 healthy controls was assessed using the Contour Drawing Rating Scale (CDRS), the CABI, which simulated the participants' self-image in different levels of weight changes, and the Eating Disorder Inventory-2-Body Dissatisfaction (EDI-2-BD) scale. Severity of depression and anxiety was also assessed. Significant differences were found among the three scales assessing body image, although most of their dimensions differentiated between patients with EDs and controls. Our findings support the use of the CABI in the comparison of body image disturbances in patients with EDs vs. Moreover, the use of different assessment tools allows for a better understanding of the differences in body image disturbances in different ED types.

X-ray phase contrast tomography by tracking near field speckle

PubMed Central

Wang, Hongchang; Berujon, Sebastien; Herzen, Julia; Atwood, Robert; Laundy, David; Hipp, Alexander; Sawhney, Kawal

2015-01-01

X-ray imaging techniques that capture variations in the x-ray phase can yield higher contrast images with lower x-ray dose than is possible with conventional absorption radiography. However, the extraction of phase information is often more difficult than the extraction of absorption information and requires a more sophisticated experimental arrangement. We here report a method for three-dimensional (3D) X-ray phase contrast computed tomography (CT) which gives quantitative volumetric information on the real part of the refractive index. The method is based on the recently developed X-ray speckle tracking technique in which the displacement of near field speckle is tracked using a digital image correlation algorithm. In addition to differential phase contrast projection images, the method allows the dark-field images to be simultaneously extracted. After reconstruction, compared to conventional absorption CT images, the 3D phase CT images show greatly enhanced contrast. This new imaging method has advantages compared to other X-ray imaging methods in simplicity of experimental arrangement, speed of measurement and relative insensitivity to beam movements. These features make the technique an attractive candidate for material imaging such as in-vivo imaging of biological systems containing soft tissue. PMID:25735237

Fpga based hardware synthesis for automatic segmentation of retinal blood vessels in diabetic retinopathy images.

PubMed

Sivakamasundari, J; Kavitha, G; Sujatha, C M; Ramakrishnan, S

2014-01-01

Diabetic Retinopathy (DR) is a disorder that affects the structure of retinal blood vessels due to long-standing diabetes mellitus. Real-Time mass screening system for DR is vital for timely diagnosis and periodic screening to prevent the patient from severe visual loss. Human retinal fundus images are widely used for an automated segmentation of blood vessel and diagnosis of various blood vessel disorders. In this work, an attempt has been made to perform hardware synthesis of Kirsch template based edge detection for segmentation of blood vessels. This method is implemented using LabVIEW software and is synthesized in field programmable gate array board to yield results in real-time application. The segmentation of blood vessels using Kirsch based edge detection is compared with other edge detection methods such as Sobel, Prewitt and Canny. The texture features such as energy, entropy, contrast, mean, homogeneity and structural feature namely ratio of vessel to vessel free area are obtained from the segmented images. The performance of segmentation is analysed in terms of sensitivity, specificity and accuracy. It is observed from the results that the Kirsch based edge detection technique segmented the edges of blood vessels better than other edge detection techniques. The ratio of vessel to vessel free area classified the normal and DR affected retinal images more significantly than other texture based features. FPGA based hardware synthesis of Kirsch edge detection method is able to differentiate normal and diseased images with high specificity (93%). This automated segmentation of retinal blood vessels system could be used in computer-assisted diagnosis for diabetic retinopathy screening in real-time application.

Orbital superconductivity, defects, and pinned nematic fluctuations in the doped iron chalcogenide FeSe 0.45 Te 0.55

DOE PAGES

Sarkar, Saheli; Van Dyke, John; Sprau, Peter O.; ...

2017-08-09

We demonstrate that the differential conductance, dI/dV , measured via spectroscopic imaging scanning tunneling microscopy in the doped iron chalcogenide FeSe0.45Te0.55, possesses a series of characteristic features that allow one to extract the orbital structure of the superconducting gaps. This yields nearly isotropic superconducting gaps on the two hole-like Fermi surfaces, and a strongly anisotropic gap on the electron-like Fermi surface. Moreover, we show that the pinning of nematic fluctuations by defects can give rise to a dumbbell-like spatial structure of the induced impurity bound states, and explains the related C 2-symmetry in the Fourier transformed differential conductance.

Orbital superconductivity, defects, and pinned nematic fluctuations in the doped iron chalcogenide FeSe 0.45 Te 0.55

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

Sarkar, Saheli; Van Dyke, John; Sprau, Peter O.

We demonstrate that the differential conductance, dI/dV , measured via spectroscopic imaging scanning tunneling microscopy in the doped iron chalcogenide FeSe0.45Te0.55, possesses a series of characteristic features that allow one to extract the orbital structure of the superconducting gaps. This yields nearly isotropic superconducting gaps on the two hole-like Fermi surfaces, and a strongly anisotropic gap on the electron-like Fermi surface. Moreover, we show that the pinning of nematic fluctuations by defects can give rise to a dumbbell-like spatial structure of the induced impurity bound states, and explains the related C 2-symmetry in the Fourier transformed differential conductance.

[Pay attention on optical coherence tomography evaluation for optic nerve diseases].

PubMed

Wang, M

2016-12-11

Optical coherence tomography(OCT) had become the most important imaging technique in ophthalmology. OCT is able to segment the retinal nerve fiber layer and retinal ganglion cell layer accurately. Quantitative analysis can be performed for both layers. OCT is very important to evaluate the neuron and axon loss in optic nerve diseases diagnosis. Meanwhile, OCT has great value for differentiating glaucoma and macular diseases from optic nerve diseases. This review presented OCT application in optic nerve diseases diagnosis, differentiation diagnosis, the key points in use and the features of en face OCT and OCT angiography. It gave us suggestions that it should be pay more attention to OCT examination in diagnosis and treatment of optic nerve diseases. (Chin J Ophthalmol, 2016, 52: 885 - 888) .

Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features.

PubMed

Mudali, D; Teune, L K; Renken, R J; Leenders, K L; Roerdink, J B T M

2015-01-01

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

Primary central nervous system vasculitis and its mimicking diseases - clinical features, outcome, comorbidities and diagnostic results - A case control study.

PubMed

Becker, J; Horn, P A; Keyvani, K; Metz, I; Wegner, C; Brück, W; Heinemann, F M; Schwitalla, J C; Berlit, P; Kraemer, M

2017-05-01

To compare clinical features and outcome, imaging characteristics, biopsy results and laboratory findings in a cohort of 69 patients with suspected or diagnosed primary central nervous system vasculitis (PCNSV) in adults; to identify risk factors and predictive features for PCNSV. We performed a case-control-study including 69 patients referred with suspected PCNSV from whom 25 were confirmed by predetermined diagnostic criteria based on biopsy (72%) or angiography (28%). Forty-four patients turned out to have 15 distinct other diagnoses. Clinical and diagnostic data were compared between PCNSV and Non-PCNSV cohorts. Clinical presentation was not able to discriminate between PCNSV and its differential diagnoses. However, a worse clinical outcome was associated with PCNSV (p=0.005). Biopsy (p=0.004), contrast enhancement (p=0.000) or tumour-like mass lesion (p=0.008) in magnetic resonance imaging (MRI), intrathecal IgG increase (p=0.020), normal Duplex findings of cerebral arteries (p=0.022) and conventional angiography (p 0.010) were able to distinguish between the two cohorts. In a cohort of 69 patients with suspected PCNSV, a large number (64%) was misdiagnosed and partly received treatment, since mimicking diseases are very difficult to discriminate. Clinical presentation at manifestation does not help to differentiate PCNSV from its mimicking diseases. MRI and cerebrospinal fluid analysis are unlikely to be normal in PCNSV, though unspecific if pathological. Cerebral angiography and biopsy must complement other diagnostics when establishing the diagnosis in order to avoid misdiagnosis and mistreatment. German clinical trials register: http://drks-neu.uniklinik-freiburg.de/drks_web/, Unique identifier: DRKS00005347. Copyright © 2017 Elsevier B.V. All rights reserved.

Heterotopic gastric mucosa in gallbladder—A rare differential diagnosis to gallbladder masses

PubMed Central

Beeskow, Anne Bettina; Meyer, Hans-Jonas; Schierle, Katrin; Surov, Alexey

2018-01-01

Abstract Background: Heterotopic gastric tissue can be found in the entire gastrointestinal tract. It is usually located in the upper intestine. Rarely, it can be found in the gallbladder. This study describes several clinically, imaging features as well as histopathology findings of heterotopic gastric tissue in gallbladder (HGM). Methods: The radiologic database of 1 tertiary university hospital was retrospectively screened for HGM. Additionally, a systemic review of the Medline database was conducted to identify previously published cases reports. In all cases clinical, imaging as well as histopathology features were retrieved from the papers. Results: In our databases, 1 patient with HGM was identified. Additionally, the systemic review yielded 32 suitable papers with 34 patients. Clinically, most of the patients suffered from abdominal discomfort. Most of the lesions were located in the lower gallbladder, especially (n = 14, 40%) in the gallbladder neck. On sonography, in 20.7% a broad-based mass was described. In 10.3% a sessile polyp was identified. In 5 cases, the mass was characterized as hyperechoic (55.5%), as isoechoic in 3 (33.3%) cases, and hypoechoic in 1 (11.1%). On computed tomography (CT), the lesions were most frequently hyperdense and all of them showed a slightly enhancement after application of contrast medium. On histopathology, most cases revealed heterotopic gastric mucosa of body-fundic type (60%) with chief and parietal cells, followed by pyloric type glands (20%). Every patient was treated with cholecystectomy and all had an uneventful recovery. Conclusion: HGM is a rare disorder with several differential diagnoses. Typically features were described to identify HGM in clinical routine and rule out malignant diseases like gallbladder carcinoma. PMID:29517663

Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm.

PubMed

Krishnan, M Muthu Rama; Venkatraghavan, Vikram; Acharya, U Rajendra; Pal, Mousumi; Paul, Ranjan Rashmi; Min, Lim Choo; Ray, Ajoy Kumar; Chatterjee, Jyotirmoy; Chakraborty, Chandan

2012-02-01

Oral cancer (OC) is the sixth most common cancer in the world. In India it is the most common malignant neoplasm. Histopathological images have widely been used in the differential diagnosis of normal, oral precancerous (oral sub-mucous fibrosis (OSF)) and cancer lesions. However, this technique is limited by subjective interpretations and less accurate diagnosis. The objective of this work is to improve the classification accuracy based on textural features in the development of a computer assisted screening of OSF. The approach introduced here is to grade the histopathological tissue sections into normal, OSF without Dysplasia (OSFWD) and OSF with Dysplasia (OSFD), which would help the oral onco-pathologists to screen the subjects rapidly. The biopsy sections are stained with H&E. The optical density of the pixels in the light microscopic images is recorded and represented as matrix quantized as integers from 0 to 255 for each fundamental color (Red, Green, Blue), resulting in a M×N×3 matrix of integers. Depending on either normal or OSF condition, the image has various granular structures which are self similar patterns at different scales termed "texture". We have extracted these textural changes using Higher Order Spectra (HOS), Local Binary Pattern (LBP), and Laws Texture Energy (LTE) from the histopathological images (normal, OSFWD and OSFD). These feature vectors were fed to five different classifiers: Decision Tree (DT), Sugeno Fuzzy, Gaussian Mixture Model (GMM), K-Nearest Neighbor (K-NN), Radial Basis Probabilistic Neural Network (RBPNN) to select the best classifier. Our results show that combination of texture and HOS features coupled with Fuzzy classifier resulted in 95.7% accuracy, sensitivity and specificity of 94.5% and 98.8% respectively. Finally, we have proposed a novel integrated index called Oral Malignancy Index (OMI) using the HOS, LBP, LTE features, to diagnose benign or malignant tissues using just one number. We hope that this OMI can help the clinicians in making a faster and more objective detection of benign/malignant oral lesions. Copyright © 2011 Elsevier Ltd. All rights reserved.

Investigation of relations between skin cancer lesions' images and their fluorescent spectra

NASA Astrophysics Data System (ADS)

Pavlova, P.; Borisova, E.; Avramov, L.; Petkova, El.; Troyanova, P.

2010-03-01

This investigation is based on images obtained from healthy tissue and skin cancer lesions and their fluorescent spectra of cutaneous lesions derived after optical stimulation. Our analyses show that the lesions’ spectra of are different of those, obtained from normal tissue and the differences depend on the type of cancer. We use a comparison between these “healthy” and “unhealthy” spectra to define forms of variations and corresponding diseases. However, the value of the emitted light varies not only between the patients, but also depending on the position of the tested area inside of one lesion. These variations could be result from two reasons: different degree of damaging and different thickness of the suspicious lesion area. Regarded to the visible image of the lesion, it could be connected with the chroma of colour of the tested area and the lesion homogeneity that corresponds to particular disease. For our investigation, images and spectra of three non-melanoma cutanous malignant tumors are investigated, namely—basal cell carcinoma, squamous cell carcinoma, and keratoacanthoma. The images were processed obtaining the chroma by elimination of the background—healthy tissue, and applying it as a basic signal for transformation from RGB to Lab colorimetric model. The chroma of the areas of emission is compared with the relative value of fluorescence spectra. Specific spectral features are used to develop hybrid diagnostic algorithm (including image and spectral features) for differentiation of these three kinds of malignant cutaneous pathologies.

The influence of stimulus format on drawing--a functional imaging study of decision making in portrait drawing.

PubMed

Miall, R C; Nam, Se-Ho; Tchalenko, J

2014-11-15

To copy a natural visual image as a line drawing, visual identification and extraction of features in the image must be guided by top-down decisions, and is usually influenced by prior knowledge. In parallel with other behavioral studies testing the relationship between eye and hand movements when drawing, we report here a functional brain imaging study in which we compared drawing of faces and abstract objects: the former can be strongly guided by prior knowledge, the latter less so. To manipulate the difficulty in extracting features to be drawn, each original image was presented in four formats including high contrast line drawings and silhouettes, and as high and low contrast photographic images. We confirmed the detailed eye-hand interaction measures reported in our other behavioral studies by using in-scanner eye-tracking and recording of pen movements with a touch screen. We also show that the brain activation pattern reflects the changes in presentation formats. In particular, by identifying the ventral and lateral occipital areas that were more highly activated during drawing of faces than abstract objects, we found a systematic increase in differential activation for the face-drawing condition, as the presentation format made the decisions more challenging. This study therefore supports theoretical models of how prior knowledge may influence perception in untrained participants, and lead to experience-driven perceptual modulation by trained artists. Copyright © 2014. Published by Elsevier Inc.

Lithologic mapping of the Mordor, NT, Australia ultramafic complex by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

USGS Publications Warehouse

Rowan, L.C.; Mars, J.C.; Simpson, C.J.

2005-01-01

Spectral measurements made in the Mordor Pound, NT, Australia study area using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), in the laboratory and in situ show dominantly Al-OH and ferric-iron VNIR-SWIR absorption features in felsic rock spectra and ferrous-iron and Fe,Mg-OH features in the mafic-ultramafic rock spectra. ASTER ratio images, matched-filter, and spectral-angle mapper processing (SAM) were evaluated for mapping the lithologies. Matched-filter processing in which VNIR + SWIR image spectra were used for reference resulted in 4 felsic classes and 4 mafic-ultramafic classes based on Al-OH or Fe,Mg-OH absorption features and, in some, subtle reflectance differences related to differential weathering and vegetation. These results were similar to those obtained by match-filter analysis of HyMap data from a previous study, but the units were more clearly demarcated in the HyMap image. ASTER TIR spectral emittance data and laboratory emissivity measurements document a wide wavelength range of Si-O spectral features, which reflect the lithological diversity of the Mordor ultramafic complex and adjacent rocks. SAM processing of the spectral emittance data distinguished 2 classes representing the mafic-ultramafic rocks and 4 classes comprising the quartzose to intermediate composition rocks. Utilization of the complementary attributes of the spectral reflectance and spectral emittance data resulted in discrimination of 4 mafic-ultramafic categories; 3 categories of alluvial-colluvial deposits; and a significantly more completely mapped quartzite unit than could be accomplished by using either data set alone. ?? 2005 Elsevier Inc. All rights reserved.

A new method for computer-assisted detection, definition and differentiation of the urinary calculi.

PubMed

Yildirim, Duzgun; Ozturk, Ovunc; Tutar, Onur; Nurili, Fuad; Bozkurt, Halil; Kayadibi, Huseyin; Karaarslan, Ercan; Bakan, Selim

2014-09-01

Urinary stones are common and can be diagnosed with computed tomography (CT) easily. In this study, we aimed to specify the opacity characteristics of various types of calcified foci that develop through the urinary system by using an image analysis program. With this method, we try to differentiate the calculi from the non-calculous opacities and also we aimed to present how to identify the characteristic features of renal and ureteral calcules. We obtained the CT studies of the subjects (n = 48, mean age = 41 years) by using a dual source CT imaging system. We grouped the calculi detected in the dual-energy CT sections as renal (n = 40) or ureteric (n = 45) based on their locations. Other radio-opaque structures that were identified outside but within close proximity of the urinary tract were recorded as calculi "mimickers". We used ImageJ program for morphological analysis. All the acquired data were analyzed statistically. According to thorough morphological parameters, there were statistically significant differences in the angle and Feret angle values between calculi and mimickers (p < 0.001). Multivariate logistical regression analysis showed that Minor Axis and Feret angle parameters can be used to distinguish between ureteric (p = 0.003) and kidney (p = 0.001) stones. Computer-based morphologic parameters can be used simply to differentiate between calcular and noncalcular densities on CT and also between renal and ureteric stones.

Multispectral image enhancement for H&E stained pathological tissue specimens

NASA Astrophysics Data System (ADS)

Bautista, Pinky A.; Abe, Tokiya; Yamaguchi, Masahiro; Ohyama, Nagaaki; Yagi, Yukako

2008-03-01

The presence of a liver disease such as cirrhosis can be determined by examining the proliferation of collagen fiber from a tissue slide stained with special stain such as the Masson's trichrome(MT) stain. Collagen fiber and smooth muscle, which are both stained the same in an H&E stained slide, are stained blue and pink respectively in an MT-stained slide. In this paper we show that with multispectral imaging the difference between collagen fiber and smooth muscle can be visualized even from an H&E stained image. In the method M KL bases are derived using the spectral data of those H&E stained tissue components which can be easily differentiated from each other, i.e. nucleus, cytoplasm, red blood cells, etc. and based on the spectral residual error of fiber weighting factors are determined to enhance spectral features at certain wavelengths. Results of our experiment demonstrate the capability of multispectral imaging and its advantage compared to the conventional RGB imaging systems to delineate tissue structures with subtle colorimetric difference.

Geological and hydrogeological investigation in West Malaysia

NASA Technical Reports Server (NTRS)

Ahmad, J. B. (Principal Investigator)

1976-01-01

The author has identified the following significant results. The broad synoptic view of the images allowed easy identification of circular features and major fault traces in low lying areas. Sedimentary units were delineated in accordance with the prevailing rock types and where applicable the folding characteristics. Igneous units could easily be differentiated by tone, degree of fracturing, texture, and drainage pattern. The larger fold structures, anticlinoriums and synclinoriums, of the younger sediments on the eastern edge of the central belt could also be easily delineated.

Development of buoyant currents in yield stress fluids

NASA Astrophysics Data System (ADS)

Rossi, P.; Karimfazli, I.

2017-11-01

Infinitesimal perturbations are known to decay in a motionless yield stress fluid. We present experimental evidence to reveal other mechanisms promoting free advection from a motionless background state. Development of natural convection in a cavity with differentially heated side-walls is investigated as a benchmark. Velocity and temperature fields are measured using particle image velocimetry/thermometry. We examine time evolution of the flow, compare experimental findings with theoretical predictions and comment on the striking features brought about by the yield stress.

Sonographic diagnosis of abscess following breast-conserving surgery with insertion of nonabsorbable mesh.

PubMed

Song, Sung Eun; Seo, Bo Kyoung; Son, Gil-Soo; Kim, Young-Sik

2014-09-01

Immediate mesh insertion has been recently used for breast reconstruction after breast-conserving surgery. We report a case of abscess formation following immediate nonabsorbable mesh insertion with breast-conserving surgery. In this article, we demonstrate multimodal breast imaging features and pathologic correlations of the case. In addition, we illustrate characteristic sonographic findings of nonabsorbable mesh fibers to differentiate them from a gossypiboma caused by a retained surgical sponge or tumor recurrence. © 2014 Wiley Periodicals, Inc.

What is the role of imaging in the clinical diagnosis of osteoarthritis and disease management?

PubMed

Wang, Xia; Oo, Win Min; Linklater, James M

2018-05-01

While OA is predominantly diagnosed on the basis of clinical criteria, imaging may aid with differential diagnosis in clinically suspected cases. While plain radiographs are traditionally the first choice of imaging modality, MRI and US also have a valuable role in assessing multiple pathologic features of OA, although each has particular advantages and disadvantages. Although modern imaging modalities provide the capability to detect a wide range of osseous and soft tissue (cartilage, menisci, ligaments, synovitis, effusion) OA-related structural damage, this extra information has not yet favourably influenced the clinical decision-making and management process. Imaging is recommended if there are unexpected rapid changes in clinical outcomes to determine whether it relates to disease severity or an additional diagnosis. On developing specific treatments, imaging serves as a sensitive tool to measure treatment response. This narrative review aims to describe the role of imaging modalities to aid in OA diagnosis, disease progression and management. It also provides insight into the use of these modalities in finding targeted treatment strategies in clinical research.

Eigenanatomy on Fractional Anisotropy Imaging Provides White Matter Anatomical Features Discriminating Between Alzheimer's Disease and Late Onset Bipolar Disorder.

PubMed

Besga, Ariadna; Chyzhyk, Darya; González-Ortega, Itxaso; Savio, Alexandre; Ayerdi, Borja; Echeveste, Jon; Graña, Manuel; González-Pinto, Ana

2016-01-01

Late Onset Bipolar Disorder (LOBD) is the arousal of Bipolar Disorder (BD) at old age (>60) without any previous history of disorders. LOBD is often difficult to distinguish from degenerative dementias, such as Alzheimer Disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence is increasing due to population aging. Biomarkers extracted from blood plasma are not discriminant because both pathologies share pathophysiological features related to neuroinflammation, therefore we look for anatomical features highly correlated with blood biomarkers that allow accurate diagnosis prediction. This may shed some light on the basic biological mechanisms leading to one or another disease. Moreover, accurate diagnosis is needed to select the best personalized treatment. We look for white matter features which are correlated with blood plasma biomarkers (inflammatory and neurotrophic) discriminating LOBD from AD. A sample of healthy controls (HC) (n=19), AD patients (n=35), and BD patients (n=24) has been recruited at the Alava University Hospital. Plasma biomarkers have been obtained at recruitment time. Diffusion weighted (DWI) magnetic resonance imaging (MRI) are obtained for each subject. DWI is preprocessed to obtain diffusion tensor imaging (DTI) data, which is reduced to fractional anisotropy (FA) data. In the selection phase, eigenanatomy finds FA eigenvolumes maximally correlated with plasma biomarkers by partial sparse canonical correlation analysis (PSCCAN). In the analysis phase, we take the eigenvolume projection coefficients as the classification features, carrying out cross-validation of support vector machine (SVM) to obtain discrimination power of each biomarker effects. The John Hopkins Universtiy white matter atlas is used to provide anatomical localizations of the detected feature clusters. Classification results show that one specific biomarker of oxidative stress (malondialdehyde MDA) gives the best classification performance ( accuracy 85%, F-score 86%, sensitivity, and specificity 87%, ) in the discrimination of AD and LOBD. Discriminating features appear to be localized in the posterior limb of the internal capsule and superior corona radiata. It is feasible to support contrast diagnosis among LOBD and AD by means of predictive classifiers based on eigenanatomy features computed from FA imaging correlated to plasma biomarkers. In addition, white matter eigenanatomy localizations offer some new avenues to assess the differential pathophysiology of LOBD and AD.

Extreme Asymmetry in the Polarized Disk of V1247 Orionis

NASA Technical Reports Server (NTRS)

Ohta, Yurina; Fukagawa, Misato; Sitko, Michael; Muto, Takayuki; Kraus, Stefan; Grady, Carol A.; Wisniewski, John A.; Swearingen, Jeremy R.; Shibai, Hiroshi; McElwain, Michael W.

2016-01-01

We present the first near-infrared scattered-light detection of the transitional disk around V1247 Ori, which was obtained using high-resolution polarimetric differential imaging observations with Subaru/HiCIAO. Our imaging in the H band reveals the disk morphology at separations of approx.0.14-0.86 (54-330 au) from the central star. The polarized intensity image shows a remarkable arc-like structure toward the southeast of the star, whereas the fainter northwest region does not exhibit any notable features. The shape of the arm is consistent with an arc of 0.28 +/- 0.09 in radius (108 au from the star), although the possibility of a spiral arm with a small pitch angle cannot be excluded. V1247 Ori features an exceptionally large azimuthal contrast in scattered, polarized light; the radial peak of the southeastern arc is about three times brighter than the northwestern disk measured at the same distance from the star. Combined with the previous indication of an inhomogeneous density distribution in the gap at 46 au, the notable asymmetry in the outer disk suggests the presence of unseen companions and/or planet-forming processes ongoing in the arc.

Automated numerical simulation of biological pattern formation based on visual feedback simulation framework

PubMed Central

Sun, Mingzhu; Xu, Hui; Zeng, Xingjuan; Zhao, Xin

2017-01-01

There are various fantastic biological phenomena in biological pattern formation. Mathematical modeling using reaction-diffusion partial differential equation systems is employed to study the mechanism of pattern formation. However, model parameter selection is both difficult and time consuming. In this paper, a visual feedback simulation framework is proposed to calculate the parameters of a mathematical model automatically based on the basic principle of feedback control. In the simulation framework, the simulation results are visualized, and the image features are extracted as the system feedback. Then, the unknown model parameters are obtained by comparing the image features of the simulation image and the target biological pattern. Considering two typical applications, the visual feedback simulation framework is applied to fulfill pattern formation simulations for vascular mesenchymal cells and lung development. In the simulation framework, the spot, stripe, labyrinthine patterns of vascular mesenchymal cells, the normal branching pattern and the branching pattern lacking side branching for lung branching are obtained in a finite number of iterations. The simulation results indicate that it is easy to achieve the simulation targets, especially when the simulation patterns are sensitive to the model parameters. Moreover, this simulation framework can expand to other types of biological pattern formation. PMID:28225811

Subretinal drusenoid deposits with increased autofluorescence in eyes with reticular pseudodrusen.

PubMed

Lee, Mee Yon; Ham, Don-Il

2014-01-01

To characterize a variant type of drusenoid deposit with different imaging features in comparison to reticular pseudodrusen. Retrospective observational consecutive case series. Eyes showing atypical drusenoid lesions were sorted out from 257 eyes of 133 patients previously diagnosed as reticular pseudodrusen. Eyes were evaluated using color fundus photography, confocal scanning laser ophthalmoscopy, and spectral domain optical coherence tomography. A variant type of drusenoid deposits showing different imaging features from reticular pseudodrusen was found in 17 eyes of 12 patients (6.6%). The mean age of patients was 62.7 ± 11.6 years, and all patients were women. These deposits were observed as yellowish white, round to oval lesions on color photographs, located under the sensory retina and above the retinal pigment epithelium on spectral domain optical coherence tomography similar to reticular pseudodrusen. However, they were present in a smaller number as discrete lesions and showed increased autofluorescence. None of them were accompanied by late age-related macular degeneration. Subretinal drusenoid deposits are not homogeneous and can be classified into two types according to the fundus autofluorescence. Multimodal imaging tests are needed for the differential diagnosis of subretinal drusenoid deposits.

Automated numerical simulation of biological pattern formation based on visual feedback simulation framework.

PubMed

Sun, Mingzhu; Xu, Hui; Zeng, Xingjuan; Zhao, Xin

2017-01-01

There are various fantastic biological phenomena in biological pattern formation. Mathematical modeling using reaction-diffusion partial differential equation systems is employed to study the mechanism of pattern formation. However, model parameter selection is both difficult and time consuming. In this paper, a visual feedback simulation framework is proposed to calculate the parameters of a mathematical model automatically based on the basic principle of feedback control. In the simulation framework, the simulation results are visualized, and the image features are extracted as the system feedback. Then, the unknown model parameters are obtained by comparing the image features of the simulation image and the target biological pattern. Considering two typical applications, the visual feedback simulation framework is applied to fulfill pattern formation simulations for vascular mesenchymal cells and lung development. In the simulation framework, the spot, stripe, labyrinthine patterns of vascular mesenchymal cells, the normal branching pattern and the branching pattern lacking side branching for lung branching are obtained in a finite number of iterations. The simulation results indicate that it is easy to achieve the simulation targets, especially when the simulation patterns are sensitive to the model parameters. Moreover, this simulation framework can expand to other types of biological pattern formation.

Physical activity advertisements that feature daily well-being improve autonomy and body image in overweight women but not men.

PubMed

Segar, Michelle L; Updegraff, John A; Zikmund-Fisher, Brian J; Richardson, Caroline R

2012-01-01

The reasons for exercising that are featured in health communications brand exercise and socialize individuals about why they should be physically active. Discovering which reasons for exercising are associated with high-quality motivation and behavioral regulation is essential to promoting physical activity and weight control that can be sustained over time. This study investigates whether framing physical activity in advertisements featuring distinct types of goals differentially influences body image and behavioral regulations based on self-determination theory among overweight and obese individuals. Using a three-arm randomized trial, overweight and obese women and men (aged 40-60 yr, n = 1690) read one of three ads framing physical activity as a way to achieve (1) better health, (2) weight loss, or (3) daily well-being. Framing effects were estimated in an ANOVA model with pairwise comparisons using the Bonferroni correction. This study showed that there are immediate framing effects on physical activity behavioral regulations and body image from reading a one-page advertisement about physical activity and that gender and BMI moderate these effects. Framing physical activity as a way to enhance daily well-being positively influenced participants' perceptions about the experience of being physically active and enhanced body image among overweight women, but not men. The experiment had less impact among the obese study participants compared to those who were overweight. These findings support a growing body of research suggesting that, compared to weight loss, framing physical activity for daily well-being is a better gain-frame message for overweight women in midlife.

Physical Activity Advertisements That Feature Daily Well-Being Improve Autonomy and Body Image in Overweight Women but Not Men

PubMed Central

Segar, Michelle L.; Updegraff, John A.; Zikmund-Fisher, Brian J.; Richardson, Caroline R.

2012-01-01

The reasons for exercising that are featured in health communications brand exercise and socialize individuals about why they should be physically active. Discovering which reasons for exercising are associated with high-quality motivation and behavioral regulation is essential to promoting physical activity and weight control that can be sustained over time. This study investigates whether framing physical activity in advertisements featuring distinct types of goals differentially influences body image and behavioral regulations based on self-determination theory among overweight and obese individuals. Using a three-arm randomized trial, overweight and obese women and men (aged 40–60 yr, n = 1690) read one of three ads framing physical activity as a way to achieve (1) better health, (2) weight loss, or (3) daily well-being. Framing effects were estimated in an ANOVA model with pairwise comparisons using the Bonferroni correction. This study showed that there are immediate framing effects on physical activity behavioral regulations and body image from reading a one-page advertisement about physical activity and that gender and BMI moderate these effects. Framing physical activity as a way to enhance daily well-being positively influenced participants' perceptions about the experience of being physically active and enhanced body image among overweight women, but not men. The experiment had less impact among the obese study participants compared to those who were overweight. These findings support a growing body of research suggesting that, compared to weight loss, framing physical activity for daily well-being is a better gain-frame message for overweight women in midlife. PMID:22701782

Terahertz transmission vs reflection imaging and model-based characterization for excised breast carcinomas.

PubMed

Bowman, Tyler; El-Shenawee, Magda; Campbell, Lucas K

2016-09-01

This work presents experimental and analytical comparison of terahertz transmission and reflection imaging modes for assessing breast carcinoma in excised paraffin-embedded human breast tissue. Modeling for both transmission and reflection imaging is developed. The refractive index and absorption coefficient of the tissue samples are obtained. The reflection measurements taken at the system's fixed oblique angle of 30° are shown to be a hybridization of TE and TM modes. The models are validated with transmission spectroscopy at fixed points on fresh bovine muscle and fat tissues. Images based on the calculated absorption coefficient and index of refraction of bovine tissue are successfully compared with the terahertz magnitude and phase measured in the reflection mode. The validated techniques are extended to 20 and 30 μm slices of fixed human lobular carcinoma and infiltrating ductal carcinoma mounted on polystyrene microscope slides in order to investigate the terahertz differentiation of the carcinoma with non-cancerous tissue. Both transmission and reflection imaging show clear differentiation in carcinoma versus healthy tissue. However, when using the reflection mode, in the calculation of the thin tissue properties, the absorption is shown to be sensitive to small phase variations that arise due to deviations in slide and tissue thickness and non-ideal tissue adhesion. On the other hand, the results show that the transmission mode is much less sensitive to these phase variations. The results also demonstrate that reflection imaging provides higher resolution and more clear margins between cancerous and fibroglandular regions, cancerous and fatty regions, and fibroglandular and fatty tissue regions. In addition, more features consistent with high power pathology images are exhibited in the reflection mode images.

Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions.

PubMed

Milenković, Jana; Hertl, Kristijana; Košir, Andrej; Zibert, Janez; Tasič, Jurij Franc

2013-06-01

The early detection of breast cancer is one of the most important predictors in determining the prognosis for women with malignant tumours. Dynamic contrast-enhanced magnetic-resonance imaging (DCE-MRI) is an important imaging modality for detecting and interpreting the different breast lesions from a time sequence of images and has proved to be a very sensitive modality for breast-cancer diagnosis. However, DCE-MRI exhibits only a moderate specificity, thus leading to a high rate of false positives, resulting in unnecessary biopsies that are stressful and physically painful for the patient and lead to an increase in the cost of treatment. There is a strong medical need for a DCE-MRI computer-aided diagnosis tool that would offer a reliable support to the physician's decision providing a high level of sensitivity and specificity. In our study we investigated the possibility of increasing differentiation between the malignant and the benign lesions with respect to the spatial variation of the temporal enhancements of three parametric maps, i.e., the initial enhancement (IE) map, the post-initial enhancement (PIE) map and the signal enhancement ratio (SER) map, by introducing additional methods along with the grey-level co-occurrence matrix, i.e., a second-order statistical method already applied for quantifying the spatiotemporal variations. We introduced the grey-level run-length matrix and the grey-level difference matrix, representing two additional, second-order statistical methods, and the circular Gabor as a frequency-domain-based method. Each of the additional methods is for the first time applied to the DCE-MRI data to differentiate between the malignant and the benign breast lesions. We applied the least-square minimum-distance classifier (LSMD), logistic regression and least-squares support vector machine (LS-SVM) classifiers on a total of 115 (78 malignant and 37 benign) breast DCE-MRI cases. The performances were evaluated using ten experiments of a ten-fold cross-validation. Our experimental analysis revealed the PIE map, together with the feature subset in which the discriminating ability of the co-occurrence features was increased by adding the newly introduced features, to be the most significant for differentiation between the malignant and the benign lesions. That diagnostic test - the aforementioned combination of parametric map and the feature subset achieved the sensitivity of 0.9193 which is statistically significantly higher compared to other diagnostic tests after ten-experiments of a ten-fold cross-validation and gave a statistically significantly higher specificity of 0.7819 for the fixed 95% sensitivity after the receiver operating characteristic (ROC) curve analysis. Combining the information from all the three parametric maps significantly increased the area under the ROC curve (AUC) of the aforementioned diagnostic test for the LSMD and logistic regression; however, not for the LS-SVM. The LSMD classifier yielded the highest area under the ROC curve when using the combined information, increasing the AUC from 0.9651 to 0.9755. Introducing new features to those of the grey-level co-occurrence matrix significantly increased the differentiation between the malignant and the benign breast lesions, thus resulting in a high sensitivity and improved specificity. Copyright © 2013 Elsevier B.V. All rights reserved.

Magnetic resonance imaging of placenta accreta

PubMed Central

Varghese, Binoj; Singh, Navdeep; George, Regi A.N; Gilvaz, Sareena

2013-01-01

Placenta accreta (PA) is a severe pregnancy complication which occurs when the chorionic villi (CV) invade the myometrium abnormally. Optimal management requires accurate prenatal diagnosis. Ultrasonography (USG) and magnetic resonance imaging (MRI) are the modalities for prenatal diagnosis of PA, although USG remains the primary investigation of choice. MRI is a complementary technique and reserved for further characterization when USG is inconclusive or incomplete. Breath-hold T2-weighted half-Fourier rapid acquisition with relaxation enhancement (RARE) and balanced steady-state free precession imaging in the three orthogonal planes is the key MRI technique. Markedly heterogeneous placenta, thick intraplacental dark bands on half-Fourier acquisition single-shot turbo spin-echo (HASTE), and disorganized abnormal intraplacental vascularity are the cardinal MRI features of PA. MRI is less reliable in differentiating between different degrees of placental invasion, especially between accreta vera and increta. PMID:24604945

Diagnostic Accuracy of Ultrasound, Contrast-enhanced CT, and Conventional MRI for Differentiating Leiomyoma From Leiomyosarcoma.

PubMed

Gaetke-Udager, Kara; McLean, Karen; Sciallis, Andrew P; Alves, Timothy; Maturen, Katherine E; Mervak, Benjamin M; Moore, Andreea G; Wasnik, Ashish P; Erba, Jake; Davenport, Matthew S

2016-10-01

This study aimed to determine whether uterine leiomyoma can be distinguished from uterine leiomyosarcoma on ultrasound (US), computed tomography (CT), and/or magnetic resonance imaging (MRI) without diffusion-weighted imaging. Institutional review board approval was obtained and informed consent was waived for this Health Insurance Portability and Accountability Act-compliant retrospective case-control diagnostic accuracy study. All subjects with resected uterine leiomyosarcoma diagnosed over a 17-year period (1998-2014) at a single institution for whom pre-resection US (n = 10), CT (n = 11), or MRI (n = 7) was available were matched by tumor size and imaging modality with 28 subjects with resected uterine leiomyoma. Six blinded radiologists (three attendings, three residents) assigned 5-point Likert scores for the following features: (1) margins, (2) necrosis, (3) hemorrhage, (4) vascularity, (5) calcifications, (6) heterogeneity, and (7) likelihood of malignancy (primary end point). Mean suspicion scores were calculated and receiver operating characteristic curves were generated. The ability of individual morphologic features to predict malignancy was assessed with logistic regression. Mean suspicion scores were 2.5 ± 1.2 (attendings) and 2.4 ± 1.3 (residents) for leiomyoma, and 2.7 ± 1.3 (attendings) and 2.7 ± 1.4 (residents) for leiomyosarcoma. The areas under the receiver operating characteristic curves (range: 0.330-0.685) were not significantly different from chance, either overall (P = .36-.88) or by any modality (P = .28-.96), for any reader. Reader experience had no effect on diagnostic accuracy. No morphologic parameter was significantly predictive of malignancy (P = .10-.97). Uterine leiomyoma cannot be differentiated accurately from leiomyosarcoma on US, CT, or MRI without diffusion-weighted imaging. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

[Diagnosis and treatment of diffuse tenosynovial giant cell tumor arising from temporomandibular joints].

PubMed

Meng, J H; Guo, Y X; Luo, H Y; Guo, C B; Ma, X C

2016-12-18

To retrospectively analyze the clinical features, treatment and prognosis to the diffuse tenosynovial giant cell tumor (D-TSGCT) arising from the temporomandibular joint (TMJ), and to give a reference for the early diagnosis and treatment of this disease. In this study, 15 patients finally diagnosed as D-TSGCT of TMJ histopathologically at the Peking University Hospital of Stomatology from October 2003 to August 2015 were selected and reviewed. Their clinical manifestations, imaging and histological features, diagnoses and differential diagnoses, treatments and follow-ups were summarized and discussed. D-TSGCT of TMJ showed obvious female predominance (12/15), the main symptoms included painful preauricular swelling or mass, limited mouth-opening and mandibular deviation with movement. D-TSGCT on computed tomography (CT) scan often showed ill-defined soft tissue masses around TMJ, enhancement after contrast administration, usually with widening of the joint spaces and with bone destruction of the condyle, the fossa and even the skull base. On magnetic resonance images (MRI), the majority of lesions on T1 weighted images and T2 weighted images both showed the characteristics of low signals (6/11). The lesions could extend beyond the joints (9/11) and into the infratemporal fossa (4/11) and the middle cranial fossa (4/11). Surgical resection was performed in 14 cases and biopsy in 1 case. Postoperative radiotherapy was performed in 3 cases. In follow-ups, 3 cases showed recurrence postoperatively. D-TSGCT arising from TMJ should be differentiated with TMJ disorders, other tumors and tumor-like lesions of TMJ and parotid neoplasms, etc. CT and MRI examinations have important values in the diagnosis and treatment design of D-TSGCT. Because of the local aggressive and extensive behavior, complete resection should be performed as soon as possible. Postoperative radiotherapy was helpful for the extensive lesions including destruction of skull base and may be a good supplementary therapy. Because of the possibility of recurrence and malignancy, long-term follow-up was suggested.

Differential Entropy Preserves Variational Information of Near-Infrared Spectroscopy Time Series Associated With Working Memory

PubMed Central

Keshmiri, Soheil; Sumioka, Hidenubo; Yamazaki, Ryuji; Ishiguro, Hiroshi

2018-01-01

Neuroscience research shows a growing interest in the application of Near-Infrared Spectroscopy (NIRS) in analysis and decoding of the brain activity of human subjects. Given the correlation that is observed between the Blood Oxygen Dependent Level (BOLD) responses that are exhibited by the time series data of functional Magnetic Resonance Imaging (fMRI) and the hemoglobin oxy/deoxy-genation that is captured by NIRS, linear models play a central role in these applications. This, in turn, results in adaptation of the feature extraction strategies that are well-suited for discretization of data that exhibit a high degree of linearity, namely, slope and the mean as well as their combination, to summarize the informational contents of the NIRS time series. In this article, we demonstrate that these features are inefficient in capturing the variational information of NIRS data, limiting the reliability and the adequacy of the conclusion on their results. Alternatively, we propose the linear estimate of differential entropy of these time series as a natural representation of such information. We provide evidence for our claim through comparative analysis of the application of these features on NIRS data pertinent to several working memory tasks as well as naturalistic conversational stimuli. PMID:29922144

Hyperspectral imaging for detection of cholesterol in human skin

NASA Astrophysics Data System (ADS)

Milanič, Matija; Bjorgan, Asgeir; Larsson, Marcus; Marraccini, Paolo; Strömberg, Tomas; Randeberg, Lise L.

2015-03-01

Hypercholesterolemia is characterized by high levels of cholesterol in the blood and is associated with an increased risk of atherosclerosis and coronary heart disease. Early detection of hypercholesterolemia is necessary to prevent onset and progress of cardiovascular disease. Optical imaging techniques might have a potential for early diagnosis and monitoring of hypercholesterolemia. In this study, hyperspectral imaging was investigated for this application. The main aim of the study was to identify spectral and spatial characteristics that can aid identification of hypercholesterolemia in facial skin. The first part of the study involved a numerical simulation of human skin affected by hypercholesterolemia. A literature survey was performed to identify characteristic morphological and physiological parameters. Realistic models were prepared and Monte Carlo simulations were performed to obtain hyperspectral images. Based on the simulations optimal wavelength regions for differentiation between normal and cholesterol rich skin were identified. Minimum Noise Fraction transformation (MNF) was used for analysis. In the second part of the study, the simulations were verified by a clinical study involving volunteers with elevated and normal levels of cholesterol. The faces of the volunteers were scanned by a hyperspectral camera covering the spectral range between 400 nm and 720 nm, and characteristic spectral features of the affected skin were identified. Processing of the images was done after conversion to reflectance and masking of the images. The identified features were compared to the known cholesterol levels of the subjects. The results of this study demonstrate that hyperspectral imaging of facial skin can be a promising, rapid modality for detection of hypercholesterolemia.

Is there a role for conventional MRI and MR diffusion-weighted imaging for distinction of skull base chordoma and chondrosarcoma?

PubMed

Müller, Uta; Kubik-Huch, Rahel A; Ares, Carmen; Hug, Eugen B; Löw, Roland; Valavanis, Antonios; Ahlhelm, Frank J

2016-02-01

Chordoma and chondrosarcoma are locally invasive skull base tumors with similar clinical symptoms and anatomic imaging features as reported in the literature. To determine differentiation of chordoma and chondrosarcoma of the skull base with conventional magnetic resonance imaging (cMRI) and diffusion-weighted MR imaging (DWI) in comparison to histopathological diagnosis. This retrospective study comprised 96 (chordoma, n = 64; chondrosarcoma, n = 32) patients with skull base tumors referred to the Paul Scherrer Institute (PSI) for proton therapy. cMRI signal intensities of all tumors were investigated. In addition, median apparent diffusion coefficient (ADC) values were measured in a subgroup of 19 patients (chordoma, n = 11; chondrosarcoma, n = 8). The majority 81.2% (26/32) of chondrosarcomas displayed an off-midline growth pattern, 18.8% (6/32) showed clival invasion, 18.8% (6/32) were located more centrally. Only 4.7% (3/64) of chordomas revealed a lateral clival origin. Using cMRI no significant differences in MR signal intensities were observed in contrast to significantly different ADC values (subgroup of 19/96 patients examined by DWI), with the highest mean value of 2017.2 × 10(-6 )mm(2)/s (SD, 139.9( )mm(2)/s) for chondrosarcoma and significantly lower value of 1263.5 × 10(-6 )mm(2)/s (SD, 100.2 × 10(-6 )mm(2)/s) for chordoma (P = 0.001/median test). An off-midline growth pattern can differentiate chondrosarcoma from chordoma on cMRI in a majority of patients. Additional DWI is a promising tool for the differentiation of these skull base tumors. © The Foundation Acta Radiologica 2015.

The 3D-based scaling index algorithm to optimize structure analysis of trabecular bone in postmenopausal women with and without osteoporotic spine fractures

NASA Astrophysics Data System (ADS)

Muller, Dirk; Monetti, Roberto A.; Bohm, Holger F.; Bauer, Jan; Rummeny, Ernst J.; Link, Thomas M.; Rath, Christoph W.

2004-05-01

The scaling index method (SIM) is a recently proposed non-linear technique to extract texture measures for the quantitative characterisation of the trabecular bone structure in high resolution magnetic resonance imaging (HR-MRI). The three-dimensional tomographic images are interpreted as a point distribution in a state space where each point (voxel) is defined by its x, y, z coordinates and the grey value. The SIM estimates local scaling properties to describe the nonlinear morphological features in this four-dimensional point distribution. Thus, it can be used for differentiating between cluster-, rod-, sheet-like and unstructured (background) image components, which makes it suitable for quantifying the microstructure of human cancellous bone. The SIM was applied to high resolution magnetic resonance images of the distal radius in patients with and without osteoporotic spine fractures in order to quantify the deterioration of bone structure. Using the receiver operator characteristic (ROC) analysis the diagnostic performance of this texture measure in differentiating patients with and without fractures was compared with bone mineral density (BMD). The SIM demonstrated the best area under the curve (AUC) value for discriminating the two groups. The reliability of our new texture measure and the validity of our results were assessed by applying bootstrapping resampling methods. The results of this study show that trabecular structure measures derived from HR-MRI of the radius in a clinical setting using a recently proposed algorithm based on a local 3D scaling index method can significantly improve the diagnostic performance in differentiating postmenopausal women with and without osteoporotic spine fractures.

DTI fiber tracking to differentiate demyelinating diseases from diffuse brain stem glioma.

PubMed

Giussani, Carlo; Poliakov, Andrew; Ferri, Raymond T; Plawner, Lauren L; Browd, Samuel R; Shaw, Dennis W W; Filardi, Tanya Z; Hoeppner, Corrine; Geyer, J Russell; Olson, James M; Douglas, James G; Villavicencio, Elisabeth H; Ellenbogen, Richard G; Ojemann, Jeffrey G

2010-08-01

Intrinsic diffuse brainstem tumors and demyelinating diseases primarily affecting the brainstem can share common clinical and radiological features, sometimes making the diagnosis difficult especially at the time of first clinical presentation. To explore the potential usefulness of new MRI sequences in particular diffusion tensor imaging fiber tracking in differentiating these two pathological entities, we review a series of brainstem tumors and demyelinating diseases treated at our institution. The clinical history including signs and symptoms and MRI findings of three consecutive demyelinating diseases involving the brainstem that presented with diagnostic uncertainty and three diffuse intrinsic brainstem tumors were reviewed, along with a child with a supratentorial tumor for comparison. Fiber tracking of the pyramidal tracts was performed for each patient using a DTI study at the time of presentation. Additionally Fractional Anisotropy values were calculated for each patient in the pons and the medulla oblongata. Routine MR imaging was unhelpful in differentiating between intrinsic tumor and demyelination. In contrast, retrospective DTI fiber tracking clearly differentiated the pathology showing deflection of the pyramidal tracts posteriorly and laterally in the case of intrinsic brainstem tumors and, in the case of demyelinating disease, poorly represented and truncated fibers. Regionalized FA values were variable and of themselves were not predictive either pathology. DTI fiber tracking of the pyramid tracts in patients with suspected intrinsic brainstem tumor or demyelinating disease presents two clearly different patterns that may help in differentiating between these two pathologies when conventional MRI and clinical data are inconclusive. Copyright 2010 Elsevier Inc. All rights reserved.

Contrast-enhanced ultrasound in diagnosis of gallbladder adenoma.

PubMed

Yuan, Hai-Xia; Cao, Jia-Ying; Kong, Wen-Tao; Xia, Han-Sheng; Wang, Xi; Wang, Wen-Ping

2015-04-01

Gallbladder adenoma is a pre-cancerous neoplasm and needs surgical resection. It is difficult to differentiate adenoma from other gallbladder polyps using imaging examinations. The study aimed to illustrate characteristics of contrast-enhanced ultrasound (CEUS) and its diagnostic value in gallbladder adenoma. Thirty-seven patients with 39 gallbladder adenomatoid lesions (maximal diameter ≥10 mm and without metastasis) were enrolled in this study. Lesion appearances in conventional ultrasound and CEUS were documented. The imaging features were compared individually among gallbladder cholesterol polyp, gallbladder adenoma and malignant lesion. Adenoma lesions showed iso-echogenicity in ultrasound, and an eccentric enhancement pattern, "fast-in and synchronous-out" contrast enhancement pattern and homogeneous at peak-time enhancement in CEUS. The homogenicity at peak-time enhancement showed the highest diagnostic ability in differentiating gallbladder adenoma from cholesterol polyps. The sensitivity, specificity, positive predictive value, negative predictive value, accuracy and Youden index were 100%, 90.9%, 92.9%, 100%, 95.8% and 0.91, respectively. The characteristic of continuous gallbladder wall shown by CEUS had the highest diagnostic ability in differentiating adenoma from malignant lesion (100%, 86.7%, 86.7%, 100%, 92.9% and 0.87, respectively). The characteristic of the eccentric enhancement pattern had the highest diagnostic ability in differentiating adenoma from cholesterol polyp and malignant lesion, with corresponding indices of 69.2%, 88.5%, 75.0%, 85.2%, 82.1% and 0.58, respectively. CEUS is valuable in differentiating gallbladder adenoma from other gallbladder polyps (≥10 mm in diameter). Homogeneous echogenicity on peak-time enhancement, a continuous gallbladder wall, and the eccentric enhancement pattern are important indicators of gallbladder adenoma on CEUS.

Stimulus Set Meaningfulness and Neurophysiological Differentiation: A Functional Magnetic Resonance Imaging Study

PubMed Central

Boly, Melanie; Sasai, Shuntaro; Gosseries, Olivia; Oizumi, Masafumi; Casali, Adenauer; Massimini, Marcello; Tononi, Giulio

2015-01-01

A meaningful set of stimuli, such as a sequence of frames from a movie, triggers a set of different experiences. By contrast, a meaningless set of stimuli, such as a sequence of ‘TV noise’ frames, triggers always the same experience—of seeing ‘TV noise’—even though the stimuli themselves are as different from each other as the movie frames. We reasoned that the differentiation of cortical responses underlying the subject’s experiences, as measured by Lempel-Ziv complexity (incompressibility) of functional MRI images, should reflect the overall meaningfulness of a set of stimuli for the subject, rather than differences among the stimuli. We tested this hypothesis by quantifying the differentiation of brain activity patterns in response to a movie sequence, to the same movie scrambled in time, and to ‘TV noise’, where the pixels from each movie frame were scrambled in space. While overall cortical activation was strong and widespread in all conditions, the differentiation (Lempel-Ziv complexity) of brain activation patterns was correlated with the meaningfulness of the stimulus set, being highest in the movie condition, intermediate in the scrambled movie condition, and minimal for ‘TV noise’. Stimulus set meaningfulness was also associated with higher information integration among cortical regions. These results suggest that the differentiation of neural responses can be used to assess the meaningfulness of a given set of stimuli for a given subject, without the need to identify the features and categories that are relevant to the subject, nor the precise location of selective neural responses. PMID:25970444

Shadows cast on the transition disk of HD 135344B. Multiwavelength VLT/SPHERE polarimetric differential imaging

NASA Astrophysics Data System (ADS)

Stolker, T.; Dominik, C.; Avenhaus, H.; Min, M.; de Boer, J.; Ginski, C.; Schmid, H. M.; Juhasz, A.; Bazzon, A.; Waters, L. B. F. M.; Garufi, A.; Augereau, J.-C.; Benisty, M.; Boccaletti, A.; Henning, Th.; Langlois, M.; Maire, A.-L.; Ménard, F.; Meyer, M. R.; Pinte, C.; Quanz, S. P.; Thalmann, C.; Beuzit, J.-L.; Carbillet, M.; Costille, A.; Dohlen, K.; Feldt, M.; Gisler, D.; Mouillet, D.; Pavlov, A.; Perret, D.; Petit, C.; Pragt, J.; Rochat, S.; Roelfsema, R.; Salasnich, B.; Soenke, C.; Wildi, F.

2016-11-01

Context. The protoplanetary disk around the F-type star HD 135344B (SAO 206462) is in a transition stage and shows many intriguing structures both in scattered light and thermal (sub-)millimeter emission which are possibly related to planet formation processes. Aims: We aim to study the morphology and surface brightness of the disk in scattered light to gain insight into the innermost disk regions, the formation of protoplanets, planet-disk interactions traced in the surface and midplane layers, and the dust grain properties of the disk surface. Methods: We have carried out high-contrast polarimetric differential imaging (PDI) observations with VLT/SPHERE and obtained polarized scattered light images with ZIMPOL in the R and I-bands and with IRDIS in the Y and J-bands. The scattered light images and surface brightness profiles are used to study in detail structures in the disk surface and brightness variations. We have constructed a 3D radiative transfer model to support the interpretation of several detected shadow features. Results: The scattered light images reveal with unprecedented angular resolution and sensitivity the spiral arms as well as the 25 au cavity of the disk. Multiple shadow features are discovered on the outer disk with one shadow only being present during the second observation epoch. A positive surface brightness gradient is observed in the stellar irradiation corrected (r2-scaled) images in southwest direction possibly due to an azimuthally asymmetric perturbation of the temperature and/or surface density by the passing spiral arms. The disk integrated polarized flux, normalized to the stellar flux, shows a positive trend towards longer wavelengths which we attribute to large (2πa ≳ λ) aggregate dust grains in the disk surface. Part of the non-azimuthal polarization signal in the Uφ image of the J-band observation can be attributed to multiple scattering in the disk. Conclusions: The detected shadow features and their possible variability have the potential to provide insight into the structure of and processes occurring in the innermost disk regions. Possible explanations for the presence of the shadows include a 22° misaligned inner disk, a warped disk region that connects the inner disk with the outer disk, and variable or transient phenomena such as a perturbation of the inner disk or an asymmetric accretion flow. The spiral arms are best explained by one or multiple protoplanets in the exterior of the disk although no gap is detected beyond the spiral arms up to 1.''0. Based on observations collected at the European Southern Observatory, Chile, ESO No. 095.C-0273(A) and 095.C-0273(D).

Benign hepatocellular nodules of healthy liver: focal nodular hyperplasia and hepatocellular adenoma

PubMed Central

Roncalli, Massimo; Sciarra, Amedeo; Tommaso, Luca Di

2016-01-01

Owing to the progress of imaging techniques, benign hepatocellular nodules are increasingly discovered in the clinical practice. This group of lesions mostly arises in the context of a putatively normal healthy liver and includes either pseudotumoral and tumoral nodules. Focal nodular hyperplasia and hepatocellular adenoma are prototypical examples of these two categories of nodules. In this review we aim to report the main pathological criteria of differential diagnosis between focal nodular hyperplasia and hepatocellular adenoma, which mainly rests upon morphological and phenotypical features. We also emphasize that for a correct diagnosis the clinical context such as sex, age, assumption of oral contraceptives, associated metabolic or vascular disturbances is of paramount importance. While focal nodular hyperplasia is a single entity epidemiologically more frequent than adenoma, the latter is representative of a more heterogeneous group which has been recently and extensively characterized from a clinical, morphological, phenotypical and molecular profile. The use of the liver biopsy in addition to imaging and the clinical context are important diagnostic tools of these lesions. In this review we will survey their systematic pathobiology and propose a diagnostic algorithm helpful to increase the diagnostic accuracy of not dedicated liver pathologists. The differential diagnosis between so-called typical and atypical adenoma and well differentiated hepatocellular carcinoma will also be discussed. PMID:27189732

Intraductal Tubulopapillary Neoplasm of the Pancreas: An Update From a Pathologist's Perspective.

PubMed

Rooney, Sarah L; Shi, Jiaqi

2016-10-01

-Intraductal tubulopapillary neoplasm (ITPN) is a rare intraductal epithelial neoplasm of the pancreas recently recognized as a distinct entity by the World Health Organization classification in 2010. It is defined as an intraductal, grossly visible, tubule-forming epithelial neoplasm with high-grade dysplasia and ductal differentiation without overt production of mucin. The diagnosis can be challenging owing to morphologic overlap with other intraductal lesions and its rarity. While recent advances in molecular genetic studies of ITPN have provided new tools to facilitate clinical diagnosis, the limited number of cases has yielded limited follow-up data to guide management. -To provide a clinical, pathologic, and molecular update on ITPN with respect to clinical presentation, imaging findings, histopathologic features, differential diagnosis, biological behavior, molecular characteristics, and treatment options. -Analysis of the pertinent literature (PubMed) and authors' research and clinical practice experience based on institutional and consultation materials. -Clinical presentation, imaging findings, histopathology, immunohistochemistry studies, molecular characteristics, prognosis, and treatment options of ITPN are reviewed. Important differential diagnoses with other intraductal neoplasms of the pancreas-especially intraductal papillary mucinous neoplasm-using histopathologic, molecular, and immunohistochemical studies, are discussed. Despite the recent progress, more studies are necessary to assess the biology and genetics of ITPN for a better understanding of the prognostic factors and treatment options.

Morphological Integration of Soft-Tissue Facial Morphology in Down Syndrome and Siblings

PubMed Central

Starbuck, John; Reeves, Roger H.; Richtsmeier, Joan

2011-01-01

Down syndrome (DS), resulting from trisomy of chromosome 21, is the most common live-born human aneuploidy. The phenotypic expression of trisomy 21 produces variable, though characteristic, facial morphology. Although certain facial features have been documented quantitatively and qualitatively as characteristic of DS (e.g., epicanthic folds, macroglossia, and hypertelorism), all of these traits occur in other craniofacial conditions with an underlying genetic cause. We hypothesize that the typical DS face is integrated differently than the face of non-DS siblings, and that the pattern of morphological integration unique to individuals with DS will yield information about underlying developmental associations between facial regions. We statistically compared morphological integration patterns of immature DS faces (N = 53) with those of non-DS siblings (N = 54), aged 6–12 years using 31 distances estimated from 3D coordinate data representing 17 anthropometric landmarks recorded on 3D digital photographic images. Facial features are affected differentially in DS, as evidenced by statistically significant differences in integration both within and between facial regions. Our results suggest a differential affect of trisomy on facial prominences during craniofacial development. PMID:21996933

Morphological integration of soft-tissue facial morphology in Down Syndrome and siblings.

PubMed

Starbuck, John; Reeves, Roger H; Richtsmeier, Joan

2011-12-01

Down syndrome (DS), resulting from trisomy of chromosome 21, is the most common live-born human aneuploidy. The phenotypic expression of trisomy 21 produces variable, though characteristic, facial morphology. Although certain facial features have been documented quantitatively and qualitatively as characteristic of DS (e.g., epicanthic folds, macroglossia, and hypertelorism), all of these traits occur in other craniofacial conditions with an underlying genetic cause. We hypothesize that the typical DS face is integrated differently than the face of non-DS siblings, and that the pattern of morphological integration unique to individuals with DS will yield information about underlying developmental associations between facial regions. We statistically compared morphological integration patterns of immature DS faces (N = 53) with those of non-DS siblings (N = 54), aged 6-12 years using 31 distances estimated from 3D coordinate data representing 17 anthropometric landmarks recorded on 3D digital photographic images. Facial features are affected differentially in DS, as evidenced by statistically significant differences in integration both within and between facial regions. Our results suggest a differential affect of trisomy on facial prominences during craniofacial development. 2011 Wiley Periodicals, Inc.

Computer-aided diagnosis of liver tumors on computed tomography images.

PubMed

Chang, Chin-Chen; Chen, Hong-Hao; Chang, Yeun-Chung; Yang, Ming-Yang; Lo, Chung-Ming; Ko, Wei-Chun; Lee, Yee-Fan; Liu, Kao-Lang; Chang, Ruey-Feng

2017-07-01

Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images. A total of 71 histologically-proven liver tumors including 49 benign and 22 malignant lesions were evaluated with the proposed CAD system to evaluate its performance. Tumors were identified by the user and then segmented using a region growing algorithm. After tumor segmentation, three kinds of features were obtained for each tumor, including texture, shape, and kinetic curve. The texture was quantified using 3 dimensional (3-D) texture data of the tumor based on the grey level co-occurrence matrix (GLCM). Compactness, margin, and an elliptic model were used to describe the 3-D shape of the tumor. The kinetic curve was established from each phase of tumor and represented as variations in density between each phase. Backward elimination was used to select the best combination of features, and binary logistic regression analysis was used to classify the tumors with leave-one-out cross validation. The accuracy and sensitivity for the texture were 71.82% and 68.18%, respectively, which were better than for the shape and kinetic curve under closed specificity. Combining all of the features achieved the highest accuracy (58/71, 81.69%), sensitivity (18/22, 81.82%), and specificity (40/49, 81.63%). The Az value of combining all features was 0.8713. Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using our proposed CAD system. Copyright © 2017 Elsevier B.V. All rights reserved.

Contrasting landform perception with varied radar illumination geometries and at simulated resolutions of Venera and Magellan

NASA Technical Reports Server (NTRS)

Ford, J. P.; Arvidson, R. E.

1989-01-01

The high sensitivity of imaging radars to slope at moderate to low incidence angles enhances the perception of linear topography on images. It reveals broad spatial patterns that are essential to landform mapping and interpretation. As radar responses are strongly directional, the ability to discriminate linear features on images varies with their orientation. Landforms that appear prominent on images where they are transverse to the illumination may be obscure to indistinguishable on images where they are parallel to it. Landform detection is also influenced by the spatial resolution in radar images. Seasat radar images of the Gran Desierto Dunes complex, Sonora, Mexico; the Appalachian Valley and Ridge Province; and accreted terranes in eastern interior Alaska were processed to simulate both Venera 15 and 16 images (1000 to 3000 km resolution) and image data expected from the Magellan mission (120 to 300 m resolution. The Gran Desierto Dunes are not discernable in the Venera simulation, whereas the higher resolution Magellan simulation shows dominant dune patterns produced from differential erosion of the rocks. The Magellan simulation also shows that fluvial processes have dominated erosion and exposure of the folds.

Clinical cancer diagnosis using optical fiber-delivered coherent anti-stokes ramon scattering microscopy

NASA Astrophysics Data System (ADS)

Gao, Liang

This thesis describes the development of a combined label-free imaging and analytical strategy for intraoperative characterization of cancer lesions using the coherent anti-Stokes Raman scattering imaging (CARS) technique. A cell morphology-based analytical platform is developed to characterize CARS images and, hence, provide diagnostic information using disease-related pathology features. This strategy is validated for three different applications, including margin detection for radical prostatectomy, differential diagnosis of lung cancer, as well as detection and differentiation of breast cancer subtypes for in situ analysis of margin status during lumpectomy. As the major contribution of this thesis, the developed analytical strategy shows high accuracy and specificity for all three diseases and thus has introduced the CARS imaging technique into the field of human cancer diagnosis, which holds substantial potential for clinical translations. In addition, I have contributed a project aimed at miniaturizing the CARS imaging device into a microendoscope setup through a fiber-delivery strategy. A four-wave-mixing (FWM) background signal, which is caused by simultaneous delivery of the two CARS-generating excitation laser beams, is initially identified. A polarization-based strategy is then introduced and tested for suppression of this FWM noise. The approach shows effective suppression of the FWM signal, both on microscopic and prototype endoscopic setups, indicating the potential of developing a novel microendoscope with a compatible size for clinical use. These positive results show promise for the development of an all-fiber-based, label-free imaging and analytical platform for minimally invasive detection and diagnosis of cancers during surgery or surgical-biopsy, thus improving surgical outcomes and reducing patients' suffering.

Face recognition with the Karhunen-Loeve transform

NASA Astrophysics Data System (ADS)

Suarez, Pedro F.

1991-12-01

The major goal of this research was to investigate machine recognition of faces. The approach taken to achieve this goal was to investigate the use of Karhunen-Loe've Transform (KLT) by implementing flexible and practical code. The KLT utilizes the eigenvectors of the covariance matrix as a basis set. Faces were projected onto the eigenvectors, called eigenfaces, and the resulting projection coefficients were used as features. Face recognition accuracies for the KLT coefficients were superior to Fourier based techniques. Additionally, this thesis demonstrated the image compression and reconstruction capabilities of the KLT. This theses also developed the use of the KLT as a facial feature detector. The ability to differentiate between facial features provides a computer communications interface for non-vocal people with cerebral palsy. Lastly, this thesis developed a KLT based axis system for laser scanner data of human heads. The scanner data axis system provides the anthropometric community a more precise method of fitting custom helmets.

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images.

PubMed

Guo, Shengwen; Lai, Chunren; Wu, Congling; Cen, Guiyin

2017-01-01

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI-cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI-NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI-NC comparison. The best performances obtained by the SVM classifier using the essential features were 5-40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease.

Isolated Main Pancreatic Duct Dilatation: CT Differentiation Between Benign and Malignant Causes.

PubMed

Kim, Se Woo; Kim, Se Hyung; Lee, Dong Ho; Lee, Sang Min; Kim, Yeon Soo; Jang, Jin Young; Han, Joon Koo

2017-11-01

The purpose of this study is to retrospectively evaluate the differential CT features of isolated benign and malignant main pancreatic duct (MPD) dilatation and to investigate whether the diagnostic performance of radiologists can be improved with knowledge of these differential CT features. Forty-one patients who had isolated MPD dilatation without any visible mass on CT from January 2000 to October 2016 were retrospectively enrolled in the study. Two radiologists reviewed CT images in consensus for the location, shape (smooth vs abrupt), length of transition, dilated pancreatic duct (PD) diameter, presence of duct penetrating sign, parenchymal atrophy, attenuation difference, associated pancreatitis, calcification, PD or common bile duct (CBD) enhancement, and perilesional cyst. The chi-square test, Fisher exact test, and t test were used to find the differential CT features of benign and malignant MPD dilatation. Two successive review sessions for differentiation between the two disease entities were then independently performed by three other reviewers with differing expertise, with the use of a 5-point confidence scale. The first session provided no information for differentiation; however, reviewers were aware of the results of univariate analyses in the second session. The diagnostic performance of the radiologists was evaluated using a pairwise comparison of ROC curves. A total of 19 benign and 22 malignant MPD dilatations were identified. In patients with benign MPD dilatation, transition areas were frequently located in the head (57.9% [11/19] vs 13.6% [3/22], p = 0.003) and showed significantly shorter (< 6.1 mm) (78.9% [15/19] vs 9.1% [2/22], p < 0.0001) and smooth transition (89.5% [17/19] vs 9.1% [2/22], p < 0.0001). Duct penetrating sign was exclusively observed in patients with benign MPD dilatation (73.7% [14/19] vs 0% [0/22], p < 0.0001). In contrast, malignant MPD dilatation frequently was accompanied by attenuation difference (63.6% [14/22] vs 10.5% [2/19], p = 0.001) and associated PD or CBD enhancement (36.4% [8/22] vs 0% [0/19], p = 0.003). The AUC values of three reviewers significantly increased from 0.653, 0.587, and 0.884 to 0.864, 0.964, and 0.908, respectively, with knowledge of significant CT features (p = 0.013, p < 0.0001, and p = 0.701, respectively). Distal, long (≥ 6.1 mm), and abrupt transition, the absence of duct penetrating sign, and the presence of attenuation difference and PD or CBD enhancement were highly suggestive CT findings for differentiation of malignant from benign MPD dilatation. The diagnostic performance of radiologists with regard to differentiation was significantly improved with knowledge of these highly suggestive CT criteria.

Morphometry Based on Effective and Accurate Correspondences of Localized Patterns (MEACOLP)

PubMed Central

Wang, Hu; Ren, Yanshuang; Bai, Lijun; Zhang, Wensheng; Tian, Jie

2012-01-01

Local features in volumetric images have been used to identify correspondences of localized anatomical structures for brain morphometry. However, the correspondences are often sparse thus ineffective in reflecting the underlying structures, making it unreliable to evaluate specific morphological differences. This paper presents a morphometry method (MEACOLP) based on correspondences with improved effectiveness and accuracy. A novel two-level scale-invariant feature transform is used to enhance the detection repeatability of local features and to recall the correspondences that might be missed in previous studies. Template patterns whose correspondences could be commonly identified in each group are constructed to serve as the basis for morphometric analysis. A matching algorithm is developed to reduce the identification errors by comparing neighboring local features and rejecting unreliable matches. The two-sample t-test is finally adopted to analyze specific properties of the template patterns. Experiments are performed on the public OASIS database to clinically analyze brain images of Alzheimer's disease (AD) and normal controls (NC). MEACOLP automatically identifies known morphological differences between AD and NC brains, and characterizes the differences well as the scaling and translation of underlying structures. Most of the significant differences are identified in only a single hemisphere, indicating that AD-related structures are characterized by strong anatomical asymmetry. In addition, classification trials to differentiate AD subjects from NC confirm that the morphological differences are reliably related to the groups of interest. PMID:22540000

Differences in the Tongue Features of Primary Dysmenorrhea Patients and Controls over a Normal Menstrual Cycle

PubMed Central

Lee, Haebeom

2017-01-01

Background The aims of this study were to investigate the relationships between tongue features and the existence of menstrual pain and to provide basic information regarding the changes in tongue features during a menstrual cycle. Methods This study was conducted at the Kyung Hee University Medical Center. Forty-eight eligible participants aged 20 to 29 years were enrolled and assigned to two groups according to their visual analogue scale (VAS) scores. Group A included 24 females suffering from primary dysmenorrhea (PD) caused by qi stagnation and blood stasis syndrome with VAS ≥ 4. In contrast, Group B included 24 females with few premenstrual symptoms and VAS < 4. All participants completed four visits (menses-follicular-luteal-menses phases), and the tongue images were taken by using a computerized tongue image analysis system (CTIS). Results The results revealed that the tongue coating color value and the tongue coating thickness in the PD group during the menstrual phase were significantly lower than those of the control group (P = 0.031 and P = 0.029, resp.). Conclusions These results suggest that the tongue features obtained from the CTIS may serve as a supplementary means for the differentiation of syndromes and the evaluation of therapeutic effect and prognosis in PD. Trial Registration This trial was registered with Clinical Research Information Service, registration number KCT0001604, registered on 27 August 2015. PMID:28642801

Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features.

PubMed

Acharya, U Rajendra; Koh, Joel En Wei; Hagiwara, Yuki; Tan, Jen Hong; Gertych, Arkadiusz; Vijayananthan, Anushya; Yaakup, Nur Adura; Abdullah, Basri Johan Jeet; Bin Mohd Fabell, Mohd Kamil; Yeong, Chai Hong

2018-03-01

Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of these conditions can improve treatment outcomes. Ultrasound imaging is a safe, non-invasive, and cost-effective way of diagnosing liver lesions. However, this technique has limited performance in determining the nature of the lesions. This study initiates a computer-aided diagnosis (CAD) system to aid radiologists in an objective and more reliable interpretation of ultrasound images of liver lesions. In this work, we have employed radon transform and bi-directional empirical mode decomposition (BEMD) to extract features from the focal liver lesions. After which, the extracted features were subjected to particle swarm optimization (PSO) technique for the selection of a set of optimized features for classification. Our automated CAD system can differentiate normal, malignant, and benign liver lesions using machine learning algorithms. It was trained using 78 normal, 26 benign and 36 malignant focal lesions of the liver. The accuracy, sensitivity, and specificity of lesion classification were 92.95%, 90.80%, and 97.44%, respectively. The proposed CAD system is fully automatic as no segmentation of region-of-interest (ROI) is required. Copyright © 2018 Elsevier Ltd. All rights reserved.

Robust crop and weed segmentation under uncontrolled outdoor illumination.

PubMed

Jeon, Hong Y; Tian, Lei F; Zhu, Heping

2011-01-01

An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA).

Ganymede G1 & G2 Encounters - Interior of Ganymede

NASA Image and Video Library

1997-12-16

NASA's Voyager images are used to create a global view of Ganymede. The cut-out reveals the interior structure of this icy moon. This structure consists of four layers based on measurements of Ganymede's gravity field and theoretical analyses using Ganymede's known mass, size and density. Ganymede's surface is rich in water ice and Voyager and Galileo images show features which are evidence of geological and tectonic disruption of the surface in the past. As with the Earth, these geological features reflect forces and processes deep within Ganymede's interior. Based on geochemical and geophysical models, scientists expected Ganymede's interior to either consist of: a) an undifferentiated mixture of rock and ice or b) a differentiated structure with a large lunar sized "core" of rock and possibly iron overlain by a deep layer of warm soft ice capped by a thin cold rigid ice crust. Galileo's measurement of Ganymede's gravity field during its first and second encounters with the huge moon have basically confirmed the differentiated model and allowed scientists to estimate the size of these layers more accurately. In addition the data strongly suggest that a dense metallic core exists at the center of the rock core. This metallic core suggests a greater degree of heating at sometime in Ganymede's past than had been proposed before and may be the source of Ganymede's magnetic field discovered by Galileo's space physics experiments. http://photojournal.jpl.nasa.gov/catalog/PIA00519

Natural image classification driven by human brain activity

NASA Astrophysics Data System (ADS)

Zhang, Dai; Peng, Hanyang; Wang, Jinqiao; Tang, Ming; Xue, Rong; Zuo, Zhentao

2016-03-01

Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.

Are rheumatoid arthritis patients discernible from other early arthritis patients using 1.5T extremity magnetic resonance imaging? a large cross-sectional study.

PubMed

Stomp, Wouter; Krabben, Annemarie; van der Heijde, Désirée; Huizinga, Tom W J; Bloem, Johan L; van der Helm-van Mil, Annette H M; Reijnierse, Monique

2014-08-01

Magnetic resonance imaging (MRI) is increasingly used in rheumatoid arthritis (RA) research. A European League Against Rheumatism (EULAR) task force recently suggested that MRI can improve the certainty of RA diagnosis. Because this recommendation may reflect a tendency to use MRI in daily practice, thorough studies on the value of MRI are required. Thus far no large studies have evaluated the accuracy of MRI to differentiate early RA from other patients with early arthritis. We performed a large cross-sectional study to determine whether patients who are clinically classified with RA differ in MRI features compared to patients with other diagnoses. In our study, 179 patients presenting with early arthritis (median symptom duration 15.4 weeks) underwent 1.5T extremity MRI of unilateral wrist, metacarpophalangeal, and metatarsophalangeal joints according to our arthritis protocol, the foot without contrast. Images were scored according to OMERACT Rheumatoid Arthritis Magnetic Resonance Imaging Scoring (RAMRIS) by 2 independent readers. Tenosynovitis was also assessed. The main outcome was fulfilling the 1987 American College of Rheumatology (ACR) criteria for RA. Test characteristics and areas under the receiver-operator-characteristic curves (AUC) were evaluated. In subanalyses, the 2010 ACR/EULAR criteria were used as outcome, and analyses were stratified for anticitrullinated protein antibodies (ACPA). The ACR 1987 criteria were fulfilled in 43 patients (24.0%). Patients with RA had higher scores for synovitis, tenosynovitis, and bone marrow edema (BME) than patients without RA (p < 0.05). ACPA-positive patients had more BME (median scores 6.5 vs. 4.25, p = 0.016) than ACPA-negative patients. For all MRI features, the predictive value for the presence of RA was low (< 50%). For all MRI features the AUC were < 0.70. Patients who fulfilled ACR/EULAR 2010 criteria but not ACR87 criteria for RA had less synovitis than patients who were positive for RA according to both sets of criteria (p = 0.029). Although patients with RA had higher scores of MRI inflammation and ACPA-positive patients had more BME, the severity of MRI inflammation assessed according to RAMRIS does not accurately differentiate patients with RA from other early arthritis patients.

Ionospheric-thermospheric UV tomography: 1. Image space reconstruction algorithms

NASA Astrophysics Data System (ADS)

Dymond, K. F.; Budzien, S. A.; Hei, M. A.

2017-03-01

We present and discuss two algorithms of the class known as Image Space Reconstruction Algorithms (ISRAs) that we are applying to the solution of large-scale ionospheric tomography problems. ISRAs have several desirable features that make them useful for ionospheric tomography. In addition to producing nonnegative solutions, ISRAs are amenable to sparse-matrix formulations and are fast, stable, and robust. We present the results of our studies of two types of ISRA: the Least Squares Positive Definite and the Richardson-Lucy algorithms. We compare their performance to the Multiplicative Algebraic Reconstruction and Conjugate Gradient Least Squares algorithms. We then discuss the use of regularization in these algorithms and present our new approach based on regularization to a partial differential equation.

Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography

NASA Astrophysics Data System (ADS)

Jesse, S.; Chi, M.; Belianinov, A.; Beekman, C.; Kalinin, S. V.; Borisevich, A. Y.; Lupini, A. R.

2016-05-01

Electron microscopy is undergoing a transition; from the model of producing only a few micrographs, through the current state where many images and spectra can be digitally recorded, to a new mode where very large volumes of data (movies, ptychographic and multi-dimensional series) can be rapidly obtained. Here, we discuss the application of so-called “big-data” methods to high dimensional microscopy data, using unsupervised multivariate statistical techniques, in order to explore salient image features in a specific example of BiFeO3 domains. Remarkably, k-means clustering reveals domain differentiation despite the fact that the algorithm is purely statistical in nature and does not require any prior information regarding the material, any coexisting phases, or any differentiating structures. While this is a somewhat trivial case, this example signifies the extraction of useful physical and structural information without any prior bias regarding the sample or the instrumental modality. Further interpretation of these types of results may still require human intervention. However, the open nature of this algorithm and its wide availability, enable broad collaborations and exploratory work necessary to enable efficient data analysis in electron microscopy.

[Study of 3D-pcASL in differentiation of acute cerebral infarction and acute encephalitis].

PubMed

Mao, Chuanwan; Fu, Yuchuan; Ye, Xinjian; Wu, Aiqin; Yan, Zhihan

2015-06-16

To investigate the value of three-dimentional pseudo-continuous arterial spin labeling (ASL) perfusion imaging in differentiating acute cerebral infarction from acute encephalitis. From September 2013 to September 2014, 42 patients with actue stroke onset and 20 healthy volunteers underwent conventional brain MRI DWI and 3D-ASL Perfusion Imaging in our hospital. Only 20 patients whose lesions located in the middle cerebral artery (MCA) territory were enrolled in this study. Of these cases, 12 cases were diagnosed with acute cerebral infarction, 8 were diagnosed with encephalitis. First, we analyzed the imaging features of the 20 patients and 20 volunteers. Then, CBF values of the lesions in the 20 patients and the gray matter of MCA territory in the 20 volunteers were measured on 3D-pcASL images. Third, the difference of mean CBF values between patients and volunteers were analyzed. Out of 20 study group, 19 patients whose lesions presented high signal intensity on DWI images, 12 cases were acute cerebral infarction and 8 were encephalitis. All the lesions of 20 cases showed abnormal perfusion on 3D-pcASL images. 3D-pcASL has good consistency with DWI in diagnostic capabilities (χ² = 0.565, P = 0.01). On 3D-pcASL, 11 acute cerebral infarction patients presented perfusion defects or low perfusion, 1 acute cerebral infarction patients showed high perfusion, 8 encephalitis patients showed inhomogeneous perfusion. The mean value of CBF was (17 ± 6) ml · min⁻¹ · 100 g⁻¹ in 12 acute cerebral infarction patients, (136 ± 69) ml · min⁻¹ · 100 g⁻¹ in 8 encephalitis patients and (68 ± 12) ml · min⁻¹ · 100 g⁻¹ three in 20 healthy volunteers. The difference in mean value of CBF among the three groups was statistically significant (P < 0.01). Acute cerebral infarction often shows low perfusion and acute encephalitis shows high perfusion on 3D-pcASL images, which has a higher application value in diagnosis and differentiation of acute cerebral infarction and encephalitis.

Imaging of blood cells based on snapshot Hyper-Spectral Imaging systems

NASA Astrophysics Data System (ADS)

Robison, Christopher J.; Kolanko, Christopher; Bourlai, Thirimachos; Dawson, Jeremy M.

2015-05-01

Snapshot Hyper-Spectral imaging systems are capable of capturing several spectral bands simultaneously, offering coregistered images of a target. With appropriate optics, these systems are potentially able to image blood cells in vivo as they flow through a vessel, eliminating the need for a blood draw and sample staining. Our group has evaluated the capability of a commercial Snapshot Hyper-Spectral imaging system, the Arrow system from Rebellion Photonics, in differentiating between white and red blood cells on unstained blood smear slides. We evaluated the imaging capabilities of this hyperspectral camera; attached to a microscope at varying objective powers and illumination intensity. Hyperspectral data consisting of 25, 443x313 hyperspectral bands with ~3nm spacing were captured over the range of 419 to 494nm. Open-source hyper-spectral data cube analysis tools, used primarily in Geographic Information Systems (GIS) applications, indicate that white blood cells features are most prominent in the 428-442nm band for blood samples viewed under 20x and 50x magnification over a varying range of illumination intensities. These images could potentially be used in subsequent automated white blood cell segmentation and counting algorithms for performing in vivo white blood cell counting.

Hypertrophic Osteoarthropathy: Clinical and Imaging Features.

PubMed

Yap, Felix Y; Skalski, Matthew R; Patel, Dakshesh B; Schein, Aaron J; White, Eric A; Tomasian, Anderanik; Masih, Sulabha; Matcuk, George R

2017-01-01

Hypertrophic osteoarthropathy (HOA) is a medical condition characterized by abnormal proliferation of skin and periosteal tissues involving the extremities and characterized by three clinical features: digital clubbing (also termed Hippocratic fingers), periostosis of tubular bones, and synovial effusions. HOA can be a primary entity, known as pachydermoperiostosis, or can be secondary to extraskeletal conditions, with different prognoses and management implications for each. There is a high association between secondary HOA and malignancy, especially non-small cell lung cancer. In such cases, it can be considered a form of paraneoplastic syndrome. The most prevalent secondary causes of HOA are pulmonary in origin, which is why this condition was formerly referred to as hypertrophic pulmonary osteoarthropathy. HOA can also be associated with pleural, mediastinal, and cardiovascular causes, as well as extrathoracic conditions such as gastrointestinal tumors and infections, cirrhosis, and inflammatory bowel disease. Although the skeletal manifestations of HOA are most commonly detected with radiography, abnormalities can also be identified with other modalities such as computed tomography, magnetic resonance imaging, and bone scintigraphy. The authors summarize the pathogenesis, classification, causes, and symptoms and signs of HOA, including the genetics underlying the primary form (pachydermoperiostosis); describe key findings of HOA found at various imaging modalities, with examples of underlying causative conditions; and discuss features differentiating HOA from other causes of multifocal periostitis, such as thyroid acropachy, hypervitaminosis A, chronic venous insufficiency, voriconazole-induced periostitis, progressive diaphyseal dysplasia, and neoplastic causes such as lymphoma. © RSNA, 2016.

Differentiation of breast cancer cells in vitro is promoted by the concurrent influence of myoepithelial cells and relaxin.

PubMed Central

Bani, D.; Riva, A.; Bigazzi, M.; Bani Sacchi, T.

1994-01-01

Our previous studies showed that relaxin promotes differentiation of MCF-7 breast adenocarcinoma cells. In the current investigation, we aimed to elucidate whether the effect of the hormone is potentiated when MCF-7 cells are grown together with myoepithelial cells, thus creating a microenvironment reminiscent of the organised tissue architecture of the mammary parenchyma in vivo. The findings obtained reveal that most MCF-7 cells cultured alone have an undifferentiated, blast-like phenotype, only a minority showing a more differentiated phenotype with more organelles and rudimentary intercellular junctions. When co-cultured with myoepithelial cells more MCF-7 cells acquire ultrastructural features consistent with a more differentiated phenotype, such as a rich organellular complement, apical microvilli and intercellular junctions. When relaxin was added to the co-cultures, the ultrastructural signs of differentiation could be observed in even more MCF-7 cells and became more pronounced than in the absence of the hormone, judged by the appearance of a clear-cut polarisation of cytoplasmic organelles, an almost continuous coat of apical microvilli and numerous intracellular pseudolumina. Images Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 PMID:7947095

Heterotopic Pancreas: Histopathologic Features, Imaging Findings, and Complications.

PubMed

Rezvani, Maryam; Menias, Christine; Sandrasegaran, Kumaresan; Olpin, Jeffrey D; Elsayes, Khaled M; Shaaban, Akram M

2017-01-01

Heterotopic pancreas is a congenital anomaly in which pancreatic tissue is anatomically separate from the main gland. The most common locations of this displacement include the upper gastrointestinal tract-specifically, the stomach, duodenum, and proximal jejunum. Less common sites are the esophagus, ileum, Meckel diverticulum, biliary tree, mesentery, and spleen. Uncomplicated heterotopic pancreas is typically asymptomatic, with the lesion being discovered incidentally during an unrelated surgery, during an imaging examination, or at autopsy. The most common computed tomographic appearance of heterotopic pancreas is that of a small oval intramural mass with microlobulated margins and an endoluminal growth pattern. The attenuation and enhancement characteristics of these lesions parallel their histologic composition. Acinus-dominant lesions demonstrate avid homogeneous enhancement after intravenous contrast material administration, whereas duct-dominant lesions are hypovascular and heterogeneous. At magnetic resonance imaging, the heterotopic pancreas is isointense to the orthotopic pancreas, with characteristic T1 hyperintensity and early avid enhancement after intravenous gadolinium-based contrast material administration. Heterotopic pancreatic tissue has a rudimentary ductal system in which an orifice is sometimes visible at imaging as a central umbilication of the lesion. Complications of heterotopic pancreas include pancreatitis, pseudocyst formation, malignant degeneration, gastrointestinal bleeding, bowel obstruction, and intussusception. Certain complications may be erroneously diagnosed as malignancy. Paraduodenal pancreatitis is thought to be due to cystic degeneration of heterotopic pancreatic tissue in the medial wall of the duodenum. Recognizing the characteristic imaging features of heterotopic pancreas aids in differentiating it from cancer and thus in avoiding unnecessary surgery. © RSNA, 2017.

MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.

PubMed

Guo, Yanrong; Zhan, Yiqiang; Gao, Yaozong; Jiang, Jianguo; Shen, Dinggang

2013-01-01

Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary ( DDD ) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First , minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second , linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third , instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients.

PubMed

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

2017-01-01

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in the United States with a five-year survival rate of 7.2% for all stages. Although surgical resection is the only curative treatment, currently we are unable to differentiate between resectable patients with occult metastatic disease from those with potentially curable disease. Identification of patients with poor prognosis via early classification would help in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant therapy. PDAC ranges in appearance from homogeneously isoattenuating masses to heterogeneously hypovascular tumors on CT images; hence, we hypothesize that heterogeneity reflects underlying differences at the histologic or genetic level and will therefore correlate with patient outcome. We quantify heterogeneity of PDAC with texture analysis to predict 2-year survival. Using fuzzy minimum-redundancy maximum-relevance feature selection and a naive Bayes classifier, the proposed features achieve an area under receiver operating characteristic curve (AUC) of 0.90 and accuracy (Ac) of 82.86% with the leave-one-image-out technique and an AUC of 0.80 and Ac of 75.0% with three-fold cross-validation. We conclude that texture analysis can be used to quantify heterogeneity in CT images to accurately predict 2-year survival in patients with pancreatic cancer. From these data, we infer differences in the biological evolution of pancreatic cancer subtypes measurable in imaging and identify opportunities for optimized patient selection for therapy.

Fine-Grained Parcellation of Brain Connectivity Improves Differentiation of States of Consciousness During Graded Propofol Sedation.

PubMed

Liu, Xiaolin; Lauer, Kathryn K; Ward, B Douglas; Roberts, Christopher J; Liu, Suyan; Gollapudy, Suneeta; Rohloff, Robert; Gross, William; Xu, Zhan; Chen, Guangyu; Binder, Jeffrey R; Li, Shi-Jiang; Hudetz, Anthony G

2017-08-01

Conscious perception relies on interactions between spatially and functionally distinct modules of the brain at various spatiotemporal scales. These interactions are altered by anesthesia, an intervention that leads to fading consciousness. Relatively little is known about brain functional connectivity and its anesthetic modulation at a fine spatial scale. Here, we used functional imaging to examine propofol-induced changes in functional connectivity in brain networks defined at a fine-grained parcellation based on a combination of anatomical and functional features. Fifteen healthy volunteers underwent resting-state functional imaging in wakeful baseline, mild sedation, deep sedation, and recovery of consciousness. Compared with wakeful baseline, propofol produced widespread, dose-dependent functional connectivity changes that scaled with the extent to which consciousness was altered. The dominant changes in connectivity were associated with the frontal lobes. By examining node pairs that demonstrated a trend of functional connectivity change between wakefulness and deep sedation, quadratic discriminant analysis differentiated the states of consciousness in individual participants more accurately at a fine-grained parcellation (e.g., 2000 nodes) than at a coarse-grained parcellation (e.g., 116 anatomical nodes). Our study suggests that defining brain networks at a high granularity may provide a superior imaging-based distinction of the graded effect of anesthesia on consciousness.

RAMAN spectroscopy imaging improves the diagnosis of papillary thyroid carcinoma

NASA Astrophysics Data System (ADS)

Rau, Julietta V.; Graziani, Valerio; Fosca, Marco; Taffon, Chiara; Rocchia, Massimiliano; Crucitti, Pierfilippo; Pozzilli, Paolo; Onetti Muda, Andrea; Caricato, Marco; Crescenzi, Anna

2016-10-01

Recent investigations strongly suggest that Raman spectroscopy (RS) can be used as a clinical tool in cancer diagnosis to improve diagnostic accuracy. In this study, we evaluated the efficiency of Raman imaging microscopy to discriminate between healthy and neoplastic thyroid tissue, by analyzing main variants of Papillary Thyroid Carcinoma (PTC), the most common type of thyroid cancer. We performed Raman imaging of large tissue areas (from 100 × 100 μm2 up to 1 × 1 mm2), collecting 38 maps containing about 9000 Raman spectra. Multivariate statistical methods, including Linear Discriminant Analysis (LDA), were applied to translate Raman spectra differences between healthy and PTC tissues into diagnostically useful information for a reliable tissue classification. Our study is the first demonstration of specific biochemical features of the PTC profile, characterized by significant presence of carotenoids with respect to the healthy tissue. Moreover, this is the first evidence of Raman spectra differentiation between classical and follicular variant of PTC, discriminated by LDA with high efficiency. The combined histological and Raman microscopy analyses allow clear-cut integration of morphological and biochemical observations, with dramatic improvement of efficiency and reliability in the differential diagnosis of neoplastic thyroid nodules, paving the way to integrative findings for tumorigenesis and novel therapeutic strategies.

Methods to Differentiate Radiation Necrosis and Recurrent Disease in Gliomas

NASA Astrophysics Data System (ADS)

Ewell, Lars

2007-03-01

Given the difficulty in differentiating Radiation Induced Necrosis (RIN) and recurrent disease in glioma patients using conventional techniques (CT scans, MRI scans), researchers have looked for different imaging modalities. Among these different modalities are Diffusion Weighted Magnetic Resonance Imaging (DWMRI) and Magnetic Resonance Spectroscopy (MRS). In DWMRI, an Apparent Diffusion Coefficient (ADC) is calculated for a Region Of Interest (ROI), and then monitored over time (longitudinally). In the brain, different anatomical features can complicate the interpretation of ADCs. In particular, the density and spatial variation of the cerebral spinal fluid filled fissures known as sulci can influence how a change in an ADC is explained. We have used the covariance of pixel intensity in T1 weighted MRI scans to study how intra-patient and inter-patient sulci density varies, and will present these results. MRS uses the shift in the MR signal due to the local chemical environment to determine the concentration of brain metabolites like choline and creatin. The ratio of metabolites such as these has been shown to have the power to discriminate between RIN and recurrent disease in glioma patients. At our institution, we have initiated a protocol whereby we will use DWMRI and MRS to study how best to utilize these complimentary forms of imaging.

Differentiating invasive and pre-invasive lung cancer by quantitative analysis of histopathologic images

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Sun, Hongliu; Chan, Heang-Ping; Chughtai, Aamer; Wei, Jun; Hadjiiski, Lubomir; Kazerooni, Ella

2018-02-01

We are developing automated radiopathomics method for diagnosis of lung nodule subtypes. In this study, we investigated the feasibility of using quantitative methods to analyze the tumor nuclei and cytoplasm in pathologic wholeslide images for the classification of pathologic subtypes of invasive nodules and pre-invasive nodules. We developed a multiscale blob detection method with watershed transform (MBD-WT) to segment the tumor cells. Pathomic features were extracted to characterize the size, morphology, sharpness, and gray level variation in each segmented nucleus and the heterogeneity patterns of tumor nuclei and cytoplasm. With permission of the National Lung Screening Trial (NLST) project, a data set containing 90 digital haematoxylin and eosin (HE) whole-slide images from 48 cases was used in this study. The 48 cases contain 77 regions of invasive subtypes and 43 regions of pre-invasive subtypes outlined by a pathologist on the HE images using the pathological tumor region description provided by NLST as reference. A logistic regression model (LRM) was built using leave-one-case-out resampling and receiver operating characteristic (ROC) analysis for classification of invasive and pre-invasive subtypes. With 11 selected features, the LRM achieved a test area under the ROC curve (AUC) value of 0.91+/-0.03. The results demonstrated that the pathologic invasiveness of lung adenocarcinomas could be categorized with high accuracy using pathomics analysis.

An edge preserving differential image coding scheme

NASA Technical Reports Server (NTRS)

Rost, Martin C.; Sayood, Khalid

1992-01-01

Differential encoding techniques are fast and easy to implement. However, a major problem with the use of differential encoding for images is the rapid edge degradation encountered when using such systems. This makes differential encoding techniques of limited utility, especially when coding medical or scientific images, where edge preservation is of utmost importance. A simple, easy to implement differential image coding system with excellent edge preservation properties is presented. The coding system can be used over variable rate channels, which makes it especially attractive for use in the packet network environment.

Hyperspectral imaging for differentiation of foreign materials from pinto beans

NASA Astrophysics Data System (ADS)

Mehrubeoglu, Mehrube; Zemlan, Michael; Henry, Sam

2015-09-01

Food safety and quality in packaged products are paramount in the food processing industry. To ensure that packaged products are free of foreign materials, such as debris and pests, unwanted materials mixed with the targeted products must be detected before packaging. A portable hyperspectral imaging system in the visible-to-NIR range has been used to acquire hyperspectral data cubes from pinto beans that have been mixed with foreign matter. Bands and band ratios have been identified as effective features to develop a classification scheme for detection of foreign materials in pinto beans. A support vector machine has been implemented with a quadratic kernel to separate pinto beans and background (Class 1) from all other materials (Class 2) in each scene. After creating a binary classification map for the scene, further analysis of these binary images allows separation of false positives from true positives for proper removal action during packaging.

Abdominal hernias: Radiological features

PubMed Central

Lassandro, Francesco; Iasiello, Francesca; Pizza, Nunzia Luisa; Valente, Tullio; Stefano, Maria Luisa Mangoni di Santo; Grassi, Roberto; Muto, Roberto

2011-01-01

Abdominal wall hernias are common diseases of the abdomen with a global incidence approximately 4%-5%. They are distinguished in external, diaphragmatic and internal hernias on the basis of their localisation. Groin hernias are the most common with a prevalence of 75%, followed by femoral (15%) and umbilical (8%). There is a higher prevalence in males (M:F, 8:1). Diagnosis is usually made on physical examination. However, clinical diagnosis may be difficult, especially in patients with obesity, pain or abdominal wall scarring. In these cases, abdominal imaging may be the first clue to the correct diagnosis and to confirm suspected complications. Different imaging modalities are used: conventional radiographs or barium studies, ultrasonography and Computed Tomography. Imaging modalities can aid in the differential diagnosis of palpable abdominal wall masses and can help to define hernial contents such as fatty tissue, bowel, other organs or fluid. This work focuses on the main radiological findings of abdominal herniations. PMID:21860678