Classification of Textures Using Filter Based Local Feature Extraction

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Bocekci Veysel Gokhan

2016-01-01

Full Text Available In this work local features are used in feature extraction process in image processing for textures. The local binary pattern feature extraction method from textures are introduced. Filtering is also used during the feature extraction process for getting discriminative features. To show the effectiveness of the algorithm before the extraction process, three different noise are added to both train and test images. Wiener filter and median filter are used to remove the noise from images. We evaluate the performance of the method with Naïve Bayesian classifier. We conduct the comparative analysis on benchmark dataset with different filtering and size. Our experiments demonstrate that feature extraction process combine with filtering give promising results on noisy images.

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

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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 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.

Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

International Nuclear Information System (INIS)

Khalvati, Farzad; Wong, Alexander; Haider, Masoom A.

2015-01-01

Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data. In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models. The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy. Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a

Parametric classification of handvein patterns based on texture features

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Al Mahafzah, Harbi; Imran, Mohammad; Supreetha Gowda H., D.

2018-04-01

In this paper, we have developed Biometric recognition system adopting hand based modality Handvein,which has the unique pattern for each individual and it is impossible to counterfeit and fabricate as it is an internal feature. We have opted in choosing feature extraction algorithms such as LBP-visual descriptor, LPQ-blur insensitive texture operator, Log-Gabor-Texture descriptor. We have chosen well known classifiers such as KNN and SVM for classification. We have experimented and tabulated results of single algorithm recognition rate for Handvein under different distance measures and kernel options. The feature level fusion is carried out which increased the performance level.

Prostate cancer detection: Fusion of cytological and textural features.

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

2011-01-01

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

A Study of Feature Extraction Using Divergence Analysis of Texture Features

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

1982-01-01

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

A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer

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Pattichis Marios S

2007-11-01

Full Text Available Abstract Background In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i the distance from the tissue (panoramic vs close up, (ii difference in viewing angles and (iii color correction. Methods We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 × 576 pixels and 24 bits color for: (i a variety of testing targets from a color palette with a known color distribution, (ii different viewing angles, (iv two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i Statistical Features (SF, (ii Spatial Gray Level Dependence Matrices (SGLDM, and (iii Gray Level Difference Statistics (GLDS. All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. Results For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better

Application of textural features to the objective diagnosis of Alzheimer-type dementia

International Nuclear Information System (INIS)

Kodama, Naoki; Shimada, Tetsuo; Kaeriyama, Tomoharu; Kaneko, Tomoyuki; Fukumoto, Ichiro; Kobayashi, Yoshio

2003-01-01

In this study, patients with Alzheimer-type dementia were compared with healthy elderly individuals by means of 13 textural features to evaluate the application of these features to the objective diagnosis of the dementia. A statistically significant difference was found in 8 of the 13 textural features between dementia patients and healthy controls. Discriminant analysis using the eight features demonstrated a sensitivity of 91.2% and a specificity of 86.4%, with an overall accuracy of 89.1%. Multiple discriminant analysis using the eight features by dementia stage showed an overall accuracy of 78.2% for discrimination of four stages. These results indicate that quantitative textural feature measurements can be used as an objective diagnostic technique for Alzheimer-type dementia. (author)

Prostate cancer detection: Fusion of cytological and textural features

Directory of Open Access Journals (Sweden)

Kien Nguyen

2011-01-01

Full Text Available A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i to locate cancer regions in a large digitized tissue biopsy, and (ii to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue regions. In this paper, we address the first stage by presenting a cancer detection approach for the whole slide tissue image. We propose a novel method to extract a cytological feature, namely the presence of cancer nuclei (nuclei with prominent nucleoli in the tissue, and apply this feature to detect the cancer regions. Additionally, conventional image texture features which have been widely used in the literature are also considered. The performance comparison among the proposed cytological textural feature combination method, the texture-based method and the cytological feature-based method demonstrates the robustness of the extracted cytological feature. At a false positive rate of 6%, the proposed method is able to achieve a sensitivity of 78% on a dataset including six training images (each of which has approximately 4,000x7,000 pixels and 1 1 whole-slide test images (each of which has approximately 5,000x23,000 pixels. All images are at 20X magnification.

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

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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.

Classification of high resolution imagery based on fusion of multiscale texture features

International Nuclear Information System (INIS)

Liu, Jinxiu; Liu, Huiping; Lv, Ying; Xue, Xiaojuan

2014-01-01

In high resolution data classification process, combining texture features with spectral bands can effectively improve the classification accuracy. However, the window size which is difficult to choose is regarded as an important factor influencing overall classification accuracy in textural classification and current approaches to image texture analysis only depend on a single moving window which ignores different scale features of various land cover types. In this paper, we propose a new method based on the fusion of multiscale texture features to overcome these problems. The main steps in new method include the classification of fixed window size spectral/textural images from 3×3 to 15×15 and comparison of all the posterior possibility values for every pixel, as a result the biggest probability value is given to the pixel and the pixel belongs to a certain land cover type automatically. The proposed approach is tested on University of Pavia ROSIS data. The results indicate that the new method improve the classification accuracy compared to results of methods based on fixed window size textural classification

Dynamic facial expression recognition based on geometric and texture features

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Li, Ming; Wang, Zengfu

2018-04-01

Recently, dynamic facial expression recognition in videos has attracted growing attention. In this paper, we propose a novel dynamic facial expression recognition method by using geometric and texture features. In our system, the facial landmark movements and texture variations upon pairwise images are used to perform the dynamic facial expression recognition tasks. For one facial expression sequence, pairwise images are created between the first frame and each of its subsequent frames. Integration of both geometric and texture features further enhances the representation of the facial expressions. Finally, Support Vector Machine is used for facial expression recognition. Experiments conducted on the extended Cohn-Kanade database show that our proposed method can achieve a competitive performance with other methods.

SAR Image Classification Based on Its Texture Features

Institute of Scientific and Technical Information of China (English)

LI Pingxiang; FANG Shenghui

2003-01-01

SAR images not only have the characteristics of all-ay, all-eather, but also provide object information which is different from visible and infrared sensors. However, SAR images have some faults, such as more speckles and fewer bands. The authors conducted the experiments of texture statistics analysis on SAR image features in order to improve the accuracy of SAR image interpretation.It is found that the texture analysis is an effective method for improving the accuracy of the SAR image interpretation.

Low-Level Color and Texture Feature Extraction of Coral Reef Components

Directory of Open Access Journals (Sweden)

Ma. Sheila Angeli Marcos

2003-06-01

Full Text Available The purpose of this study is to develop a computer-based classifier that automates coral reef assessmentfrom digitized underwater video. We extract low-level color and texture features from coral images toserve as input to a high-level classifier. Low-level features for color were labeled blue, green, yellow/brown/orange, and gray/white, which are described by the normalized chromaticity histograms of thesemajor colors. The color matching capability of these features was determined through a technique called“Histogram Backprojection”. The low-level texture feature marks a region as coarse or fine dependingon the gray-level variance of the region.

SD LMS L-Filters for Filtration of Gray Level Images in Timespatial Domain Based on GLCM Features

Directory of Open Access Journals (Sweden)

Robert Hudec

2008-01-01

Full Text Available In this paper, the new kind of adaptive signal-dependent LMS L-filter for suppression of a mixed noise in greyscale images is developed. It is based on the texture parameter measurement as modification of spatial impulse detector structure. Moreover, the one of GLCM (Gray Level Co-occurrence Matrix features, namely, the contrast or inertia adjusted by threshold as switch between partial filters is utilised. Finally, at the positions of partial filters the adaptive LMS versions of L-filters are chosen.

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

Energy Technology Data Exchange (ETDEWEB)

Nketiah, Gabriel; Elschot, Mattijs; Kim, Eugene; Teruel, Jose R. [NTNU, Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine, Trondheim (Norway); Scheenen, Tom W. [Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen (Netherlands); Bathen, Tone F.; Selnaes, Kirsten M. [NTNU, Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine, Trondheim (Norway); St. Olavs Hospital, Trondheim University Hospital, Trondheim (Norway)

2017-07-15

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{sup trans} and V{sub 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{sup trans} and V{sub e}. GS 4+3 cancers had significantly lower ASM and higher entropy than 3+4 cancers, but insignificant differences in median ADC, K{sup trans}, and V{sub 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. (orig.)

Fusion of geometric and texture features for finger knuckle surface recognition

Directory of Open Access Journals (Sweden)

K. Usha

2016-03-01

Full Text Available Hand-based biometrics plays a significant role in establishing security for real-time environments involving human interaction and is found to be more successful in terms of high speed and accuracy. This paper investigates on an integrated approach for personal authentication using Finger Back Knuckle Surface (FBKS based on two methodologies viz., Angular Geometric Analysis based Feature Extraction Method (AGFEM and Contourlet Transform based Feature Extraction Method (CTFEM. Based on these methods, this personal authentication system simultaneously extracts shape oriented feature information and textural pattern information of FBKS for authenticating an individual. Furthermore, the proposed geometric and textural analysis methods extract feature information from both proximal phalanx and distal phalanx knuckle regions (FBKS, while the existing works of the literature concentrate only on the features of proximal phalanx knuckle region. The finger joint region found nearer to the tip of the finger is called distal phalanx region of FBKS, which is a unique feature and has greater potentiality toward identification. Extensive experiments conducted using newly created database with 5400 FBKS images and the obtained results infer that the integration of shape oriented features with texture feature information yields excellent accuracy rate of 99.12% with lowest equal error rate of 1.04%.

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

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

2006-01-01

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

Analysis of chemiluminescence measurements by grey-scale ICCD and colour digital cameras

International Nuclear Information System (INIS)

Migliorini, F; Maffi, S; De Iuliis, S; Zizak, G

2014-01-01

Spectral, grey-scale and colour chemiluminescence measurements of C 2 * and CH* radicals' emission are carried out on the flame front of a methane–air premixed flame at different equivalence ratios. To this purpose, properly spatially resolved optical equipment has been implemented in order to reduce the background emission from other burned gas regions. The grey-scale (ICCD + interference filters) and RGB colour (commercial digital camera) approaches have been compared in order to find a correspondence between the C 2 * and the green component, as well as the CH* and the blue component of the emission intensities. The C 2 */CH* chemiluminescence ratio has been investigated at different equivalence ratios and a good correlation has been obtained, showing the possibility of sensing the equivalence ratio in practical systems. The grey-scale and colour chemiluminescence analysis has then been applied to a meso-scale not premixed swirl combustor fuelled with a methane–air mixture and operating at 0.3 MPa. 2D results are presented and discussed in this work. (paper)

Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters

DEFF Research Database (Denmark)

Galavis, P.E.; Hollensen, Christian; Jallow, N.

2010-01-01

Background. Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes...... reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results. Fifty textural features were...... classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range 30%). Conclusion. Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small...

Histogram-based adaptive gray level scaling for texture feature classification of colorectal polyps

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Pomeroy, Marc; Lu, Hongbing; Pickhardt, Perry J.; Liang, Zhengrong

2018-02-01

Texture features have played an ever increasing role in computer aided detection (CADe) and diagnosis (CADx) methods since their inception. Texture features are often used as a method of false positive reduction for CADe packages, especially for detecting colorectal polyps and distinguishing them from falsely tagged residual stool and healthy colon wall folds. While texture features have shown great success there, the performance of texture features for CADx have lagged behind primarily because of the more similar features among different polyps types. In this paper, we present an adaptive gray level scaling and compare it to the conventional equal-spacing of gray level bins. We use a dataset taken from computed tomography colonography patients, with 392 polyp regions of interest (ROIs) identified and have a confirmed diagnosis through pathology. Using the histogram information from the entire ROI dataset, we generate the gray level bins such that each bin contains roughly the same number of voxels Each image ROI is the scaled down to two different numbers of gray levels, using both an equal spacing of Hounsfield units for each bin, and our adaptive method. We compute a set of texture features from the scaled images including 30 gray level co-occurrence matrix (GLCM) features and 11 gray level run length matrix (GLRLM) features. Using a random forest classifier to distinguish between hyperplastic polyps and all others (adenomas and adenocarcinomas), we find that the adaptive gray level scaling can improve performance based on the area under the receiver operating characteristic curve by up to 4.6%.

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

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Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J., E-mail: bje@mayo.edu [Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Coufalova, Lucie [Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Department of Neurosurgery of First Faculty of Medicine, Charles University in Prague, Military University Hospital, Prague 128 21 (Czech Republic); International Clinical Research Center, St. Anne’s University Hospital Brno, Brno 656 91 (Czech Republic); Lachance, Daniel H. [Department of Neurology, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Parney, Ian F. [Department of Neurologic Surgery, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Carter, Rickey E. [Department of Health Sciences Research, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Buckner, Jan C. [Department of Medical Oncology, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States)

2016-06-15

Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O{sup 6}-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status. Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78–0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

International Nuclear Information System (INIS)

Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J.; Coufalova, Lucie; Lachance, Daniel H.; Parney, Ian F.; Carter, Rickey E.; Buckner, Jan C.

2016-01-01

Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O 6 -methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status. Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78–0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.

Plaque echodensity and textural features are associated with histologic carotid plaque instability.

Science.gov (United States)

Doonan, Robert J; Gorgui, Jessica; Veinot, Jean P; Lai, Chi; Kyriacou, Efthyvoulos; Corriveau, Marc M; Steinmetz, Oren K; Daskalopoulou, Stella S

2016-09-01

Carotid plaque echodensity and texture features predict cerebrovascular symptomatology. Our purpose was to determine the association of echodensity and textural features obtained from a digital image analysis (DIA) program with histologic features of plaque instability as well as to identify the specific morphologic characteristics of unstable plaques. Patients scheduled to undergo carotid endarterectomy were recruited and underwent carotid ultrasound imaging. DIA was performed to extract echodensity and textural features using Plaque Texture Analysis software (LifeQ Medical Ltd, Nicosia, Cyprus). Carotid plaque surgical specimens were obtained and analyzed histologically. Principal component analysis (PCA) was performed to reduce imaging variables. Logistic regression models were used to determine if PCA variables and individual imaging variables predicted histologic features of plaque instability. Image analysis data from 160 patients were analyzed. Individual imaging features of plaque echolucency and homogeneity were associated with a more unstable plaque phenotype on histology. These results were independent of age, sex, and degree of carotid stenosis. PCA reduced 39 individual imaging variables to five PCA variables. PCA1 and PCA2 were significantly associated with overall plaque instability on histology (both P = .02), whereas PCA3 did not achieve statistical significance (P = .07). DIA features of carotid plaques are associated with histologic plaque instability as assessed by multiple histologic features. Importantly, unstable plaques on histology appear more echolucent and homogeneous on ultrasound imaging. These results are independent of stenosis, suggesting that image analysis may have a role in refining the selection of patients who undergo carotid endarterectomy. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

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

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

2011-06-01

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

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

Science.gov (United States)

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

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

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

Science.gov (United States)

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

2014-02-01

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

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

Science.gov (United States)

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

In vivo placental MRI shape and textural features predict fetal growth restriction and postnatal outcome.

Science.gov (United States)

Dahdouh, Sonia; Andescavage, Nickie; Yewale, Sayali; Yarish, Alexa; Lanham, Diane; Bulas, Dorothy; du Plessis, Adre J; Limperopoulos, Catherine

2018-02-01

To investigate the ability of three-dimensional (3D) MRI placental shape and textural features to predict fetal growth restriction (FGR) and birth weight (BW) for both healthy and FGR fetuses. We recruited two groups of pregnant volunteers between 18 and 39 weeks of gestation; 46 healthy subjects and 34 FGR. Both groups underwent fetal MR imaging on a 1.5 Tesla GE scanner using an eight-channel receiver coil. We acquired T2-weighted images on either the coronal or the axial plane to obtain MR volumes with a slice thickness of either 4 or 8 mm covering the full placenta. Placental shape features (volume, thickness, elongation) were combined with textural features; first order textural features (mean, variance, kurtosis, and skewness of placental gray levels), as well as, textural features computed on the gray level co-occurrence and run-length matrices characterizing placental homogeneity, symmetry, and coarseness. The features were used in two machine learning frameworks to predict FGR and BW. The proposed machine-learning based method using shape and textural features identified FGR pregnancies with 86% accuracy, 77% precision and 86% recall. BW estimations were 0.3 ± 13.4% (mean percentage error ± standard error) for healthy fetuses and -2.6 ± 15.9% for FGR. The proposed FGR identification and BW estimation methods using in utero placental shape and textural features computed on 3D MR images demonstrated high accuracy in our healthy and high-risk cohorts. Future studies to assess the evolution of each feature with regard to placental development are currently underway. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:449-458. © 2017 International Society for Magnetic Resonance in Medicine.

Ethnicity distinctiveness through iris texture features using Gabor filters

CSIR Research Space (South Africa)

Mabuza-Hocquet, Gugulethu P

2017-02-01

Full Text Available Research in iris biometrics has been focused on utilizing iris features as a means of identity verification and authentication. However, not enough research work has been done to explore iris textures to determine soft biometrics such as gender...

Parallel implementation of Gray Level Co-occurrence Matrices and Haralick texture features on cell architecture

NARCIS (Netherlands)

Shahbahrami, A.; Pham, T.A.; Bertels, K.L.M.

2011-01-01

Texture features extraction algorithms are key functions in various image processing applications such as medical images, remote sensing, and content-based image retrieval. The most common way to extract texture features is the use of Gray Level Co-occurrence Matrices (GLCMs). The GLCM contains the

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

Science.gov (United States)

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 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.

Analysis of the effect of spatial resolution on texture features in the classification of breast masses in mammograms

International Nuclear Information System (INIS)

Rangayyan, R.M.; Nguyen, T.M.; Ayres, F.J.; Nandi, A.K.

2007-01-01

The present study investigates the effect of spatial resolution on co-occurrence matrix-based texture features in discriminating breast lesions as benign masses or malignant tumors. The highest classification result, in terms of the area under the receiver operating characteristics (ROC) curve, of A z 0.74, was obtained at the spatial resolution of 800 μm using all 14 of Haralick's texture features computed using the margins, or ribbons, of the breast masses as seen on mammograms. Furthermore, our study indicates that texture features computed using the ribbons resulted in higher classification accuracy than the same texture features computed using the corresponding regions of interest within the mass boundaries drawn by an expert radiologist. Classification experiments using each single texture feature showed that the texture F 8 , sum entropy, gives consistently high classification results with an average A z of 0.64 across all levels of resolution. At certain levels of resolution, the textures F 5 , F 9 , and F 11 individually gave the highest classification result with A z = 0.70. (orig.)

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

Science.gov (United States)

Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image

International Nuclear Information System (INIS)

Wang Huan; Guo Xiuhua; Jia Zhongwei; Li Hongkai; Liang Zhigang; Li Kuncheng; He Qian

2010-01-01

Purpose: To introduce multilevel binomial logistic prediction model-based computer-aided diagnostic (CAD) method of small solitary pulmonary nodules (SPNs) diagnosis by combining patient and image characteristics by textural features of CT image. Materials and methods: Describe fourteen gray level co-occurrence matrix textural features obtained from 2171 benign and malignant small solitary pulmonary nodules, which belongs to 185 patients. Multilevel binomial logistic model is applied to gain these initial insights. Results: Five texture features, including Inertia, Entropy, Correlation, Difference-mean, Sum-Entropy, and age of patients own aggregating character on patient-level, which are statistically different (P < 0.05) between benign and malignant small solitary pulmonary nodules. Conclusion: Some gray level co-occurrence matrix textural features are efficiently descriptive features of CT image of small solitary pulmonary nodules, which can profit diagnosis of earlier period lung cancer if combined patient-level characteristics to some extent.

Parametric features of image textures in 18F-FDG PET/CT evaluation of lung nodules

International Nuclear Information System (INIS)

Wang Changmei; Guan Yihui; Zhang Wenqiang; Zuo Chuantao; Hua Fengchun

2013-01-01

Objective: To evaluate the parametric features of image textures on 18 F-FDG PET/CT for the differentiation between malignant and benign pulmonary nodules and compare the diagnostic performance of these parameters with SUV max . Methods: 18 F-FDG PET/CT images of 170 patients (102 males, 68 females, age range: 29-81 (mean 59) years) with pulmonary nodules were retrospectively evaluated. Eighty-nine pulmonary nodules (230 slices) were malignant and 81 (193 slices) were benign. The pulmonary nodules were contoured on CT images and mapped to the co-registered PET images. Thirteen parameters of textural features were extracted and SUV max was measured. Logistic regression analysis was used to identify the significant texture parameters and create a regression model. The efficacy of the textural features and SUV max to distinguish between malignant and benign pulmonary nodules was evaluated by ROC curve analysis. The textural features of squamous cell carcinoma and adenocarcinoma were compared via the Mann-Whitney u test. The sensitivity and specificity of the textural features and SUV max for the differential diagnosis were compared with χ 2 test. Results: Logistic regression model identified 4 textural features (skewness (β=1.7058), kurtosis (β=-1.0989), angular second moment (ASM, 3=-4.4140) and strength (β=0.5626); all P<0.05) to have significant correlation with the malignancy of lung nodules. The AUC of ROC curve was 0.775 (95% CI 0.732-0.819; P<0.001) with the sensitivity of 89.6% (206/230) and specificity of 50.8% (98/193). ASM and strength had statistically significant differences between squamous cell carcinoma and adenocarcinoma [ASM: 0.0303 (95% CI 0.0392-0.0724) vs 0.0594 (95% CI 0.0721-0.0947); strength: 2.4714 (95% CI 2.4632-4.1050) vs 1.5945 (95% CI 1.9003-2.4652); u=3082.0 and 3115.0, both P<0.01]. The AUC of SUV max -based diagnosis was 0.757 (95% CI 0.711-0.802; P<0.001) with the sensitivity of 80.9% (186/230) and specificity of 50.3% (97/193) at

Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

Directory of Open Access Journals (Sweden)

Jia Uddin

2014-01-01

Full Text Available This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D texture features and a multiclass support vector machine (MCSVM. The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns, and extracts these texture features by generating the dominant neighborhood structure (DNS map. The principal component analysis (PCA is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA multiclass support vector machines (MCSVMs to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition

Science.gov (United States)

Sultana, Maryam; Bhatti, Naeem; Javed, Sajid; Jung, Soon Ki

2017-09-01

Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.

Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters.

Science.gov (United States)

Galavis, Paulina E; Hollensen, Christian; Jallow, Ngoneh; Paliwal, Bhudatt; Jeraj, Robert

2010-10-01

Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45-60 minutes post-injection of 10 mCi of [(18)F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor

Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters

International Nuclear Information System (INIS)

Galavis, Paulina E.; Jallow, Ngoneh; Paliwal, Bhudatt; Jeraj, Robert; Hollensen, Christian

2010-01-01

Background. Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Material and methods. Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45-60 minutes post-injection of 10 mCi of [ 18 F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results. Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range = 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% = range = 25%) were entropy-GLCM, sum entropy, high gray level run emphasis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Conclusion. Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be

Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters.

Science.gov (United States)

Wang, Jing-Jing; Wu, Hai-Feng; Sun, Tao; Li, Xia; Wang, Wei; Tao, Li-Xin; Huo, Da; Lv, Ping-Xin; He, Wen; Guo, Xiu-Hua

2013-01-01

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters.

Science.gov (United States)

Brynolfsson, Patrik; Nilsson, David; Torheim, Turid; Asklund, Thomas; Karlsson, Camilla Thellenberg; Trygg, Johan; Nyholm, Tufve; Garpebring, Anders

2017-06-22

In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

Feature-aware natural texture synthesis

KAUST Repository

Wu, Fuzhang; Dong, Weiming; Kong, Yan; Mei, Xing; Yan, Dongming; Zhang, Xiaopeng; Paul, Jean Claude

2014-01-01

This article presents a framework for natural texture synthesis and processing. This framework is motivated by the observation that given examples captured in natural scene, texture synthesis addresses a critical problem, namely, that synthesis

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

Science.gov (United States)

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 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 textural features of any DCE-MRI parameter maps could discriminate between subtypes of grade II and III gliomas (P features revealed relatively lower inter-observer agreement. No significant correlation was found between microvascular density and textural features, compared with a moderate correlation found between cellular proliferation index and those features. Textural features of DCE-MRI parameter maps displayed a good ability in glioma grading. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1099-1111. © 2017 International Society for Magnetic Resonance in Medicine.

SU-F-R-35: Repeatability of Texture Features in T1- and T2-Weighted MR Images

International Nuclear Information System (INIS)

Mahon, R; Weiss, E; Karki, K; Hugo, G; Ford, J

2016-01-01

Purpose: To evaluate repeatability of lung tumor texture features from inspiration/expiration MR image pairs for potential use in patient specific care models and applications. Repeatability is a desirable and necessary characteristic of features included in such models. Methods: T1-weighted Volumetric Interpolation Breath-Hold Examination (VIBE) and/or T2-weighted MRI scans were acquired for 15 patients with non-small cell lung cancer before and during radiotherapy for a total of 32 and 34 same session inspiration-expiration breath-hold image pairs respectively. Bias correction was applied to the VIBE (VIBE-BC) and T2-weighted (T2-BC) images. Fifty-nine texture features at five wavelet decomposition ratios were extracted from the delineated primary tumor including: histogram(HIST), gray level co-occurrence matrix(GLCM), gray level run length matrix(GLRLM), gray level size zone matrix(GLSZM), and neighborhood gray tone different matrix (NGTDM) based features. Repeatability of the texture features for VIBE, VIBE-BC, T2-weighted, and T2-BC image pairs was evaluated by the concordance correlation coefficient (CCC) between corresponding image pairs, with a value greater than 0.90 indicating repeatability. Results: For the VIBE image pairs, the percentage of repeatable texture features by wavelet ratio was between 20% and 24% of the 59 extracted features; the T2-weighted image pairs exhibited repeatability in the range of 44–49%. The percentage dropped to 10–20% for the VIBE-BC images, and 12–14% for the T2-BC images. In addition, five texture features were found to be repeatable in all four image sets including two GLRLM, two GLZSM, and one NGTDN features. No single texture feature category was repeatable among all three image types; however, certain categories performed more consistently on a per image type basis. Conclusion: We identified repeatable texture features on T1- and T2-weighted MRI scans. These texture features should be further investigated for use

SU-F-R-35: Repeatability of Texture Features in T1- and T2-Weighted MR Images

Energy Technology Data Exchange (ETDEWEB)

Mahon, R; Weiss, E; Karki, K; Hugo, G [Virginia Commonwealth University, Richmond, VA (United States); Ford, J [University of Miami Miller School of Medicine, Miami, FL (United States)

2016-06-15

Purpose: To evaluate repeatability of lung tumor texture features from inspiration/expiration MR image pairs for potential use in patient specific care models and applications. Repeatability is a desirable and necessary characteristic of features included in such models. Methods: T1-weighted Volumetric Interpolation Breath-Hold Examination (VIBE) and/or T2-weighted MRI scans were acquired for 15 patients with non-small cell lung cancer before and during radiotherapy for a total of 32 and 34 same session inspiration-expiration breath-hold image pairs respectively. Bias correction was applied to the VIBE (VIBE-BC) and T2-weighted (T2-BC) images. Fifty-nine texture features at five wavelet decomposition ratios were extracted from the delineated primary tumor including: histogram(HIST), gray level co-occurrence matrix(GLCM), gray level run length matrix(GLRLM), gray level size zone matrix(GLSZM), and neighborhood gray tone different matrix (NGTDM) based features. Repeatability of the texture features for VIBE, VIBE-BC, T2-weighted, and T2-BC image pairs was evaluated by the concordance correlation coefficient (CCC) between corresponding image pairs, with a value greater than 0.90 indicating repeatability. Results: For the VIBE image pairs, the percentage of repeatable texture features by wavelet ratio was between 20% and 24% of the 59 extracted features; the T2-weighted image pairs exhibited repeatability in the range of 44–49%. The percentage dropped to 10–20% for the VIBE-BC images, and 12–14% for the T2-BC images. In addition, five texture features were found to be repeatable in all four image sets including two GLRLM, two GLZSM, and one NGTDN features. No single texture feature category was repeatable among all three image types; however, certain categories performed more consistently on a per image type basis. Conclusion: We identified repeatable texture features on T1- and T2-weighted MRI scans. These texture features should be further investigated for use

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

International Nuclear Information System (INIS)

Xie, Y; Wang, J; Wang, C; Chang, Z

2016-01-01

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

CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer.

Science.gov (United States)

Lee, Scott J; Zea, Ryan; Kim, David H; Lubner, Meghan G; Deming, Dustin A; Pickhardt, Perry J

2018-04-01

To determine if identifiable hepatic textural features are present at abdominal CT in patients with colorectal cancer (CRC) prior to the development of CT-detectable hepatic metastases. Four filtration-histogram texture features (standard deviation, skewness, entropy and kurtosis) were extracted from the liver parenchyma on portal venous phase CT images at staging and post-treatment surveillance. Surveillance scans corresponded to the last scan prior to the development of CT-detectable CRC liver metastases in 29 patients (median time interval, 6 months), and these were compared with interval-matched surveillance scans in 60 CRC patients who did not develop liver metastases. Predictive models of liver metastasis-free survival and overall survival were built using regularised Cox proportional hazards regression. Texture features did not significantly differ between cases and controls. For Cox models using all features as predictors, all coefficients were shrunk to zero, suggesting no association between any CT texture features and outcomes. Prognostic indices derived from entropy features at surveillance CT incorrectly classified patients into risk groups for future liver metastases (p < 0.001). On surveillance CT scans immediately prior to the development of CRC liver metastases, we found no evidence suggesting that changes in identifiable hepatic texture features were predictive of their development. • No correlation between liver texture features and metastasis-free survival was observed. • Liver texture features incorrectly classified patients into risk groups for liver metastases. • Standardised texture analysis workflows need to be developed to improve research reproducibility.

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

Science.gov (United States)

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

2015-10-01

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

Breast tissue classification in digital breast tomosynthesis images using texture features: a feasibility study

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Kontos, Despina; Berger, Rachelle; Bakic, Predrag R.; Maidment, Andrew D. A.

2009-02-01

Mammographic breast density is a known breast cancer risk factor. Studies have shown the potential to automate breast density estimation by using computerized texture-based segmentation of the dense tissue in mammograms. Digital breast tomosynthesis (DBT) is a tomographic x-ray breast imaging modality that could allow volumetric breast density estimation. We evaluated the feasibility of distinguishing between dense and fatty breast regions in DBT using computer-extracted texture features. Our long-term hypothesis is that DBT texture analysis can be used to develop 3D dense tissue segmentation algorithms for estimating volumetric breast density. DBT images from 40 women were analyzed. The dense tissue area was delineated within each central source projection (CSP) image using a thresholding technique (Cumulus, Univ. Toronto). Two (2.5cm)2 ROIs were manually selected: one within the dense tissue region and another within the fatty region. Corresponding (2.5cm)3 ROIs were placed within the reconstructed DBT images. Texture features, previously used for mammographic dense tissue segmentation, were computed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate feature classification performance. Different texture features appeared to perform best in the 3D reconstructed DBT compared to the 2D CSP images. Fractal dimension was superior in DBT (AUC=0.90), while contrast was best in CSP images (AUC=0.92). We attribute these differences to the effects of tissue superimposition in CSP and the volumetric visualization of the breast tissue in DBT. Our results suggest that novel approaches, different than those conventionally used in projection mammography, need to be investigated in order to develop DBT dense tissue segmentation algorithms for estimating volumetric breast density.

Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images

Science.gov (United States)

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

2017-08-01

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

Classification of Carotid Plaque Echogenicity by Combining Texture Features and Morphologic Characteristics.

Science.gov (United States)

Huang, Xiaowei; Zhang, Yanling; Qian, Ming; Meng, Long; Xiao, Yang; Niu, Lili; Zheng, Rongqin; Zheng, Hairong

2016-10-01

Anechoic carotid plaques on sonography have been used to predict future cardiovascular or cerebrovascular events. The purpose of this study was to investigate whether carotid plaque echogenicity could be assessed objectively by combining texture features extracted by MaZda software (Institute of Electronics, Technical University of Lodz, Lodz, Poland) and morphologic characteristics, which may provide a promising method for early prediction of acute cardiovascular disease. A total of 268 plaque images were collected from 136 volunteers and classified into 85 hyperechoic, 83 intermediate, and 100 anechoic plaques. About 300 texture features were extracted from histogram, absolute gradient, run-length matrix, gray-level co-occurrence matrix, autoregressive model, and wavelet transform algorithms by MaZda. The morphologic characteristics, including degree of stenosis, maximum plaque intima-media thickness, and maximum plaque length, were measured by B-mode sonography. Statistically significant features were selected by analysis of covariance. The most discriminative features were obtained from statistically significant features by linear discriminant analysis. The K-nearest neighbor classifier was used to classify plaque echogenicity based on statistically significant and most discriminative features. A total of 30 statistically significant features were selected among the plaques, and 2 most discriminative features were obtained from the statistically significant features. The classification accuracy rates for 3 types of plaques based on statistically significant and most discriminative features were 72.03% (κ= 0.571; P MaZda and morphologic characteristics.

Linear feature selection in texture analysis - A PLS based method

DEFF Research Database (Denmark)

Marques, Joselene; Igel, Christian; Lillholm, Martin

2013-01-01

We present a texture analysis methodology that combined uncommitted machine-learning techniques and partial least square (PLS) in a fully automatic framework. Our approach introduces a robust PLS-based dimensionality reduction (DR) step to specifically address outliers and high-dimensional feature...... and considering all CV groups, the methods selected 36 % of the original features available. The diagnosis evaluation reached a generalization area-under-the-ROC curve of 0.92, which was higher than established cartilage-based markers known to relate to OA diagnosis....

Use of feature extraction techniques for the texture and context information in ERTS imagery. [discrimination of land use categories in Kansas from MSS textural-spectral features

Science.gov (United States)

Haralick, R. M.; Kelly, G. L. (Principal Investigator); Bosley, R. J.

1973-01-01

The author has identified the following significant results. The land use category of subimage regions over Kansas within an MSS image can be identified with an accuracy of about 70% using the textural-spectral features of the multi-images from the four MSS bands.

A statistical-textural-features based approach for classification of solid drugs using surface microscopic images.

Science.gov (United States)

Tahir, Fahima; Fahiem, Muhammad Abuzar

2014-01-01

The quality of pharmaceutical products plays an important role in pharmaceutical industry as well as in our lives. Usage of defective tablets can be harmful for patients. In this research we proposed a nondestructive method to identify defective and nondefective tablets using their surface morphology. Three different environmental factors temperature, humidity and moisture are analyzed to evaluate the performance of the proposed method. Multiple textural features are extracted from the surface of the defective and nondefective tablets. These textural features are gray level cooccurrence matrix, run length matrix, histogram, autoregressive model and HAAR wavelet. Total textural features extracted from images are 281. We performed an analysis on all those 281, top 15, and top 2 features. Top 15 features are extracted using three different feature reduction techniques: chi-square, gain ratio and relief-F. In this research we have used three different classifiers: support vector machine, K-nearest neighbors and naïve Bayes to calculate the accuracies against proposed method using two experiments, that is, leave-one-out cross-validation technique and train test models. We tested each classifier against all selected features and then performed the comparison of their results. The experimental work resulted in that in most of the cases SVM performed better than the other two classifiers.

Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

Energy Technology Data Exchange (ETDEWEB)

Fried, David V. [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas (United States); Tucker, Susan L. [Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Zhou, Shouhao [Division of Quantitative Sciences, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Liao, Zhongxing [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Mawlawi, Osama [Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas (United States); Ibbott, Geoffrey [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas (United States); Court, Laurence E., E-mail: LECourt@mdanderson.org [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas (United States)

2014-11-15

Purpose: To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non-small cell lung cancer (NSCLC). Methods and Materials: We retrospectively reviewed 91 cases with stage III NSCLC treated with definitive chemoradiation therapy. All patients underwent pretreatment diagnostic contrast enhanced computed tomography (CE-CT) followed by 4-dimensional CT (4D-CT) for treatment simulation. We used the average-CT and expiratory (T50-CT) images from the 4D-CT along with the CE-CT for texture extraction. Histogram, gradient, co-occurrence, gray tone difference, and filtration-based techniques were used for texture feature extraction. Penalized Cox regression implementing cross-validation was used for covariate selection and modeling. Models incorporating texture features from the 33 image types and CPFs were compared to those with models incorporating CPFs alone for overall survival (OS), local-regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan-Meier curves were generated using leave-one-out cross-validation. Patients were stratified based on whether their predicted outcome was above or below the median. Reproducibility of texture features was evaluated using test-retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test-retest scans to our original models and determined the classification reproducibility. Results: Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone for OS (P=.046), LRC (P=.01), and FFDM (P=.005). The average CCCs were 0.89, 0.91, and 0.67 for texture features extracted from the average-CT, T50-CT, and CE-CT, respectively. Incorporating reproducibility within our models yielded 80.4% (±3.7% SD), 78.3% (±4.0% SD), and 78

Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

International Nuclear Information System (INIS)

Fried, David V.; Tucker, Susan L.; Zhou, Shouhao; Liao, Zhongxing; Mawlawi, Osama; Ibbott, Geoffrey; Court, Laurence E.

2014-01-01

Purpose: To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non-small cell lung cancer (NSCLC). Methods and Materials: We retrospectively reviewed 91 cases with stage III NSCLC treated with definitive chemoradiation therapy. All patients underwent pretreatment diagnostic contrast enhanced computed tomography (CE-CT) followed by 4-dimensional CT (4D-CT) for treatment simulation. We used the average-CT and expiratory (T50-CT) images from the 4D-CT along with the CE-CT for texture extraction. Histogram, gradient, co-occurrence, gray tone difference, and filtration-based techniques were used for texture feature extraction. Penalized Cox regression implementing cross-validation was used for covariate selection and modeling. Models incorporating texture features from the 33 image types and CPFs were compared to those with models incorporating CPFs alone for overall survival (OS), local-regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan-Meier curves were generated using leave-one-out cross-validation. Patients were stratified based on whether their predicted outcome was above or below the median. Reproducibility of texture features was evaluated using test-retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test-retest scans to our original models and determined the classification reproducibility. Results: Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone for OS (P=.046), LRC (P=.01), and FFDM (P=.005). The average CCCs were 0.89, 0.91, and 0.67 for texture features extracted from the average-CT, T50-CT, and CE-CT, respectively. Incorporating reproducibility within our models yielded 80.4% (±3.7% SD), 78.3% (±4.0% SD), and 78

Grey-scale conversion X-ray mapping by EDS of multielement and multiphase layered microstructures

DEFF Research Database (Denmark)

Dahl, Kristian Vinter; Hald, John; Horsewell, Andy

2007-01-01

been obtained for several long-term isothermal heat treatments in which significant interdiffusion has taken place. The resulting composition profiles have greatly improved counting statistics compared to traditional point-by-point scans for the same scanning electron microscope time and may......procedure for grey-scale conversion of energy dispersive spectroscopy X-ray maps has been developed, which is particularly useful for the plotting of line composition profiles across modified layered engineering surfaces. The method involves (a) the collection of grey-scale elemental maps, (b......, the procedure has been applied to a layered microstructure that results from a plasma-sprayed metallic MCrAlY coating onto a nickel-superalloy turbine blade. As a further demonstration of the accuracy and amount of compositional data that can be obtained with this procedure, measured compositional profiles have...

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

International Nuclear Information System (INIS)

Yang, F; Yang, Y; Young, L

2016-01-01

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

An improved high order texture features extraction method with application to pathological diagnosis of colon lesions for CT colonography

Science.gov (United States)

Song, Bowen; Zhang, Guopeng; Lu, Hongbing; Wang, Huafeng; Han, Fangfang; Zhu, Wei; Liang, Zhengrong

2014-03-01

Differentiation of colon lesions according to underlying pathology, e.g., neoplastic and non-neoplastic, is of fundamental importance for patient management. Image intensity based textural features have been recognized as a useful biomarker for the differentiation task. In this paper, we introduce high order texture features, beyond the intensity, such as gradient and curvature, for that task. Based on the Haralick texture analysis method, we introduce a virtual pathological method to explore the utility of texture features from high order differentiations, i.e., gradient and curvature, of the image intensity distribution. The texture features were validated on database consisting of 148 colon lesions, of which 35 are non-neoplastic lesions, using the random forest classifier and the merit of area under the curve (AUC) of the receiver operating characteristics. The results show that after applying the high order features, the AUC was improved from 0.8069 to 0.8544 in differentiating non-neoplastic lesion from neoplastic ones, e.g., hyperplastic polyps from tubular adenomas, tubulovillous adenomas and adenocarcinomas. The experimental results demonstrated that texture features from the higher order images can significantly improve the classification accuracy in pathological differentiation of colorectal lesions. The gain in differentiation capability shall increase the potential of computed tomography (CT) colonography for colorectal cancer screening by not only detecting polyps but also classifying them from optimal polyp management for the best outcome in personalized medicine.

Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection.

Science.gov (United States)

Li, Baopu; Meng, Max Q-H

2012-05-01

Tumor in digestive tract is a common disease and wireless capsule endoscopy (WCE) is a relatively new technology to examine diseases for digestive tract especially for small intestine. This paper addresses the problem of automatic recognition of tumor for WCE images. Candidate color texture feature that integrates uniform local binary pattern and wavelet is proposed to characterize WCE images. The proposed features are invariant to illumination change and describe multiresolution characteristics of WCE images. Two feature selection approaches based on support vector machine, sequential forward floating selection and recursive feature elimination, are further employed to refine the proposed features for improving the detection accuracy. Extensive experiments validate that the proposed computer-aided diagnosis system achieves a promising tumor recognition accuracy of 92.4% in WCE images on our collected data.

The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech

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Kun-Ching Wang

2014-09-01

Full Text Available In this paper, we present a novel texture image feature for Emotion Sensing in Speech (ESS. This idea is based on the fact that the texture images carry emotion-related information. The feature extraction is derived from time-frequency representation of spectrogram images. First, we transform the spectrogram as a recognizable image. Next, we use a cubic curve to enhance the image contrast. Then, the texture image information (TII derived from the spectrogram image can be extracted by using Laws’ masks to characterize emotional state. In order to evaluate the effectiveness of the proposed emotion recognition in different languages, we use two open emotional databases including the Berlin Emotional Speech Database (EMO-DB and eNTERFACE corpus and one self-recorded database (KHUSC-EmoDB, to evaluate the performance cross-corpora. The results of the proposed ESS system are presented using support vector machine (SVM as a classifier. Experimental results show that the proposed TII-based feature extraction inspired by visual perception can provide significant classification for ESS systems. The two-dimensional (2-D TII feature can provide the discrimination between different emotions in visual expressions except for the conveyance pitch and formant tracks. In addition, the de-noising in 2-D images can be more easily completed than de-noising in 1-D speech.

Computerized detection of diffuse lung disease in MDCT: the usefulness of statistical texture features

International Nuclear Information System (INIS)

Wang Jiahui; Li Qiang; Li Feng; Doi Kunio

2009-01-01

Accurate detection of diffuse lung disease is an important step for computerized diagnosis and quantification of this disease. It is also a difficult clinical task for radiologists. We developed a computerized scheme to assist radiologists in the detection of diffuse lung disease in multi-detector computed tomography (CT). Two radiologists selected 31 normal and 37 abnormal CT scans with ground glass opacity, reticular, honeycombing and nodular disease patterns based on clinical reports. The abnormal cases in our database must contain at least an abnormal area with a severity of moderate or severe level that was subjectively rated by the radiologists. Because statistical texture features may lack the power to distinguish a nodular pattern from a normal pattern, the abnormal cases that contain only a nodular pattern were excluded. The areas that included specific abnormal patterns in the selected CT images were then delineated as reference standards by an expert chest radiologist. The lungs were first segmented in each slice by use of a thresholding technique, and then divided into contiguous volumes of interest (VOIs) with a 64 x 64 x 64 matrix size. For each VOI, we determined and employed statistical texture features, such as run-length and co-occurrence matrix features, to distinguish abnormal from normal lung parenchyma. In particular, we developed new run-length texture features with clear physical meanings to considerably improve the accuracy of our detection scheme. A quadratic classifier was employed for distinguishing between normal and abnormal VOIs by the use of a leave-one-case-out validation scheme. A rule-based criterion was employed to further determine whether a case was normal or abnormal. We investigated the impact of new and conventional texture features, VOI size and the dimensionality for regions of interest on detecting diffuse lung disease. When we employed new texture features for 3D VOIs of 64 x 64 x 64 voxels, our system achieved the

Combination of radiological and gray level co-occurrence matrix textural features used to distinguish solitary pulmonary nodules by computed tomography.

Science.gov (United States)

Wu, Haifeng; Sun, Tao; Wang, Jingjing; Li, Xia; Wang, Wei; Huo, Da; Lv, Pingxin; He, Wen; Wang, Keyang; Guo, Xiuhua

2013-08-01

The objective of this study was to investigate the method of the combination of radiological and textural features for the differentiation of malignant from benign solitary pulmonary nodules by computed tomography. Features including 13 gray level co-occurrence matrix textural features and 12 radiological features were extracted from 2,117 CT slices, which came from 202 (116 malignant and 86 benign) patients. Lasso-type regularization to a nonlinear regression model was applied to select predictive features and a BP artificial neural network was used to build the diagnostic model. Eight radiological and two textural features were obtained after the Lasso-type regularization procedure. Twelve radiological features alone could reach an area under the ROC curve (AUC) of 0.84 in differentiating between malignant and benign lesions. The 10 selected characters improved the AUC to 0.91. The evaluation results showed that the method of selecting radiological and textural features appears to yield more effective in the distinction of malignant from benign solitary pulmonary nodules by computed tomography.

Ensemble based system for whole-slide prostate cancer probability mapping using color texture features.

LENUS (Irish Health Repository)

DiFranco, Matthew D

2011-01-01

We present a tile-based approach for producing clinically relevant probability maps of prostatic carcinoma in histological sections from radical prostatectomy. Our methodology incorporates ensemble learning for feature selection and classification on expert-annotated images. Random forest feature selection performed over varying training sets provides a subset of generalized CIEL*a*b* co-occurrence texture features, while sample selection strategies with minimal constraints reduce training data requirements to achieve reliable results. Ensembles of classifiers are built using expert-annotated tiles from training images, and scores for the probability of cancer presence are calculated from the responses of each classifier in the ensemble. Spatial filtering of tile-based texture features prior to classification results in increased heat-map coherence as well as AUC values of 95% using ensembles of either random forests or support vector machines. Our approach is designed for adaptation to different imaging modalities, image features, and histological decision domains.

Suitability of texture features to assess changes in trabecular bone architecture

DEFF Research Database (Denmark)

Veenland, JF; Grashuis, JL; Weinans, H

2002-01-01

The purpose of this study was to determine the ability of texture features to assess changes in trabecular bone architecture as projected in radiographs. Micro-CT datasets of trabecular bone were processed to simulate different changes in architecture. Radiographs were simulated by projecting the...

Textural features of the beach sediments of Wast Water Lake, Northwest England

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Bala Emilia

2016-06-01

Full Text Available This study is dedicated to Wast Water Lake (Northwest England, Great Britain and the character of its beach sediments. The aim of the study is to identify the textural features of the lake’s beach sediments based on two methods. The first is a granulometric analysis and the second a pebble shape analysis according to Zingg (1935 and Sneed & Folk (1958. Both analyses were carried out for all of the lake’s accessible beaches and the cliffs adjacent to them. The transport and deposition history of the examined sediments was identified through field research and laboratory analysis. The results show that the textural features of the sediments at Wast Water are more often typical of a fluvial environment, rather than having been changed by lacustrine water movements.

Can CT and MR Shape and Textural Features Differentiate Benign Versus Malignant Pleural Lesions?

Science.gov (United States)

Pena, Elena; Ojiaku, MacArinze; Inacio, Joao R; Gupta, Ashish; Macdonald, D Blair; Shabana, Wael; Seely, Jean M; Rybicki, Frank J; Dennie, Carole; Thornhill, Rebecca E

2017-10-01

The study aimed to identify a radiomic approach based on CT and or magnetic resonance (MR) features (shape and texture) that may help differentiate benign versus malignant pleural lesions, and to assess if the radiomic model may improve confidence and accuracy of radiologists with different subspecialty backgrounds. Twenty-nine patients with pleural lesions studied on both contrast-enhanced CT and MR imaging were reviewed retrospectively. Three texture and three shape features were extracted. Combinations of features were used to generate logistic regression models using histopathology as outcome. Two thoracic and two abdominal radiologists evaluated their degree of confidence in malignancy. Diagnostic accuracy of radiologists was determined using contingency tables. Cohen's kappa coefficient was used to assess inter-reader agreement. Using optimal threshold criteria, sensitivity, specificity, and accuracy of each feature and combination of features were obtained and compared to the accuracy and confidence of radiologists. The CT model that best discriminated malignant from benign lesions revealed an AUC CT  = 0.92 ± 0.05 (P textural and shape analysis may help distinguish malignant from benign lesions. A radiomics-based approach may increase diagnostic confidence of abdominal radiologists on CT and MR and may potentially improve radiologists' accuracy in the assessment of pleural lesions characterized by MR. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Identification of natural images and computer-generated graphics based on statistical and textural features.

Science.gov (United States)

Peng, Fei; Li, Jiao-ting; Long, Min

2015-03-01

To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer-generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer-generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer-generated graphics. © 2014 American Academy of Forensic Sciences.

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

Science.gov (United States)

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

2015-09-01

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

Effect of zooming on texture features of ultrasonic images

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Kyriacou Efthyvoulos

2006-01-01

Full Text Available Abstract Background Unstable carotid plaques on subjective, visual, assessment using B-mode ultrasound scanning appear as echolucent and heterogeneous. Although previous studies on computer assisted plaque characterisation have standardised B-mode images for brightness, improving the objective assessment of echolucency, little progress has been made towards standardisation of texture analysis methods, which assess plaque heterogeneity. The aim of the present study was to investigate the influence of image zooming during ultrasound scanning on textural features and to test whether or not resolution standardisation decreases the variability introduced. Methods Eighteen still B-mode images of carotid plaques were zoomed during carotid scanning (zoom factor 1.3 and both images were transferred to a PC and normalised. Using bilinear and bicubic interpolation, the original images were interpolated in a process of simulating off-line zoom using the same interpolation factor. With the aid of the colour-coded image, carotid plaques of the original, zoomed and two resampled images for each case were outlined and histogram, first order and second order statistics were subsequently calculated. Results Most second order statistics (21/25, 84% were significantly (p Conclusion Texture analysis of ultrasonic plaques should be performed under standardised resolution settings; otherwise a resolution normalisation algorithm should be applied.

Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

Directory of Open Access Journals (Sweden)

Minh-Tan Pham

2017-10-01

Full Text Available A novel efficient method for content-based image retrieval (CBIR is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteristic points (i.e., keypoints within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED is generated for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.

SEGMENTATION OF POLARIMETRIC SAR IMAGES USIG WAVELET TRANSFORMATION AND TEXTURE FEATURES

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A. Rezaeian

2015-12-01

Full Text Available Polarimetric Synthetic Aperture Radar (PolSAR sensors can collect useful observations from earth’s surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT. Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

Segmentation of Polarimetric SAR Images Usig Wavelet Transformation and Texture Features

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Rezaeian, A.; Homayouni, S.; Safari, A.

2015-12-01

Polarimetric Synthetic Aperture Radar (PolSAR) sensors can collect useful observations from earth's surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR) are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT). Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM) and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

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

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Cui, Chen; Asari, Vijayan K.

2014-03-01

Biometric features such as fingerprints, iris patterns, and face features help to identify people and restrict access to secure areas by performing advanced pattern analysis and matching. Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. However, the recognition results obtained by face recognition systems are a affected by several variations that may happen to the patterns in an unrestricted environment. As a result, several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition still remains as a difficult issue that needs to be resolved. In this paper, we propose a novel approach to tackling some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern (ELBP) description of all the local regions of the face. The relationship of each pixel with respect to its neighborhood is extracted and employed to calculate the new representation. ELBP reconstructs a much better textural feature extraction vector from an original gray level image in different lighting conditions. The dimensionality of the texture image is reduced by principal component analysis performed on each local face region. Each low dimensional vector representing a local region is now weighted based on the significance of the sub-region. The weight of each sub-region is determined by employing the local variance estimate of the respective region, which represents the significance of the region. The final facial textural feature vector is obtained by concatenating the reduced dimensional weight sets of all the modules (sub-regions) of the face image

Probability mapping of scarred myocardium using texture and intensity features in CMR images

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2013-01-01

Background The myocardium exhibits heterogeneous nature due to scarring after Myocardial Infarction (MI). In Cardiac Magnetic Resonance (CMR) imaging, Late Gadolinium (LG) contrast agent enhances the intensity of scarred area in the myocardium. Methods In this paper, we propose a probability mapping technique using Texture and Intensity features to describe heterogeneous nature of the scarred myocardium in Cardiac Magnetic Resonance (CMR) images after Myocardial Infarction (MI). Scarred tissue and non-scarred tissue are represented with high and low probabilities, respectively. Intermediate values possibly indicate areas where the scarred and healthy tissues are interwoven. The probability map of scarred myocardium is calculated by using a probability function based on Bayes rule. Any set of features can be used in the probability function. Results In the present study, we demonstrate the use of two different types of features. One is based on the mean intensity of pixel and the other on underlying texture information of the scarred and non-scarred myocardium. Examples of probability maps computed using the mean intensity of pixel and the underlying texture information are presented. We hypothesize that the probability mapping of myocardium offers alternate visualization, possibly showing the details with physiological significance difficult to detect visually in the original CMR image. Conclusion The probability mapping obtained from the two features provides a way to define different cardiac segments which offer a way to identify areas in the myocardium of diagnostic importance (like core and border areas in scarred myocardium). PMID:24053280

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

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

2013-05-01

The aim of this pilot study was to explore heterogeneity in the temporal behavior of intratumoral [(18)F]fluorodeoxyglucose (FDG) accumulation at a regional scale in patients with cervical cancer undergoing chemoradiotherapy. Included in the study were 20 patients with FIGO stages IB1 to IVA cervical cancer treated with combined chemoradiotherapy. Patients underwent FDG PET/CT before treatment, during weeks 2 and 4 of treatment, and 12 weeks after completion of therapy. Patients were classified based on response to therapy as showing a complete metabolic response (CMR), a partial metabolic response (PMR), or residual disease and the development of new disease (NEW). Based on the presence of residual primary tumor following therapy, patients were divided into two groups, CMR and PMR/NEW. Temporal profiles of intratumoral FDG heterogeneity as characterized by textural features at a regional scale were assessed and compared with those of the standardized uptake value (SUV) indices (SUVmax and SUVmean) within the context of differentiating response groups. Textural features at a regional scale with emphasis on characterizing contiguous regions of high uptake in tumors decreased significantly with time (P features describing contiguous regions of low uptake along with those measuring the nonuniformity of contiguous isointense regions in tumors exhibited significant temporal changes in the PMR/NEW group (P textural features may provide an adjunctive or alternative option for understanding tumor response to chemoradiotherapy and interpreting FDG accumulation dynamics in patients with malignant cervical tumors during the course of the disease.

Textural feature calculated from segmental fluences as a modulation index for VMAT.

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Park, So-Yeon; Park, Jong Min; Kim, Jung-In; Kim, Hyoungnyoun; Kim, Il Han; Ye, Sung-Joon

2015-12-01

Textural features calculated from various segmental fluences of volumetric modulated arc therapy (VMAT) plans were optimized to enhance its performance to predict plan delivery accuracy. Twenty prostate and twenty head and neck VMAT plans were selected retrospectively. Fluences were generated for each VMAT plan by summations of segments at sequential groups of control points. The numbers of summed segments were 5, 10, 20, 45, 90, 178 and 356. For each fluence, we investigated 6 textural features: angular second moment, inverse difference moment, contrast, variance, correlation and entropy (particular displacement distances, d = 1, 5 and 10). Spearman's rank correlation coefficients (rs) were calculated between each textural feature and several different measures of VMAT delivery accuracy. The values of rs of contrast (d = 10) with 10 segments to both global and local gamma passing rates with 2%/2 mm were 0.666 (p <0.001) and 0.573 (p <0.001), respectively. It showed rs values of -0.895 (p <0.001) and 0.727 (p <0.001) to multi-leaf collimator positional errors and gantry angle errors during delivery, respectively. The number of statistically significant rs values (p <0.05) to the changes in dose-volumetric parameters during delivery was 14 among a total of 35 tested parameters. Contrast (d = 10) with 10 segments showed higher correlations to the VMAT delivery accuracy than did the conventional modulation indices. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

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

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Kun-Ching Wang

2015-01-01

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

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

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Andrews, M; Abazeed, M; Woody, N; Stephans, K; Videtic, G; Xia, P; Zhuang, T [The Cleveland Clinic Foundation, Cleveland, OH (United States)

2016-06-15

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

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

International Nuclear Information System (INIS)

Andrews, M; Abazeed, M; Woody, N; Stephans, K; Videtic, G; Xia, P; Zhuang, T

2016-01-01

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

SU-D-BRA-06: Dual-Energy Chest CT: The Effects of Virtual Monochromatic Reconstructions On Texture Analysis Features

International Nuclear Information System (INIS)

Sorensen, J; Duran, C; Stingo, F; Wei, W; Rao, A; Zhang, L; Court, L; Erasmus, J; Godoy, M

2015-01-01

Purpose: To characterize the effect of virtual monochromatic reconstructions on several commonly used texture analysis features in DECT of the chest. Further, to assess the effect of monochromatic energy levels on the ability of these textural features to identify tissue types. Methods: 20 consecutive patients underwent chest CTs for evaluation of lung nodules using Siemens Somatom Definition Flash DECT. Virtual monochromatic images were constructed at 10keV intervals from 40–190keV. For each patient, an ROI delineated the lesion under investigation, and cylindrical ROI’s were placed within 5 different healthy tissues (blood, fat, muscle, lung, and liver). Several histogram- and Grey Level Cooccurrence Matrix (GLCM)-based texture features were then evaluated in each ROI at each energy level. As a means of validation, these feature values were then used in a random forest classifier to attempt to identify the tissue types present within each ROI. Their predictive accuracy at each energy level was recorded. Results: All textural features changed considerably with virtual monochromatic energy, particularly below 70keV. Most features exhibited a global minimum or maximum around 80keV, and while feature values changed with energy above this, patient ranking was generally unaffected. As expected, blood demonstrated the lowest inter-patient variability, for all features, while lung lesions (encompassing many different pathologies) exhibited the highest. The accuracy of these features in identifying tissues (76% accuracy) was highest at 80keV, but no clear relationship between energy and classification accuracy was found. Two common misclassifications (blood vs liver and muscle vs fat) accounted for the majority (24 of the 28) errors observed. Conclusion: All textural features were highly dependent on virtual monochromatic energy level, especially below 80keV, and were more stable above this energy. However, in a random forest model, these commonly used features were

SU-D-BRA-06: Dual-Energy Chest CT: The Effects of Virtual Monochromatic Reconstructions On Texture Analysis Features

Energy Technology Data Exchange (ETDEWEB)

Sorensen, J; Duran, C; Stingo, F; Wei, W; Rao, A; Zhang, L; Court, L; Erasmus, J; Godoy, M [UT MD Anderson Cancer Center, Houston, TX (United States)

2015-06-15

Purpose: To characterize the effect of virtual monochromatic reconstructions on several commonly used texture analysis features in DECT of the chest. Further, to assess the effect of monochromatic energy levels on the ability of these textural features to identify tissue types. Methods: 20 consecutive patients underwent chest CTs for evaluation of lung nodules using Siemens Somatom Definition Flash DECT. Virtual monochromatic images were constructed at 10keV intervals from 40–190keV. For each patient, an ROI delineated the lesion under investigation, and cylindrical ROI’s were placed within 5 different healthy tissues (blood, fat, muscle, lung, and liver). Several histogram- and Grey Level Cooccurrence Matrix (GLCM)-based texture features were then evaluated in each ROI at each energy level. As a means of validation, these feature values were then used in a random forest classifier to attempt to identify the tissue types present within each ROI. Their predictive accuracy at each energy level was recorded. Results: All textural features changed considerably with virtual monochromatic energy, particularly below 70keV. Most features exhibited a global minimum or maximum around 80keV, and while feature values changed with energy above this, patient ranking was generally unaffected. As expected, blood demonstrated the lowest inter-patient variability, for all features, while lung lesions (encompassing many different pathologies) exhibited the highest. The accuracy of these features in identifying tissues (76% accuracy) was highest at 80keV, but no clear relationship between energy and classification accuracy was found. Two common misclassifications (blood vs liver and muscle vs fat) accounted for the majority (24 of the 28) errors observed. Conclusion: All textural features were highly dependent on virtual monochromatic energy level, especially below 80keV, and were more stable above this energy. However, in a random forest model, these commonly used features were

Personal recognition using finger knuckle shape oriented features and texture analysis

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K. Usha

2016-10-01

Full Text Available Finger knuckle print is considered as one of the emerging hand biometric traits due to its potentiality toward the identification of individuals. This paper contributes a new method for personal recognition using finger knuckle print based on two approaches namely, geometric and texture analyses. In the first approach, the shape oriented features of the finger knuckle print are extracted by means of angular geometric analysis and then integrated to achieve better precision rate. Whereas, the knuckle texture feature analysis is carried out by means of multi-resolution transform known as Curvelet transform. This Curvelet transform has the ability to approximate curved singularities with minimum number of Curvelet coefficients. Since, finger knuckle patterns mainly consist of lines and curves, Curvelet transform is highly suitable for its representation. Further, the Curvelet transform decomposes the finger knuckle image into Curvelet sub-bands which are termed as ‘Curvelet knuckle’. Finally, principle component analysis is applied on each Curvelet knuckle for extracting its feature vector through the covariance matrix derived from their Curvelet coefficients. Extensive experiments were conducted using PolyU database and IIT finger knuckle database. The experimental results confirm that, our proposed method shows a high recognition rate of 98.72% with lower false acceptance rate of 0.06%.

Differentiating benign from malignant solid breast masses: value of shear wave elastography according to lesion stiffness combined with greyscale ultrasound according to BI-RADS classification.

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Evans, A; Whelehan, P; Thomson, K; Brauer, K; Jordan, L; Purdie, C; McLean, D; Baker, L; Vinnicombe, S; Thompson, A

2012-07-10

The aim of this study was to assess the performance of shear wave elastography combined with BI-RADS classification of greyscale ultrasound images for benign/malignant differentiation in a large group of patients. One hundred and seventy-five consecutive patients with solid breast masses on routine ultrasonography undergoing percutaneous biopsy had the greyscale findings classified according to the American College of Radiology BI-RADS. The mean elasticity values from four shear wave images were obtained. For mean elasticity vs greyscale BI-RADS, the performance results against histology were sensitivity: 95% vs 95%, specificity: 77% vs 69%, Positive Predictive Value (PPV): 88% vs 84%, Negative Predictive Value (NPV): 90% vs 91%, and accuracy: 89% vs 86% (all P>0.05). The results for the combination (positive result from either modality counted as malignant) were sensitivity 100%, specificity 61%, PPV 82%, NPV 100%, and accuracy 86%. The combination of BI-RADS greyscale and shear wave elastography yielded superior sensitivity to BI-RADS alone (P=0.03) or shear wave alone (P=0.03). The NPV was superior in combination compared with either alone (BI-RADS P=0.01 and shear wave P=0.02). Together, BI-RADS assessment of greyscale ultrasound images and shear wave ultrasound elastography are extremely sensitive for detection of malignancy.

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

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

2012-09-01

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

Military personnel recognition system using texture, colour, and SURF features

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Irhebhude, Martins E.; Edirisinghe, Eran A.

2014-06-01

This paper presents an automatic, machine vision based, military personnel identification and classification system. Classification is done using a Support Vector Machine (SVM) on sets of Army, Air Force and Navy camouflage uniform personnel datasets. In the proposed system, the arm of service of personnel is recognised by the camouflage of a persons uniform, type of cap and the type of badge/logo. The detailed analysis done include; camouflage cap and plain cap differentiation using gray level co-occurrence matrix (GLCM) texture feature; classification on Army, Air Force and Navy camouflaged uniforms using GLCM texture and colour histogram bin features; plain cap badge classification into Army, Air Force and Navy using Speed Up Robust Feature (SURF). The proposed method recognised camouflage personnel arm of service on sets of data retrieved from google images and selected military websites. Correlation-based Feature Selection (CFS) was used to improve recognition and reduce dimensionality, thereby speeding the classification process. With this method success rates recorded during the analysis include 93.8% for camouflage appearance category, 100%, 90% and 100% rates of plain cap and camouflage cap categories for Army, Air Force and Navy categories, respectively. Accurate recognition was recorded using SURF for the plain cap badge category. Substantial analysis has been carried out and results prove that the proposed method can correctly classify military personnel into various arms of service. We show that the proposed method can be integrated into a face recognition system, which will recognise personnel in addition to determining the arm of service which the personnel belong. Such a system can be used to enhance the security of a military base or facility.

Grey-scale and colour Doppler ultrasound versus magnetic resonance imaging for the prenatal diagnosis of placenta accreta.

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Rezk, Mohamed Abd-Allah; Shawky, Mohamed

2016-01-01

To assess the effectiveness of grey-scale and colour Doppler ultrasound (US) versus magnetic resonance imaging (MRI) for the prenatal diagnosis of placenta accreta. A prospective observational study including a total of 74 patients with placenta previa and previous uterine scar (n = 74). Grey-scale and colour Doppler US was done followed by MRI by different observers to diagnose adherent placenta. Test validity of US and MRI were calculated. Maternal morbidity and mortality were also assessed. A total of 53 patients confirmed to have placenta accreta at operation. The overall sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of US was 94.34, 91.67, 96.15 and 88% compared to 96.08, 87.50, 94.23 and 91.3% for MRI, respectively. The most relevant US sign was turbulent blood flow by colour Doppler, while dark intra-placental band was the most sensitive MRI sign. Venous thromboembolism (1.3%), bladder injury (29.7%), ureteric injury (18.9%), postoperative fever (10.8%), admission to ICU (50%) and re-operation (31.1%). Placenta accreta can be successfully diagnosed by grey-scale and colour Doppler US. MRI would be more likely suggested for either posteriorly or laterally situated placenta previa in order to exclude placental invasion.

Feature Extraction of Weld Defectology in Digital Image of Radiographic Film Using Geometric Invariant Moment and Statistical Texture

International Nuclear Information System (INIS)

Muhtadan

2009-01-01

The purpose of this research is to perform feature extraction in weld defect of digital image of radiographic film using geometric invariant moment and statistical texture method. Feature extraction values can be use as values that used to classify and pattern recognition on interpretation of weld defect in digital image of radiographic film by computer automatically. Weld defectology type that used in this research are longitudinal crack, transversal crack, distributed porosity, clustered porosity, wormhole, and no defect. Research methodology on this research are program development to read digital image, then performing image cropping to localize weld position, and then applying geometric invariant moment and statistical texture formulas to find feature values. The result of this research are feature extraction values that have tested with RST (rotation, scale, transformation) treatment and yield moment values that more invariant there are ϕ 3 , ϕ 4 , ϕ 5 from geometric invariant moment method. Feature values from statistical texture that are average intensity, average contrast, smoothness, 3 rd moment, uniformity, and entropy, they used as feature extraction values. (author)

Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach.

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Irshad, Humayun; Jalali, Sepehr; Roux, Ludovic; Racoceanu, Daniel; Hwee, Lim Joo; Naour, Gilles Le; Capron, Frédérique

2013-01-01

According to Nottingham grading system, mitosis count in breast cancer histopathology is one of three components required for cancer grading and prognosis. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. The aim is to investigate the various texture features and Hierarchical Model and X (HMAX) biologically inspired approach for mitosis detection using machine-learning techniques. We propose an approach that assists pathologists in automated mitosis detection and counting. The proposed method, which is based on the most favorable texture features combination, examines the separability between different channels of color space. Blue-ratio channel provides more discriminative information for mitosis detection in histopathological images. Co-occurrence features, run-length features, and Scale-invariant feature transform (SIFT) features were extracted and used in the classification of mitosis. Finally, a classification is performed to put the candidate patch either in the mitosis class or in the non-mitosis class. Three different classifiers have been evaluated: Decision tree, linear kernel Support Vector Machine (SVM), and non-linear kernel SVM. We also evaluate the performance of the proposed framework using the modified biologically inspired model of HMAX and compare the results with other feature extraction methods such as dense SIFT. The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS) dataset provided for an International Conference on Pattern Recognition (ICPR) 2012 contest. The proposed framework achieved 76% recall, 75% precision and 76% F-measure. Different frameworks for classification have been evaluated for mitosis detection. In future work, instead of regions, we intend to compute features on the results of mitosis contour segmentation and use them to improve detection and classification rate.

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

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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: 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.

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

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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.

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

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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.

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

Science.gov (United States)

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.

Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach

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Humayun Irshad

2013-01-01

Full Text Available Context: According to Nottingham grading system, mitosis count in breast cancer histopathology is one of three components required for cancer grading and prognosis. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Aims: The aim is to investigate the various texture features and Hierarchical Model and X (HMAX biologically inspired approach for mitosis detection using machine-learning techniques. Materials and Methods: We propose an approach that assists pathologists in automated mitosis detection and counting. The proposed method, which is based on the most favorable texture features combination, examines the separability between different channels of color space. Blue-ratio channel provides more discriminative information for mitosis detection in histopathological images. Co-occurrence features, run-length features, and Scale-invariant feature transform (SIFT features were extracted and used in the classification of mitosis. Finally, a classification is performed to put the candidate patch either in the mitosis class or in the non-mitosis class. Three different classifiers have been evaluated: Decision tree, linear kernel Support Vector Machine (SVM, and non-linear kernel SVM. We also evaluate the performance of the proposed framework using the modified biologically inspired model of HMAX and compare the results with other feature extraction methods such as dense SIFT. Results: The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS dataset provided for an International Conference on Pattern Recognition (ICPR 2012 contest. The proposed framework achieved 76% recall, 75% precision and 76% F-measure. Conclusions: Different frameworks for classification have been evaluated for mitosis detection. In future work, instead of regions, we intend to compute features on the results of mitosis contour segmentation and use them to improve detection and

Screening Mississippi River Levees Using Texture-Based and Polarimetric-Based Features from Synthetic Aperture Radar Data

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Lalitha Dabbiru

2017-03-01

Full Text Available This article reviews the use of synthetic aperture radar remote sensing data for earthen levee mapping with an emphasis on finding the slump slides on the levees. Earthen levees built on the natural levees parallel to the river channel are designed to protect large areas of populated and cultivated land in the Unites States from flooding. One of the signs of potential impending levee failure is the appearance of slump slides. On-site inspection of levees is expensive and time-consuming; therefore, a need to develop efficient techniques based on remote sensing technologies is mandatory to prevent failures under flood loading. Analysis of multi-polarized radar data is one of the viable tools for detecting the problem areas on the levees. In this study, we develop methods to detect anomalies on the levee, such as slump slides and give levee managers new tools to prioritize their tasks. This paper presents results of applying the National Aeronautics and Space Administration (NASA Jet Propulsion Lab (JPL’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR quad-polarized L-band data to detect slump slides on earthen levees. The study area encompasses a portion of levees of the lower Mississippi River in the United States. In this paper, we investigate the performance of polarimetric and texture features for efficient levee classification. Texture features derived from the gray level co-occurrence (GLCM matrix and discrete wavelet transform were computed and analyzed for efficient levee classification. The pixel-based polarimetric decomposition features, such as entropy, anisotropy, and scattering angle were also computed and applied to the support vector machine classifier to characterize the radar imagery and compared the results with texture-based classification. Our experimental results showed that inclusion of textural features derived from the SAR data using the discrete wavelet transform (DWT features and GLCM features provided

Game theory-based visual tracking approach focusing on color and texture features.

Science.gov (United States)

Jin, Zefenfen; Hou, Zhiqiang; Yu, Wangsheng; Chen, Chuanhua; Wang, Xin

2017-07-20

It is difficult for a single-feature tracking algorithm to achieve strong robustness under a complex environment. To solve this problem, we proposed a multifeature fusion tracking algorithm that is based on game theory. By focusing on color and texture features as two gamers, this algorithm accomplishes tracking by using a mean shift iterative formula to search for the Nash equilibrium of the game. The contribution of different features is always keeping the state of optical balance, so that the algorithm can fully take advantage of feature fusion. According to the experiment results, this algorithm proves to possess good performance, especially under the condition of scene variation, target occlusion, and similar interference.

Automatic system for radar echoes filtering based on textural features and artificial intelligence

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Hedir, Mehdia; Haddad, Boualem

2017-10-01

Among the very popular Artificial Intelligence (AI) techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been retained to process Ground Echoes (GE) on meteorological radar images taken from Setif (Algeria) and Bordeaux (France) with different climates and topologies. To achieve this task, AI techniques were associated with textural approaches. We used Gray Level Co-occurrence Matrix (GLCM) and Completed Local Binary Pattern (CLBP); both methods were largely used in image analysis. The obtained results show the efficiency of texture to preserve precipitations forecast on both sites with the accuracy of 98% on Bordeaux and 95% on Setif despite the AI technique used. 98% of GE are suppressed with SVM, this rate is outperforming ANN skills. CLBP approach associated to SVM eliminates 98% of GE and preserves precipitations forecast on Bordeaux site better than on Setif's, while it exhibits lower accuracy with ANN. SVM classifier is well adapted to the proposed application since the average filtering rate is 95-98% with texture and 92-93% with CLBP. These approaches allow removing Anomalous Propagations (APs) too with a better accuracy of 97.15% with texture and SVM. In fact, textural features associated to AI techniques are an efficient tool for incoherent radars to surpass spurious echoes.

Aesthetic Perception of Visual Textures: A Holistic Exploration using Texture Analysis, Psychological Experiment and Perception Modeling

Directory of Open Access Journals (Sweden)

Jianli eLiu

2015-11-01

Full Text Available 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 nonlinear 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.

Histogram analysis of greyscale sonograms to differentiate between the subtypes of follicular variant of papillary thyroid cancer.

Science.gov (United States)

Kwon, M-R; Shin, J H; Hahn, S Y; Oh, Y L; Kwak, J Y; Lee, E; Lim, Y

2018-06-01

To evaluate the diagnostic value of histogram analysis using ultrasound (US) to differentiate between the subtypes of follicular variant of papillary thyroid carcinoma (FVPTC). The present study included 151 patients with surgically confirmed FVPTC diagnosed between January 2014 and May 2016. Their preoperative US features were reviewed retrospectively. Histogram parameters (mean, maximum, minimum, range, root mean square, skewness, kurtosis, energy, entropy, and correlation) were obtained for each nodule. The 152 nodules in 151 patients comprised 48 non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTPs; 31.6%), 60 invasive encapsulated FVPTCs (EFVPTCs; 39.5%), and 44 infiltrative FVPTCs (28.9%). The US features differed significantly between the subtypes of FVPTC. Discrimination was achieved between NIFTPs and infiltrative FVPTC, and between invasive EFVPTC and infiltrative FVPTC using histogram parameters; however, the parameters were not significantly different between NIFTP and invasive EFVPTC. It is feasible to use greyscale histogram analysis to differentiate between NIFTP and infiltrative FVPTC, but not between NIFTP and invasive EFVPTC. Histograms can be used as a supplementary tool to differentiate the subtypes of FVPTC. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Breast density pattern characterization by histogram features and texture descriptors

OpenAIRE

Carneiro,Pedro Cunha; Franco,Marcelo Lemos Nunes; Thomaz,Ricardo de Lima; Patrocinio,Ana Claudia

2017-01-01

Abstract Introduction Breast cancer is the first leading cause of death for women in Brazil as well as in most countries in the world. Due to the relation between the breast density and the risk of breast cancer, in medical practice, the breast density classification is merely visual and dependent on professional experience, making this task very subjective. The purpose of this paper is to investigate image features based on histograms and Haralick texture descriptors so as to separate mammo...

Dynamic crystallization of a eucrite basalt. [achondrite textural features produced by superheating and differing cooling rates

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Walker, D.; Powell, M. A.; Hays, J. F.; Lofgren, G. E.

1978-01-01

The textural features produced in Stannern, a non-porpyritic representative of the eucrite basaltic achondrite class of meteorite, at differing cooling rates and various degrees of initial superheating were studied. Textures produced from mildly superheated melts were found to be fasciculate rather than porphyritic as the result of the cosaturated bulk chemistry of Stannern. The qualitative type of texture apparently depends mainly on the degree of initial superheating, whereas cooling rate exerts a strong influence on the coarseness of texture. Increasing the degree of superheating produces textures from intergranular/subophitic to fasciculate/porphyritic. With initial superheating to 1200 deg C the transition to quasi-porphyritic is controlled by cooling rate, but the development of phenocrysts is merely an overprint on the fasciculate background texture of the groundmass. The suppression of fasciculate texture is completed by a decrease of the degree of initial superheating below the plagioclast entry and suppression of quasi-porphyritic texture is completed by decrease of the degree of initial superheating below pyroxene entry; these qualitative changes do not seem to be produced by changes of cooling rate. A grain size/cooling rate dependence has been used to deduce the cooling rate of fasciculate-textured Stannern clasts (10.1 to 100 deg C/hr).

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

NARCIS (Netherlands)

Nketiah, G.; Elschot, M.; Kim, E.; Teruel, J.R.; Scheenen, T.W.J.; Bathen, T.F.; Selnaes, K.M.

2017-01-01

PURPOSE: 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. MATERIALS AND METHODS:

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

International Nuclear Information System (INIS)

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

2012-01-01

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

Facial expression recognition in the wild based on multimodal texture features

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Sun, Bo; Li, Liandong; Zhou, Guoyan; He, Jun

2016-11-01

Facial expression recognition in the wild is a very challenging task. We describe our work in static and continuous facial expression recognition in the wild. We evaluate the recognition results of gray deep features and color deep features, and explore the fusion of multimodal texture features. For the continuous facial expression recognition, we design two temporal-spatial dense scale-invariant feature transform (SIFT) features and combine multimodal features to recognize expression from image sequences. For the static facial expression recognition based on video frames, we extract dense SIFT and some deep convolutional neural network (CNN) features, including our proposed CNN architecture. We train linear support vector machine and partial least squares classifiers for those kinds of features on the static facial expression in the wild (SFEW) and acted facial expression in the wild (AFEW) dataset, and we propose a fusion network to combine all the extracted features at decision level. The final achievement we gained is 56.32% on the SFEW testing set and 50.67% on the AFEW validation set, which are much better than the baseline recognition rates of 35.96% and 36.08%.

Features of the normal choriocapillaris with OCT-angiography: Density estimation and textural properties.

Science.gov (United States)

Montesano, Giovanni; Allegrini, Davide; Colombo, Leonardo; Rossetti, Luca M; Pece, Alfredo

2017-01-01

The main objective of our work is to perform an in depth analysis of the structural features of normal choriocapillaris imaged with OCT Angiography. Specifically, we provide an optimal radius for a circular Region of Interest (ROI) to obtain a stable estimate of the subfoveal choriocapillaris density and characterize its textural properties using Markov Random Fields. On each binarized image of the choriocapillaris OCT Angiography we performed simulated measurements of the subfoveal choriocapillaris densities with circular Regions of Interest (ROIs) of different radii and with small random displacements from the center of the Foveal Avascular Zone (FAZ). We then calculated the variability of the density measure with different ROI radii. We then characterized the textural features of choriocapillaris binary images by estimating the parameters of an Ising model. For each image we calculated the Optimal Radius (OR) as the minimum ROI radius required to obtain a standard deviation in the simulation below 0.01. The density measured with the individual OR was 0.52 ± 0.07 (mean ± STD). Similar density values (0.51 ± 0.07) were obtained using a fixed ROI radius of 450 μm. The Ising model yielded two parameter estimates (β = 0.34 ± 0.03; γ = 0.003 ± 0.012; mean ± STD), characterizing pixel clustering and white pixel density respectively. Using the estimated parameters to synthetize new random textures via simulation we obtained a good reproduction of the original choriocapillaris structural features and density. In conclusion, we developed an extensive characterization of the normal subfoveal choriocapillaris that might be used for flow analysis and applied to the investigation pathological alterations.

TU-F-CAMPUS-J-04: Impact of Voxel Anisotropy On Statistic Texture Features of Oncologic PET: A Simulation Study

International Nuclear Information System (INIS)

Yang, F; Byrd, D; Bowen, S; Kinahan, P; Sandison, G

2015-01-01

Purpose: Texture metrics extracted from oncologic PET have been investigated with respect to their usefulness as definitive indicants for prognosis in a variety of cancer. Metric calculation is often based on cubic voxels. Most commonly used PET scanners, however, produce rectangular voxels, which may change texture metrics. The objective of this study was to examine the variability of PET texture feature metrics resulting from voxel anisotropy. Methods: Sinograms of NEMA NU-2 phantom for 18F-FDG were simulated using the ASIM simulation tool. The obtained projection data was reconstructed (3D-OSEM) on grids of cubic and rectangular voxels, producing PET images of resolution of 2.73x2.73x3.27mm3 and 3.27x3.27x3.27mm3, respectively. An interpolated dataset obtained from resampling the rectangular voxel data for isotropic voxel dimension (3.27mm) was also considered. For each image dataset, 28 texture parameters based on grey-level co-occurrence matrices (GLCOM), intensity histograms (GLIH), neighborhood difference matrices (GLNDM), and zone size matrices (GLZSM) were evaluated within lesions of diameter of 33, 28, 22, and 17mm. Results: In reference to the isotopic image data, texture features appearing on the rectangular voxel data varied with a range of -34-10% for GLCOM based, -31-39% for GLIH based, -80 -161% for GLNDM based, and −6–45% for GLZSM based while varied with a range of -35-23% for GLCOM based, -27-35% for GLIH based, -65-86% for GLNDM based, and -22 -18% for GLZSM based for the interpolated image data. For the anisotropic data, GLNDM-cplx exhibited the largest extent of variation (161%) while GLZSM-zp showed the least (<1%). As to the interpolated data, GLNDM-busy varied the most (86%) while GLIH-engy varied the least (<1%). Conclusion: Variability of texture appearance on oncologic PET with respect to voxel representation is substantial and feature-dependent. It necessitates consideration of standardized voxel representation for inter

Shape-Tailored Features and their Application to Texture Segmentation

KAUST Repository

Khan, Naeemullah

2014-01-01

Texture Segmentation is one of the most challenging areas of computer vision. One reason for this difficulty is the huge variety and variability of textures occurring in real world, making it very difficult to quantitatively study textures. One

Evaluation of Shape and Textural Features from CT as Prognostic Biomarkers in Non-small Cell Lung Cancer.

Science.gov (United States)

Bianconi, Francesco; Fravolini, Mario Luca; Bello-Cerezo, Raquel; Minestrini, Matteo; Scialpi, Michele; Palumbo, Barbara

2018-04-01

We retrospectively investigated the prognostic potential (correlation with overall survival) of 9 shape and 21 textural features from non-contrast-enhanced computed tomography (CT) in patients with non-small-cell lung cancer. We considered a public dataset of 203 individuals with inoperable, histologically- or cytologically-confirmed NSCLC. Three-dimensional shape and textural features from CT were computed using proprietary code and their prognostic potential evaluated through four different statistical protocols. Volume and grey-level run length matrix (GLRLM) run length non-uniformity were the only two features to pass all four protocols. Both features correlated negatively with overall survival. The results also showed a strong dependence on the evaluation protocol used. Tumour volume and GLRLM run-length non-uniformity from CT were the best predictor of survival in patients with non-small-cell lung cancer. We did not find enough evidence to claim a relationship with survival for the other features. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Haralick textural features on T2 -weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer.

Science.gov (United States)

Gnep, Khémara; Fargeas, Auréline; Gutiérrez-Carvajal, Ricardo E; Commandeur, Frédéric; Mathieu, Romain; Ospina, Juan D; Rolland, Yan; Rohou, Tanguy; Vincendeau, Sébastien; Hatt, Mathieu; Acosta, Oscar; de Crevoisier, Renaud

2017-01-01

To explore the association between magnetic resonance imaging (MRI), including Haralick textural features, and biochemical recurrence following prostate cancer radiotherapy. In all, 74 patients with peripheral zone localized prostate adenocarcinoma underwent pretreatment 3.0T MRI before external beam radiotherapy. Median follow-up of 47 months revealed 11 patients with biochemical recurrence. Prostate tumors were segmented on T 2 -weighted sequences (T 2 -w) and contours were propagated onto the coregistered apparent diffusion coefficient (ADC) images. We extracted 140 image features from normalized T 2 -w and ADC images corresponding to first-order (n = 6), gradient-based (n = 4), and second-order Haralick textural features (n = 130). Four geometrical features (tumor diameter, perimeter, area, and volume) were also computed. Correlations between Gleason score and MRI features were assessed. Cox regression analysis and random survival forests (RSF) were performed to assess the association between MRI features and biochemical recurrence. Three T 2 -w and one ADC Haralick textural features were significantly correlated with Gleason score (P recurrence (P recurrence following prostate cancer radiotherapy. 3 J. Magn. Reson. Imaging 2017;45:103-117. © 2016 International Society for Magnetic Resonance in Medicine.

Geometric Total Variation for Texture Deformation

DEFF Research Database (Denmark)

Bespalov, Dmitriy; Dahl, Anders Lindbjerg; Shokoufandeh, Ali

2010-01-01

In this work we propose a novel variational method that we intend to use for estimating non-rigid texture deformation. The method is able to capture variation in grayscale images with respect to the geometry of its features. Our experimental evaluations demonstrate that accounting for geometry...... of features in texture images leads to significant improvements in localization of these features, when textures undergo geometrical transformations. Accurate localization of features in the presense of unkown deformations is a crucial property for texture characterization methods, and we intend to expoit...

Textural features of 18F-fluorodeoxyglucose positron emission tomography scanning in diagnosing aortic prosthetic graft infection.

Science.gov (United States)

Saleem, Ben R; Beukinga, Roelof J; Boellaard, Ronald; Glaudemans, Andor W J M; Reijnen, Michel M P J; Zeebregts, Clark J; Slart, Riemer H J A

2017-05-01

The clinical problem in suspected aortoiliac graft infection (AGI) is to obtain proof of infection. Although 18 F-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography scanning (PET) has been suggested to play a pivotal role, an evidence-based interpretation is lacking. The objective of this retrospective study was to examine the feasibility and utility of 18 F-FDG uptake heterogeneity characterized by textural features to diagnose AGI. Thirty patients with a history of aortic graft reconstruction who underwent 18 F-FDG PET/CT scanning were included. Sixteen patients were suspected to have an AGI (group I). AGI was considered proven only in the case of a positive bacterial culture. Positive cultures were found in 10 of the 16 patients (group Ia), and in the other six patients, cultures remained negative (group Ib). A control group was formed of 14 patients undergoing 18 F-FDG PET for other reasons (group II). PET images were assessed using conventional maximal standardized uptake value (SUVmax), tissue-to-background ratio (TBR), and visual grading scale (VGS). Additionally, 64 different 18 F-FDG PET based textural features were applied to characterize 18 F-FDG uptake heterogeneity. To select candidate predictors, univariable logistic regression analysis was performed (α = 0.16). The accuracy was satisfactory in case of an AUC > 0.8. The feature selection process yielded the textural features named variance (AUC = 0.88), high grey level zone emphasis (AUC = 0.87), small zone low grey level emphasis (AUC = 0.80), and small zone high grey level emphasis (AUC = 0.81) most optimal for distinguishing between groups I and II. SUVmax, TBR, and VGS were also able to distinguish between these groups with AUCs of 0.87, 0.78, and 0.90, respectively. The textural feature named short run high grey level emphasis was able to distinguish group Ia from Ib (AUC = 0.83), while for the same task the TBR and VGS were not found to be predictive. SUVmax

False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review.

Directory of Open Access Journals (Sweden)

Anastasia Chalkidou

Full Text Available A number of recent publications have proposed that a family of image-derived indices, called texture features, can predict clinical outcome in patients with cancer. However, the investigation of multiple indices on a single data set can lead to significant inflation of type-I errors. We report a systematic review of the type-I error inflation in such studies and review the evidence regarding associations between patient outcome and texture features derived from positron emission tomography (PET or computed tomography (CT images.For study identification PubMed and Scopus were searched (1/2000-9/2013 using combinations of the keywords texture, prognostic, predictive and cancer. Studies were divided into three categories according to the sources of the type-I error inflation and the use or not of an independent validation dataset. For each study, the true type-I error probability and the adjusted level of significance were estimated using the optimum cut-off approach correction, and the Benjamini-Hochberg method. To demonstrate explicitly the variable selection bias in these studies, we re-analyzed data from one of the published studies, but using 100 random variables substituted for the original image-derived indices. The significance of the random variables as potential predictors of outcome was examined using the analysis methods used in the identified studies.Fifteen studies were identified. After applying appropriate statistical corrections, an average type-I error probability of 76% (range: 34-99% was estimated with the majority of published results not reaching statistical significance. Only 3/15 studies used a validation dataset. For the 100 random variables examined, 10% proved to be significant predictors of survival when subjected to ROC and multiple hypothesis testing analysis.We found insufficient evidence to support a relationship between PET or CT texture features and patient survival. Further fit for purpose validation of these

False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review.

Science.gov (United States)

Chalkidou, Anastasia; O'Doherty, Michael J; Marsden, Paul K

2015-01-01

A number of recent publications have proposed that a family of image-derived indices, called texture features, can predict clinical outcome in patients with cancer. However, the investigation of multiple indices on a single data set can lead to significant inflation of type-I errors. We report a systematic review of the type-I error inflation in such studies and review the evidence regarding associations between patient outcome and texture features derived from positron emission tomography (PET) or computed tomography (CT) images. For study identification PubMed and Scopus were searched (1/2000-9/2013) using combinations of the keywords texture, prognostic, predictive and cancer. Studies were divided into three categories according to the sources of the type-I error inflation and the use or not of an independent validation dataset. For each study, the true type-I error probability and the adjusted level of significance were estimated using the optimum cut-off approach correction, and the Benjamini-Hochberg method. To demonstrate explicitly the variable selection bias in these studies, we re-analyzed data from one of the published studies, but using 100 random variables substituted for the original image-derived indices. The significance of the random variables as potential predictors of outcome was examined using the analysis methods used in the identified studies. Fifteen studies were identified. After applying appropriate statistical corrections, an average type-I error probability of 76% (range: 34-99%) was estimated with the majority of published results not reaching statistical significance. Only 3/15 studies used a validation dataset. For the 100 random variables examined, 10% proved to be significant predictors of survival when subjected to ROC and multiple hypothesis testing analysis. We found insufficient evidence to support a relationship between PET or CT texture features and patient survival. Further fit for purpose validation of these image

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

Directory of Open Access Journals (Sweden)

Lakshmanan Sannachi

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

COMPUTER GRAPHICS MEETS IMAGE FUSION: THE POWER OF TEXTURE BAKING TO SIMULTANEOUSLY VISUALISE 3D SURFACE FEATURES AND COLOUR

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G. J. Verhoeven

2017-08-01

Full Text Available Since a few years, structure-from-motion and multi-view stereo pipelines have become omnipresent in the cultural heritage domain. The fact that such Image-Based Modelling (IBM approaches are capable of providing a photo-realistic texture along the threedimensional (3D digital surface geometry is often considered a unique selling point, certainly for those cases that aim for a visually pleasing result. However, this texture can very often also obscure the underlying geometrical details of the surface, making it very hard to assess the morphological features of the digitised artefact or scene. Instead of constantly switching between the textured and untextured version of the 3D surface model, this paper presents a new method to generate a morphology-enhanced colour texture for the 3D polymesh. The presented approach tries to overcome this switching between objects visualisations by fusing the original colour texture data with a specific depiction of the surface normals. Whether applied to the original 3D surface model or a lowresolution derivative, this newly generated texture does not solely convey the colours in a proper way but also enhances the smalland large-scale spatial and morphological features that are hard or impossible to perceive in the original textured model. In addition, the technique is very useful for low-end 3D viewers, since no additional memory and computing capacity are needed to convey relief details properly. Apart from simple visualisation purposes, the textured 3D models are now also better suited for on-surface interpretative mapping and the generation of line drawings.

Computer Graphics Meets Image Fusion: the Power of Texture Baking to Simultaneously Visualise 3d Surface Features and Colour

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Verhoeven, G. J.

2017-08-01

Since a few years, structure-from-motion and multi-view stereo pipelines have become omnipresent in the cultural heritage domain. The fact that such Image-Based Modelling (IBM) approaches are capable of providing a photo-realistic texture along the threedimensional (3D) digital surface geometry is often considered a unique selling point, certainly for those cases that aim for a visually pleasing result. However, this texture can very often also obscure the underlying geometrical details of the surface, making it very hard to assess the morphological features of the digitised artefact or scene. Instead of constantly switching between the textured and untextured version of the 3D surface model, this paper presents a new method to generate a morphology-enhanced colour texture for the 3D polymesh. The presented approach tries to overcome this switching between objects visualisations by fusing the original colour texture data with a specific depiction of the surface normals. Whether applied to the original 3D surface model or a lowresolution derivative, this newly generated texture does not solely convey the colours in a proper way but also enhances the smalland large-scale spatial and morphological features that are hard or impossible to perceive in the original textured model. In addition, the technique is very useful for low-end 3D viewers, since no additional memory and computing capacity are needed to convey relief details properly. Apart from simple visualisation purposes, the textured 3D models are now also better suited for on-surface interpretative mapping and the generation of line drawings.

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

International Nuclear Information System (INIS)

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

2015-01-01

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

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

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

2017-07-01

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

A Local Texture-Based Superpixel Feature Coding for Saliency Detection Combined with Global Saliency

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Bingfei Nan

2015-12-01

Full Text Available Because saliency can be used as the prior knowledge of image content, saliency detection has been an active research area in image segmentation, object detection, image semantic understanding and other relevant image-based applications. In the case of saliency detection from cluster scenes, the salient object/region detected needs to not only be distinguished clearly from the background, but, preferably, to also be informative in terms of complete contour and local texture details to facilitate the successive processing. In this paper, a Local Texture-based Region Sparse Histogram (LTRSH model is proposed for saliency detection from cluster scenes. This model uses a combination of local texture patterns and color distribution as well as contour information to encode the superpixels to characterize the local feature of image for region contrast computing. Combining the region contrast as computed with the global saliency probability, a full-resolution salient map, in which the salient object/region detected adheres more closely to its inherent feature, is obtained on the bases of the corresponding high-level saliency spatial distribution as well as on the pixel-level saliency enhancement. Quantitative comparisons with five state-of-the-art saliency detection methods on benchmark datasets are carried out, and the comparative results show that the method we propose improves the detection performance in terms of corresponding measurements.

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

International Nuclear Information System (INIS)

Masotti, Matteo; Lanconelli, Nico; Campanini, Renato

2009-01-01

In this work, gray-scale invariant ranklet texture features are proposed for false positive reduction (FPR) in computer-aided detection (CAD) of breast masses. Two main considerations are at the basis of this proposal. First, false positive (FP) marks surviving our previous CAD system seem to be characterized by specific texture properties that can be used to discriminate them from masses. Second, our previous CAD system achieves invariance to linear/nonlinear monotonic gray-scale transformations by encoding regions of interest into ranklet images through the ranklet transform, an image transformation similar to the wavelet transform, yet dealing with pixels' ranks rather than with their gray-scale values. Therefore, the new FPR approach proposed herein defines a set of texture features which are calculated directly from the ranklet images corresponding to the regions of interest surviving our previous CAD system, hence, ranklet texture features; then, a support vector machine (SVM) classifier is used for discrimination. As a result of this approach, texture-based information is used to discriminate FP marks surviving our previous CAD system; at the same time, invariance to linear/nonlinear monotonic gray-scale transformations of the new CAD system is guaranteed, as ranklet texture features are calculated from ranklet images that have this property themselves by construction. To emphasize the gray-scale invariance of both the previous and new CAD systems, training and testing are carried out without any in-between parameters' adjustment on mammograms having different gray-scale dynamics; in particular, training is carried out on analog digitized mammograms taken from a publicly available digital database, whereas testing is performed on full-field digital mammograms taken from an in-house database. Free-response receiver operating characteristic (FROC) curve analysis of the two CAD systems demonstrates that the new approach achieves a higher reduction of FP marks

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

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Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

Classification of pre-sliced pork and Turkey ham qualities based on image colour and textural features and their relationships with consumer responses.

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Iqbal, Abdullah; Valous, Nektarios A; Mendoza, Fernando; Sun, Da-Wen; Allen, Paul

2010-03-01

Images of three qualities of pre-sliced pork and Turkey hams were evaluated for colour and textural features to characterize and classify them, and to model the ham appearance grading and preference responses of a group of consumers. A total of 26 colour features and 40 textural features were extracted for analysis. Using Mahalanobis distance and feature inter-correlation analyses, two best colour [mean of S (saturation in HSV colour space), std. deviation of b*, which indicates blue to yellow in L*a*b* colour space] and three textural features [entropy of b*, contrast of H (hue of HSV colour space), entropy of R (red of RGB colour space)] for pork, and three colour (mean of R, mean of H, std. deviation of a*, which indicates green to red in L*a*b* colour space) and two textural features [contrast of B, contrast of L* (luminance or lightness in L*a*b* colour space)] for Turkey hams were selected as features with the highest discriminant power. High classification performances were reached for both types of hams (>99.5% for pork and >90.5% for Turkey) using the best selected features or combinations of them. In spite of the poor/fair agreement among ham consumers as determined by Kappa analysis (Kappa-valuetexture appearance and acceptability), a dichotomous logistic regression model using the best image features was able to explain the variability of consumers' responses for all sensorial attributes with accuracies higher than 74.1% for pork hams and 83.3% for Turkey hams. Copyright 2009 Elsevier Ltd. All rights reserved.

Natural texture retrieval based on perceptual similarity measurement

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Gao, Ying; Dong, Junyu; Lou, Jianwen; Qi, Lin; Liu, Jun

2018-04-01

A typical texture retrieval system performs feature comparison and might not be able to make human-like judgments of image similarity. Meanwhile, it is commonly known that perceptual texture similarity is difficult to be described by traditional image features. In this paper, we propose a new texture retrieval scheme based on texture perceptual similarity. The key of the proposed scheme is that prediction of perceptual similarity is performed by learning a non-linear mapping from image features space to perceptual texture space by using Random Forest. We test the method on natural texture dataset and apply it on a new wallpapers dataset. Experimental results demonstrate that the proposed texture retrieval scheme with perceptual similarity improves the retrieval performance over traditional image features.

Textural features of {sup 18}F-fluorodeoxyglucose positron emission tomography scanning in diagnosing aortic prosthetic graft infection

Energy Technology Data Exchange (ETDEWEB)

Saleem, Ben R.; Zeebregts, Clark J. [University of Groningen, University Medical Center Groningen, Department of Surgery, Division of Vascular Surgery, P.O. Box 30 001, Groningen (Netherlands); Beukinga, Roelof J.; Slart, Riemer H.J.A. [University of Groningen, University Medical Center Groningen, Nuclear Medicine and Molecular Imaging, Groningen (Netherlands); University of Twente, Department of Biomedical Photonic Imaging (BMPI), Enschede (Netherlands); Boellaard, Ronald; Glaudemans, Andor W.J.M. [University of Groningen, University Medical Center Groningen, Nuclear Medicine and Molecular Imaging, Groningen (Netherlands); Reijnen, Michel M.P.J. [Rijnstate Hospital, Department of Surgery, Arnhem (Netherlands)

2017-05-15

The clinical problem in suspected aortoiliac graft infection (AGI) is to obtain proof of infection. Although {sup 18}F-fluorodeoxyglucose ({sup 18}F-FDG) positron emission tomography scanning (PET) has been suggested to play a pivotal role, an evidence-based interpretation is lacking. The objective of this retrospective study was to examine the feasibility and utility of {sup 18}F-FDG uptake heterogeneity characterized by textural features to diagnose AGI. Thirty patients with a history of aortic graft reconstruction who underwent {sup 18}F-FDG PET/CT scanning were included. Sixteen patients were suspected to have an AGI (group I). AGI was considered proven only in the case of a positive bacterial culture. Positive cultures were found in 10 of the 16 patients (group Ia), and in the other six patients, cultures remained negative (group Ib). A control group was formed of 14 patients undergoing {sup 18}F-FDG PET for other reasons (group II). PET images were assessed using conventional maximal standardized uptake value (SUVmax), tissue-to-background ratio (TBR), and visual grading scale (VGS). Additionally, 64 different {sup 18}F-FDG PET based textural features were applied to characterize {sup 18}F-FDG uptake heterogeneity. To select candidate predictors, univariable logistic regression analysis was performed (α = 0.16). The accuracy was satisfactory in case of an AUC > 0.8. The feature selection process yielded the textural features named variance (AUC = 0.88), high grey level zone emphasis (AUC = 0.87), small zone low grey level emphasis (AUC = 0.80), and small zone high grey level emphasis (AUC = 0.81) most optimal for distinguishing between groups I and II. SUVmax, TBR, and VGS were also able to distinguish between these groups with AUCs of 0.87, 0.78, and 0.90, respectively. The textural feature named short run high grey level emphasis was able to distinguish group Ia from Ib (AUC = 0.83), while for the same task the TBR and VGS were not found to be predictive

Textural features of "1"8F-fluorodeoxyglucose positron emission tomography scanning in diagnosing aortic prosthetic graft infection

International Nuclear Information System (INIS)

Saleem, Ben R.; Zeebregts, Clark J.; Beukinga, Roelof J.; Slart, Riemer H.J.A.; Boellaard, Ronald; Glaudemans, Andor W.J.M.; Reijnen, Michel M.P.J.

2017-01-01

The clinical problem in suspected aortoiliac graft infection (AGI) is to obtain proof of infection. Although "1"8F-fluorodeoxyglucose ("1"8F-FDG) positron emission tomography scanning (PET) has been suggested to play a pivotal role, an evidence-based interpretation is lacking. The objective of this retrospective study was to examine the feasibility and utility of "1"8F-FDG uptake heterogeneity characterized by textural features to diagnose AGI. Thirty patients with a history of aortic graft reconstruction who underwent "1"8F-FDG PET/CT scanning were included. Sixteen patients were suspected to have an AGI (group I). AGI was considered proven only in the case of a positive bacterial culture. Positive cultures were found in 10 of the 16 patients (group Ia), and in the other six patients, cultures remained negative (group Ib). A control group was formed of 14 patients undergoing "1"8F-FDG PET for other reasons (group II). PET images were assessed using conventional maximal standardized uptake value (SUVmax), tissue-to-background ratio (TBR), and visual grading scale (VGS). Additionally, 64 different "1"8F-FDG PET based textural features were applied to characterize "1"8F-FDG uptake heterogeneity. To select candidate predictors, univariable logistic regression analysis was performed (α = 0.16). The accuracy was satisfactory in case of an AUC > 0.8. The feature selection process yielded the textural features named variance (AUC = 0.88), high grey level zone emphasis (AUC = 0.87), small zone low grey level emphasis (AUC = 0.80), and small zone high grey level emphasis (AUC = 0.81) most optimal for distinguishing between groups I and II. SUVmax, TBR, and VGS were also able to distinguish between these groups with AUCs of 0.87, 0.78, and 0.90, respectively. The textural feature named short run high grey level emphasis was able to distinguish group Ia from Ib (AUC = 0.83), while for the same task the TBR and VGS were not found to be predictive. SUVmax was found predictive

SU-F-R-45: The Prognostic Value of Radiotherapy Based On the Changes of Texture Features Between Pre-Treatment and Post-Treatment FDG PET Image for NSCLC Patients

International Nuclear Information System (INIS)

Ma, C; Yin, Y

2016-01-01

Purpose: The purpose of this research is investigating which texture features extracted from FDG-PET images by gray-level co-occurrence matrix(GLCM) have a higher prognostic value than the other texture features. Methods: 21 non-small cell lung cancer(NSCLC) patients were approved in the study. Patients underwent 18F-FDG PET/CT scans with both pre-treatment and post-treatment. Firstly, the tumors were extracted by our house developed software. Secondly, the clinical features including the maximum SUV and tumor volume were extracted by MIM vista software, and texture features including angular second moment, contrast, inverse different moment, entropy and correlation were extracted using MATLAB.The differences can be calculated by using post-treatment features to subtract pre-treatment features. Finally, the SPSS software was used to get the Pearson correlation coefficients and Spearman rank correlation coefficients between the change ratios of texture features and change ratios of clinical features. Results: The Pearson and Spearman rank correlation coefficient between contrast and SUV maximum is 0.785 and 0.709. The P and S value between inverse difference moment and tumor volume is 0.953 and 0.942. Conclusion: This preliminary study showed that the relationships between different texture features and the same clinical feature are different. Finding the prognostic value of contrast and inverse difference moment were higher than the other three textures extracted by GLCM.

SU-F-R-45: The Prognostic Value of Radiotherapy Based On the Changes of Texture Features Between Pre-Treatment and Post-Treatment FDG PET Image for NSCLC Patients

Energy Technology Data Exchange (ETDEWEB)

Ma, C; Yin, Y [Shandong Cancer Hospital and Institute, China, Jinan, Shandong (China)

2016-06-15

Purpose: The purpose of this research is investigating which texture features extracted from FDG-PET images by gray-level co-occurrence matrix(GLCM) have a higher prognostic value than the other texture features. Methods: 21 non-small cell lung cancer(NSCLC) patients were approved in the study. Patients underwent 18F-FDG PET/CT scans with both pre-treatment and post-treatment. Firstly, the tumors were extracted by our house developed software. Secondly, the clinical features including the maximum SUV and tumor volume were extracted by MIM vista software, and texture features including angular second moment, contrast, inverse different moment, entropy and correlation were extracted using MATLAB.The differences can be calculated by using post-treatment features to subtract pre-treatment features. Finally, the SPSS software was used to get the Pearson correlation coefficients and Spearman rank correlation coefficients between the change ratios of texture features and change ratios of clinical features. Results: The Pearson and Spearman rank correlation coefficient between contrast and SUV maximum is 0.785 and 0.709. The P and S value between inverse difference moment and tumor volume is 0.953 and 0.942. Conclusion: This preliminary study showed that the relationships between different texture features and the same clinical feature are different. Finding the prognostic value of contrast and inverse difference moment were higher than the other three textures extracted by GLCM.

Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma.

Science.gov (United States)

Kebir, Sied; Khurshid, Zain; Gaertner, Florian C; Essler, Markus; Hattingen, Elke; Fimmers, Rolf; Scheffler, Björn; Herrlinger, Ulrich; Bundschuh, Ralph A; Glas, Martin

2017-01-31

Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror tumor uptake heterogeneity; some of them may be associated with tumor progression. Fourteen patients with HGG and suspected of PSP underwent FET-PET imaging. A set of 19 conventional and textural FET-PET features were evaluated and subjected to unsupervised consensus clustering. The final diagnosis of true progression vs. PSP was based on follow-up MRI using RANO criteria. Three robust clusters have been identified based on 10 predominantly textural FET-PET features. None of the patients with PSP fell into cluster 2, which was associated with high values for textural FET-PET markers of uptake heterogeneity. Three out of 4 patients with PSP were assigned to cluster 3 that was largely associated with low values of textural FET-PET features. By comparison, tumor-to-normal brain ratio (TNRmax) at the optimal cutoff 2.1 was less predictive of PSP (negative predictive value 57% for detecting true progression, p=0.07 vs. 75% with cluster 3, p=0.04). Clustering based on textural O-(2-[18F]fluoroethyl)-L-tyrosine PET features may provide valuable information in assessing the elusive phenomenon of pseudoprogression.

Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer.

Science.gov (United States)

Garcia-Vicente, Ana María; Molina, David; Pérez-Beteta, Julián; Amo-Salas, Mariano; Martínez-González, Alicia; Bueno, Gloria; Tello-Galán, María Jesús; Soriano-Castrejón, Ángel

2017-12-01

To study the influence of dual time point 18F-FDG PET/CT in textural features and SUV-based variables and their relation among them. Fifty-six patients with locally advanced breast cancer (LABC) were prospectively included. All of them underwent a standard 18F-FDG PET/CT (PET-1) and a delayed acquisition (PET-2). After segmentation, SUV variables (SUVmax, SUVmean, and SUVpeak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained. Eighteen three-dimensional (3D) textural measures were computed including: run-length matrices (RLM) features, co-occurrence matrices (CM) features, and energies. Differences between all PET-derived variables obtained in PET-1 and PET-2 were studied. Significant differences were found between the SUV-based parameters and MTV obtained in the dual time point PET/CT, with higher values of SUV-based variables and lower MTV in the PET-2 with respect to the PET-1. In relation with the textural parameters obtained in dual time point acquisition, significant differences were found for the short run emphasis, low gray-level run emphasis, short run high gray-level emphasis, run percentage, long run emphasis, gray-level non-uniformity, homogeneity, and dissimilarity. Textural variables showed relations with MTV and TLG. Significant differences of textural features were found in dual time point 18F-FDG PET/CT. Thus, a dynamic behavior of metabolic characteristics should be expected, with higher heterogeneity in delayed PET acquisition compared with the standard PET. A greater heterogeneity was found in bigger tumors.

SU-E-J-256: Predicting Metastasis-Free Survival of Rectal Cancer Patients Treated with Neoadjuvant Chemo-Radiotherapy by Data-Mining of CT Texture Features of Primary Lesions

International Nuclear Information System (INIS)

Zhong, H; Wang, J; Shen, L; Hu, W; Wan, J; Zhou, Z; Zhang, Z

2015-01-01

Purpose: The purpose of this study is to investigate the relationship between computed tomographic (CT) texture features of primary lesions and metastasis-free survival for rectal cancer patients; and to develop a datamining prediction model using texture features. Methods: A total of 220 rectal cancer patients treated with neoadjuvant chemo-radiotherapy (CRT) were enrolled in this study. All patients underwent CT scans before CRT. The primary lesions on the CT images were delineated by two experienced oncologists. The CT images were filtered by Laplacian of Gaussian (LoG) filters with different filter values (1.0–2.5: from fine to coarse). Both filtered and unfiltered images were analyzed using Gray-level Co-occurrence Matrix (GLCM) texture analysis with different directions (transversal, sagittal, and coronal). Totally, 270 texture features with different species, directions and filter values were extracted. Texture features were examined with Student’s t-test for selecting predictive features. Principal Component Analysis (PCA) was performed upon the selected features to reduce the feature collinearity. Artificial neural network (ANN) and logistic regression were applied to establish metastasis prediction models. Results: Forty-six of 220 patients developed metastasis with a follow-up time of more than 2 years. Sixtyseven texture features were significantly different in t-test (p<0.05) between patients with and without metastasis, and 12 of them were extremely significant (p<0.001). The Area-under-the-curve (AUC) of ANN was 0.72, and the concordance index (CI) of logistic regression was 0.71. The predictability of ANN was slightly better than logistic regression. Conclusion: CT texture features of primary lesions are related to metastasisfree survival of rectal cancer patients. Both ANN and logistic regression based models can be developed for prediction

Visual texture perception via graph-based semi-supervised learning

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Zhang, Qin; Dong, Junyu; Zhong, Guoqiang

2018-04-01

Perceptual features, for example direction, contrast and repetitiveness, are important visual factors for human to perceive a texture. However, it needs to perform psychophysical experiment to quantify these perceptual features' scale, which requires a large amount of human labor and time. This paper focuses on the task of obtaining perceptual features' scale of textures by small number of textures with perceptual scales through a rating psychophysical experiment (what we call labeled textures) and a mass of unlabeled textures. This is the scenario that the semi-supervised learning is naturally suitable for. This is meaningful for texture perception research, and really helpful for the perceptual texture database expansion. A graph-based semi-supervised learning method called random multi-graphs, RMG for short, is proposed to deal with this task. We evaluate different kinds of features including LBP, Gabor, and a kind of unsupervised deep features extracted by a PCA-based deep network. The experimental results show that our method can achieve satisfactory effects no matter what kind of texture features are used.

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

Science.gov (United States)

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

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

[18]Fluorodeoxyglucose positron emission tomography for the textural features of cervical cancer associated with lymph node metastasis and histological type

International Nuclear Information System (INIS)

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

2017-01-01

In this study, we investigated the correlation between the lymph node (LN) status or histological types and textural features of cervical cancers on "1"8F-fluorodeoxyglucose positron emission tomography/computed tomography. We retrospectively reviewed the imaging records of 170 patients with International Federation of Gynecology and Obstetrics stage IB-IVA cervical cancer. Four groups of textural features were studied in addition to the maximum standardized uptake value (SUV_m_a_x), metabolic tumor volume, and total lesion glycolysis (TLG). Moreover, we studied the associations between the indices and clinical parameters, including the LN status, clinical stage, and histology. Receiver operating characteristic curves were constructed to evaluate the optimal predictive performance among the various textural indices. Quantitative differences were determined using the Mann-Whitney U test. Multivariate logistic regression analysis was performed to determine the independent factors, among all the variables, for predicting LN metastasis. Among all the significant indices related to pelvic LN metastasis, homogeneity derived from the gray-level co-occurrence matrix (GLCM) was the sole independent predictor. By combining SUV_m_a_x, the risk of pelvic LN metastasis can be scored accordingly. The TLG_m_e_a_n was the independent feature of positive para-aortic LNs. Quantitative differences between squamous and nonsquamous histology can be determined using short-zone emphasis (SZE) from the gray-level size zone matrix (GLSZM). This study revealed that in patients with cervical cancer, pelvic or para-aortic LN metastases can be predicted by using textural feature of homogeneity from the GLCM and TLG_m_e_a_n_, respectively. SZE from the GLSZM is the sole feature associated with quantitative differences between squamous and nonsquamous histology. (orig.)

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

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

2017-09-01

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

[18]Fluorodeoxyglucose positron emission tomography for the textural features of cervical cancer associated with lymph node metastasis and histological type

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Shen, Wei-Chih [Asia University, Department of Computer Science and Information Engineering, Taichung (China); Chen, Shang-Wen [China Medical University Hospital, Department of Radiation Oncology, Taichung (China); China Medical University, School of Medicine, Taichung (China); Taipei Medical University, School of Medicine, Taipei (China); China Medical University, Graduate Institute of Clinical Medical Science, School of Medicine, College of Medicine, Taichung (China); Liang, Ji-An [China Medical University Hospital, Department of Radiation Oncology, Taichung (China); China Medical University, Graduate Institute of Clinical Medical Science, School of Medicine, College of Medicine, Taichung (China); Hsieh, Te-Chun; Yen, Kuo-Yang [China Medical University Hospital, Department of Nuclear Medicine and PET Center, Taichung (China); China Medical University, Department of Biomedical Imaging and Radiological Science, Taichung (China); Kao, Chia-Hung [China Medical University, Graduate Institute of Clinical Medical Science, School of Medicine, College of Medicine, Taichung (China); China Medical University Hospital, Department of Nuclear Medicine and PET Center, Taichung (China); Asia University, Department of Bioinformatics and Medical Engineering, Taichung (China)

2017-09-15

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

Comparison of features response in texture-based iris segmentation

CSIR Research Space (South Africa)

Bachoo, A

2009-03-01

Full Text Available the Fisher linear discriminant and the iris region of interest is extracted. Four texture description methods are compared for segmenting iris texture using a region based pattern classification approach: Grey Level Co-occurrence Matrix (GLCM), Discrete...

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

Science.gov (United States)

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

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

Science.gov (United States)

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

2012-05-01

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

[Visual Texture Agnosia in Humans].

Science.gov (United States)

Suzuki, Kyoko

2015-06-01

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

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

Science.gov (United States)

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

2015-03-01

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

SU-F-R-31: Identification of Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease

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Choi, W; Riyahi, S; Lu, W [University of Maryland School of Medicine, Baltimore, MD (United States)

2016-06-15

Purpose: Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (radiation pneumonitis and radiation fibrosis). For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume. Methods: The free-breathing CTs of 14 lung SBRT patients were studied. Different sizes of GTVs were simulated with spheres placed at the upper lobe and lower lobe respectively in the normal lung (contralateral to tumor). 27 texture features (9 from intensity histogram, 8 from grey-level co-occurrence matrix [GLCM] and 10 from grey-level run-length matrix [GLRM]) were extracted from [normal lung-GTV]. To measure the variability of a feature F, the relative difference D=|Fref -Fsim|/Fref*100% was calculated, where Fref was for the entire normal lung and Fsim was for [normal lung-GTV]. A feature was considered as robust if the largest non-outlier (Q3+1.5*IQR) D was less than 5%, and considered as not correlated with normal lung volume when their Pearson correlation was lower than 0.50. Results: Only 11 features were robust. All first-order intensity-histogram features (mean, max, etc.) were robust, while most higher-order features (skewness, kurtosis, etc.) were unrobust. Only two of the GLCM and four of the GLRM features were robust. Larger GTV resulted greater feature variation, this was particularly true for unrobust features. All robust features were not correlated with normal lung volume while three unrobust features showed high correlation. Excessive variations were observed in two low grey-level run features and were later identified to be from one patient with local lung diseases (atelectasis) in the normal lung. There was no dependence on GTV location. Conclusion: We identified 11 robust normal lung CT texture features that can be further examined for the prediction of radiation-induced lung disease. Interestingly, low grey-level run features identified normal

Semantic attributes based texture generation

Science.gov (United States)

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

2018-04-01

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

TU-CD-BRB-01: Normal Lung CT Texture Features Improve Predictive Models for Radiation Pneumonitis

International Nuclear Information System (INIS)

Krafft, S; Briere, T; Court, L; Martel, M

2015-01-01

Purpose: Existing normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) traditionally rely on dosimetric and clinical data but are limited in terms of performance and generalizability. Extraction of pre-treatment image features provides a potential new category of data that can improve NTCP models for RP. We consider quantitative measures of total lung CT intensity and texture in a framework for prediction of RP. Methods: Available clinical and dosimetric data was collected for 198 NSCLC patients treated with definitive radiotherapy. Intensity- and texture-based image features were extracted from the T50 phase of the 4D-CT acquired for treatment planning. A total of 3888 features (15 clinical, 175 dosimetric, and 3698 image features) were gathered and considered candidate predictors for modeling of RP grade≥3. A baseline logistic regression model with mean lung dose (MLD) was first considered. Additionally, a least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the set of clinical and dosimetric features, and subsequently to the full set of clinical, dosimetric, and image features. Model performance was assessed by comparing area under the curve (AUC). Results: A simple logistic fit of MLD was an inadequate model of the data (AUC∼0.5). Including clinical and dosimetric parameters within the framework of the LASSO resulted in improved performance (AUC=0.648). Analysis of the full cohort of clinical, dosimetric, and image features provided further and significant improvement in model performance (AUC=0.727). Conclusions: To achieve significant gains in predictive modeling of RP, new categories of data should be considered in addition to clinical and dosimetric features. We have successfully incorporated CT image features into a framework for modeling RP and have demonstrated improved predictive performance. Validation and further investigation of CT image features in the context of RP NTCP

Document Image Processing: Going beyond the Black-and-White Barrier. Progress, Issues and Options with Greyscale and Colour Image Processing.

Science.gov (United States)

Hendley, Tom

1995-01-01

Discussion of digital document image processing focuses on issues and options associated with greyscale and color image processing. Topics include speed; size of original document; scanning resolution; markets for different categories of scanners, including photographic libraries, publishing, and office applications; hybrid systems; data…

Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery.

Directory of Open Access Journals (Sweden)

Ahmad Chaddad

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

A Clinical Decision Support System Using Ultrasound Textures and Radiologic Features to Distinguish Metastasis From Tumor-Free Cervical Lymph Nodes in Patients With Papillary Thyroid Carcinoma.

Science.gov (United States)

Abbasian Ardakani, Ali; Reiazi, Reza; Mohammadi, Afshin

2018-03-30

This study investigated the potential of a clinical decision support approach for the classification of metastatic and tumor-free cervical lymph nodes (LNs) in papillary thyroid carcinoma on the basis of radiologic and textural analysis through ultrasound (US) imaging. In this research, 170 metastatic and 170 tumor-free LNs were examined by the proposed clinical decision support method. To discover the difference between the groups, US imaging was used for the extraction of radiologic and textural features. The radiologic features in the B-mode scans included the echogenicity, margin, shape, and presence of microcalcification. To extract the textural features, a wavelet transform was applied. A support vector machine classifier was used to classify the LNs. In the training set data, a combination of radiologic and textural features represented the best performance with sensitivity, specificity, accuracy, and area under the curve (AUC) values of 97.14%, 98.57%, 97.86%, and 0.994, respectively, whereas the classification based on radiologic and textural features alone yielded lower performance, with AUCs of 0.964 and 0.922. On testing the data set, the proposed model could classify the tumor-free and metastatic LNs with an AUC of 0.952, which corresponded to sensitivity, specificity, and accuracy of 93.33%, 96.66%, and 95.00%. The clinical decision support method based on textural and radiologic features has the potential to characterize LNs via 2-dimensional US. Therefore, it can be used as a supplementary technique in daily clinical practice to improve radiologists' understanding of conventional US imaging for characterizing LNs. © 2018 by the American Institute of Ultrasound in Medicine.

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

International Nuclear Information System (INIS)

Anthony, G; Cunliffe, A; Armato, S; Al-Hallaq, H; Castillo, R; Pham, N; Guerrero, T

2015-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Anthony, G; Cunliffe, A; Armato, S; Al-Hallaq, H [The University of Chicago, Chicago, IL (United States); Castillo, R [Univ Texas Medical Branch of Galveston, Pearland, TX (United States); Pham, N [Baylor College of Medicine, Houston, TX (United States); Guerrero, T [Beaumont Health System, Royal Oak, MI (United States)

2015-06-15

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

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

International Nuclear Information System (INIS)

Nyflot, MJ; Yang, F; Byrd, D; Bowen, SR; Sandison, GA; Kinahan, PE

2015-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Nyflot, MJ; Yang, F; Byrd, D; Bowen, SR; Sandison, GA; Kinahan, PE [University of Washington, Seattle, WA (United States)

2015-06-15

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

Investigating the use of texture features for analysis of breast lesions on contrast-enhanced cone beam CT

Science.gov (United States)

Wang, Xixi; Nagarajan, Mahesh B.; Conover, David; Ning, Ruola; O'Connell, Avice; Wismueller, Axel

2014-04-01

Cone beam computed tomography (CBCT) has found use in mammography for imaging the entire breast with sufficient spatial resolution at a radiation dose within the range of that of conventional mammography. Recently, enhancement of lesion tissue through the use of contrast agents has been proposed for cone beam CT. This study investigates whether the use of such contrast agents improves the ability of texture features to differentiate lesion texture from healthy tissue on CBCT in an automated manner. For this purpose, 9 lesions were annotated by an experienced radiologist on both regular and contrast-enhanced CBCT images using two-dimensional (2D) square ROIs. These lesions were then segmented, and each pixel within the lesion ROI was assigned a label - lesion or non-lesion, based on the segmentation mask. On both sets of CBCT images, four three-dimensional (3D) Minkowski Functionals were used to characterize the local topology at each pixel. The resulting feature vectors were then used in a machine learning task involving support vector regression with a linear kernel (SVRlin) to classify each pixel as belonging to the lesion or non-lesion region of the ROI. Classification performance was assessed using the area under the receiver-operating characteristic (ROC) curve (AUC). Minkowski Functionals derived from contrastenhanced CBCT images were found to exhibit significantly better performance at distinguishing between lesion and non-lesion areas within the ROI when compared to those extracted from CBCT images without contrast enhancement (p < 0.05). Thus, contrast enhancement in CBCT can improve the ability of texture features to distinguish lesions from surrounding healthy tissue.

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

Energy Technology Data Exchange (ETDEWEB)

Cheng, Nai-Ming [Chang Gung Memorial Hospital and Chang Gung University, Departments of Nuclear Medicine, Taiyuan (China); Chang Gung Memorial Hospital, Department of Nuclear Medicine, Keelung (China); National Tsing Hua University, Department of Biomedical Engineering and Environmental Sciences, Hsinchu (China); Fang, Yu-Hua Dean [Chang Gung University, Department of Electrical Engineering, Taiyuan (China); Lee, Li-yu [Chang Gung University College of Medicine, Department of Pathology, Chang Gung Memorial Hospital, Taoyuan (China); Chang, Joseph Tung-Chieh; Tsan, Din-Li [Chang Gung University College of Medicine, Department of Radiation Oncology, Chang Gung Memorial Hospital, Taoyuan (China); Ng, Shu-Hang [Chang Gung University College of Medicine, Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Taoyuan (China); Wang, Hung-Ming [Chang Gung University College of Medicine, Division of Hematology/Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital, Taoyuan (China); Liao, Chun-Ta [Chang Gung University College of Medicine, Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Taoyuan (China); Yang, Lan-Yan [Chang Gung Memorial Hospital, Biostatistics Unit, Clinical Trial Center, Taoyuan (China); Hsu, Ching-Han [National Tsing Hua University, Department of Biomedical Engineering and Environmental Sciences, Hsinchu (China); Yen, Tzu-Chen [Chang Gung Memorial Hospital and Chang Gung University, Departments of Nuclear Medicine, Taiyuan (China); Chang Gung University College of Medicine, Department of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital, Taipei (China)

2014-10-23

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

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

International Nuclear Information System (INIS)

Krafft, S; Court, L; Briere, T; Martel, M

2014-01-01

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

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

International Nuclear Information System (INIS)

Oliver, J; Budzevich, M; Moros, E; Zhang, G; Hunt, D

2015-01-01

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

Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods.

Science.gov (United States)

Georgiadis, Pantelis; Cavouras, Dionisis; Kalatzis, Ioannis; Glotsos, Dimitris; Athanasiadis, Emmanouil; Kostopoulos, Spiros; Sifaki, Koralia; Malamas, Menelaos; Nikiforidis, George; Solomou, Ekaterini

2009-01-01

Three-dimensional (3D) texture analysis of volumetric brain magnetic resonance (MR) images has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to evaluate the efficiency of 3D textural features using a pattern recognition system in the task of discriminating benign, malignant and metastatic brain tissues on T1 postcontrast MR imaging (MRI) series. The dataset consisted of 67 brain MRI series obtained from patients with verified and untreated intracranial tumors. The pattern recognition system was designed as an ensemble classification scheme employing a support vector machine classifier, specially modified in order to integrate the least squares features transformation logic in its kernel function. The latter, in conjunction with using 3D textural features, enabled boosting up the performance of the system in discriminating metastatic, malignant and benign brain tumors with 77.14%, 89.19% and 93.33% accuracy, respectively. The method was evaluated using an external cross-validation process; thus, results might be considered indicative of the generalization performance of the system to "unseen" cases. The proposed system might be used as an assisting tool for brain tumor characterization on volumetric MRI series.

Pretreatment 18F-FDG PET Textural Features in Locally Advanced Non-Small Cell Lung Cancer: Secondary Analysis of ACRIN 6668/RTOG 0235.

Science.gov (United States)

Ohri, Nitin; Duan, Fenghai; Snyder, Bradley S; Wei, Bo; Machtay, Mitchell; Alavi, Abass; Siegel, Barry A; Johnson, Douglas W; Bradley, Jeffrey D; DeNittis, Albert; Werner-Wasik, Maria; El Naqa, Issam

2016-06-01

In a secondary analysis of American College of Radiology Imaging Network (ACRIN) 6668/RTOG 0235, high pretreatment metabolic tumor volume (MTV) on (18)F-FDG PET was found to be a poor prognostic factor for patients treated with chemoradiotherapy for locally advanced non-small cell lung cancer (NSCLC). Here we utilize the same dataset to explore whether heterogeneity metrics based on PET textural features can provide additional prognostic information. Patients with locally advanced NSCLC underwent (18)F-FDG PET prior to treatment. A gradient-based segmentation tool was used to contour each patient's primary tumor. MTV, maximum SUV, and 43 textural features were extracted for each tumor. To address overfitting and high collinearity among PET features, the least absolute shrinkage and selection operator (LASSO) method was applied to identify features that were independent predictors of overall survival (OS) after adjusting for MTV. Recursive binary partitioning in a conditional inference framework was utilized to identify optimal thresholds. Kaplan-Meier curves and log-rank testing were used to compare outcomes among patient groups. Two hundred one patients met inclusion criteria. The LASSO procedure identified 1 textural feature (SumMean) as an independent predictor of OS. The optimal cutpoint for MTV was 93.3 cm(3), and the optimal SumMean cutpoint for tumors above 93.3 cm(3) was 0.018. This grouped patients into three categories: low tumor MTV (n = 155; median OS, 22.6 mo), high tumor MTV and high SumMean (n = 23; median OS, 20.0 mo), and high tumor MTV and low SumMean (n = 23; median OS, 6.2 mo; log-rank P textural PET features in the context of established prognostic factors. We have also identified a promising feature that may have prognostic value in locally advanced NSCLC patients with large tumors who are treated with chemoradiotherapy. Validation studies are warranted. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Pretreatment 18F-FDG PET Textural Features in Locally Advanced Non–Small Cell Lung Cancer: Secondary Analysis of ACRIN 6668/RTOG 0235

Science.gov (United States)

Ohri, Nitin; Duan, Fenghai; Snyder, Bradley S.; Wei, Bo; Machtay, Mitchell; Alavi, Abass; Siegel, Barry A.; Johnson, Douglas W.; Bradley, Jeffrey D.; DeNittis, Albert; Werner-Wasik, Maria; El Naqa, Issam

2016-01-01

In a secondary analysis of American College of Radiology Imaging Network (ACRIN) 6668/RTOG 0235, high pretreatment metabolic tumor volume (MTV) on 18F-FDG PET was found to be a poor prognostic factor for patients treated with chemoradiotherapy for locally advanced non–small cell lung cancer (NSCLC). Here we utilize the same dataset to explore whether heterogeneity metrics based on PET textural features can provide additional prognostic information. Methods Patients with locally advanced NSCLC underwent 18F-FDG PET prior to treatment. A gradient-based segmentation tool was used to contour each patient’s primary tumor. MTV, maximum SUV, and 43 textural features were extracted for each tumor. To address over-fitting and high collinearity among PET features, the least absolute shrinkage and selection operator (LASSO) method was applied to identify features that were independent predictors of overall survival (OS) after adjusting for MTV. Recursive binary partitioning in a conditional inference framework was utilized to identify optimal thresholds. Kaplan–Meier curves and log-rank testing were used to compare outcomes among patient groups. Results Two hundred one patients met inclusion criteria. The LASSO procedure identified 1 textural feature (SumMean) as an independent predictor of OS. The optimal cutpoint for MTV was 93.3 cm3, and the optimal Sum-Mean cutpoint for tumors above 93.3 cm3 was 0.018. This grouped patients into three categories: low tumor MTV (n = 155; median OS, 22.6 mo), high tumor MTV and high SumMean (n = 23; median OS, 20.0 mo), and high tumor MTV and low SumMean (n = 23; median OS, 6.2 mo; log-rank P textural PET features in the context of established prognostic factors. We have also identified a promising feature that may have prognostic value in locally advanced NSCLC patients with large tumors who are treated with chemoradiotherapy. Validation studies are warranted. PMID:26912429

WE-E-17A-05: Complementary Prognostic Value of CT and 18F-FDG PET Non-Small Cell Lung Cancer Tumor Heterogeneity Features Quantified Through Texture Analysis

Energy Technology Data Exchange (ETDEWEB)

Desseroit, M; Cheze Le Rest, C; Tixier, F [CHU Poitiers Poitiers (France); INSERM LaTIM UMR 1101, Brest (France); Majdoub, M; Visvikis, D; Hatt, M [INSERM LaTIM UMR 1101, Brest (France); Guillevin, R; Perdrisot, R [CHU Poitiers Poitiers (France)

2014-06-15

Purpose: Previous studies have shown that CT or 18F-FDG PET intratumor heterogeneity features computed using texture analysis may have prognostic value in Non-Small Cell Lung Cancer (NSCLC), but have been mostly investigated separately. The purpose of this study was to evaluate the potential added value with respect to prognosis regarding the combination of non-enhanced CT and 18F-FDG PET heterogeneity textural features on primary NSCLC tumors. Methods: One hundred patients with non-metastatic NSCLC (stage I–III), treated with surgery and/or (chemo)radiotherapy, that underwent staging 18F-FDG PET/CT images, were retrospectively included. Morphological tumor volumes were semi-automatically delineated on non-enhanced CT using 3D SlicerTM. Metabolically active tumor volumes (MATV) were automatically delineated on PET using the Fuzzy Locally Adaptive Bayesian (FLAB) method. Intratumoral tissue density and FDG uptake heterogeneities were quantified using texture parameters calculated from co-occurrence, difference, and run-length matrices. In addition to these textural features, first order histogram-derived metrics were computed on the whole morphological CT tumor volume, as well as on sub-volumes corresponding to fine, medium or coarse textures determined through various levels of LoG-filtering. Association with survival regarding all extracted features was assessed using Cox regression for both univariate and multivariate analysis. Results: Several PET and CT heterogeneity features were prognostic factors of overall survival in the univariate analysis. CT histogram-derived kurtosis and uniformity, as well as Low Grey-level High Run Emphasis (LGHRE), and PET local entropy were independent prognostic factors. Combined with stage and MATV, they led to a powerful prognostic model (p<0.0001), with median survival of 49 vs. 12.6 months and a hazard ratio of 3.5. Conclusion: Intratumoral heterogeneity quantified through textural features extracted from both CT and FDG PET

An explorative childhood pneumonia analysis based on ultrasonic imaging texture features

Science.gov (United States)

Zenteno, Omar; Diaz, Kristians; Lavarello, Roberto; Zimic, Mirko; Correa, Malena; Mayta, Holger; Anticona, Cynthia; Pajuelo, Monica; Oberhelman, Richard; Checkley, William; Gilman, Robert H.; Figueroa, Dante; Castañeda, Benjamín.

2015-12-01

According to World Health Organization, pneumonia is the respiratory disease with the highest pediatric mortality rate accounting for 15% of all deaths of children under 5 years old worldwide. The diagnosis of pneumonia is commonly made by clinical criteria with support from ancillary studies and also laboratory findings. Chest imaging is commonly done with chest X-rays and occasionally with a chest CT scan. Lung ultrasound is a promising alternative for chest imaging; however, interpretation is subjective and requires adequate training. In the present work, a two-class classification algorithm based on four Gray-level co-occurrence matrix texture features (i.e., Contrast, Correlation, Energy and Homogeneity) extracted from lung ultrasound images from children aged between six months and five years is presented. Ultrasound data was collected using a L14-5/38 linear transducer. The data consisted of 22 positive- and 68 negative-diagnosed B-mode cine-loops selected by a medical expert and captured in the facilities of the Instituto Nacional de Salud del Niño (Lima, Peru), for a total number of 90 videos obtained from twelve children diagnosed with pneumonia. The classification capacity of each feature was explored independently and the optimal threshold was selected by a receiver operator characteristic (ROC) curve analysis. In addition, a principal component analysis was performed to evaluate the combined performance of all the features. Contrast and correlation resulted the two more significant features. The classification performance of these two features by principal components was evaluated. The results revealed 82% sensitivity, 76% specificity, 78% accuracy and 0.85 area under the ROC.

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

Directory of Open Access Journals (Sweden)

Maíla de Lima Claro

2016-08-01

Full Text Available The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classiffication of images in glaucomatous or not. We obtained results of 93% accuracy.

MO-G-BRF-01: BEST IN PHYSICS (JOINT IMAGING-THERAPY) - Sensitivity of PET-Based Texture Features to Respiratory Motion in Non-Small Cell Lung Cancer (NSCLC)

Energy Technology Data Exchange (ETDEWEB)

Yip, S; Aerts, H; Berbeco, R [Brigham and Womens Hospital, Boston, MA (United States); Farber Cancer Institute, Boston, MA (United States); McCall, K [Brigham and Womens Hospital, Boston, MA (United States); Aristophanous, M [Farber Cancer Institute, Boston, MA (United States); Chen, A [UT MD Anderson Cancer Center, Houston, TX, (United States)

2014-06-15

Purpose: PET-based texture features are used to quantify tumor heterogeneity due to their predictive power in treatment outcome. We investigated the sensitivity of texture features to tumor motion by comparing whole body (3D) and respiratory-gated (4D) PET imaging. Methods: Twenty-six patients (34 lesions) received 3D and 4D [F-18]FDG-PET scans before chemo-radiotherapy. The acquired 4D data were retrospectively binned into five breathing phases to create the 4D image sequence. Four texture features (Coarseness, Contrast, Busyness, and Complexity) were computed within the the physician-defined tumor volume. The relative difference (δ) in each measure between the 3D- and 4D-PET imaging was calculated. Wilcoxon signed-rank test (p<0.01) was used to determine if δ was significantly different from zero. Coefficient of variation (CV) was used to determine the variability in the texture features between all 4D-PET phases. Pearson correlation coefficient was used to investigate the impact of tumor size and motion amplitude on δ. Results: Significant differences (p<<0.01) between 3D and 4D imaging were found for Coarseness, Busyness, and Complexity. The difference for Contrast was not significant (p>0.24). 4D-PET increased Busyness (∼20%) and Complexity (∼20%), and decreased Coarseness (∼10%) and Contrast (∼5%) compared to 3D-PET. Nearly negligible variability (CV=3.9%) was found between the 4D phase bins for Coarseness and Complexity. Moderate variability was found for Contrast and Busyness (CV∼10%). Poor correlation was found between the tumor volume and δ for the texture features (R=−0.34−0.34). Motion amplitude had moderate impact on δ for Contrast and Busyness (R=−0.64− 0.54) and no impact for Coarseness and Complexity (R=−0.29−0.17). Conclusion: Substantial differences in textures were found between 3D and 4D-PET imaging. Moreover, the variability between phase bins for Coarseness and Complexity was negligible, suggesting that similar

MO-G-BRF-01: BEST IN PHYSICS (JOINT IMAGING-THERAPY) - Sensitivity of PET-Based Texture Features to Respiratory Motion in Non-Small Cell Lung Cancer (NSCLC)

International Nuclear Information System (INIS)

Yip, S; Aerts, H; Berbeco, R; McCall, K; Aristophanous, M; Chen, A

2014-01-01

Purpose: PET-based texture features are used to quantify tumor heterogeneity due to their predictive power in treatment outcome. We investigated the sensitivity of texture features to tumor motion by comparing whole body (3D) and respiratory-gated (4D) PET imaging. Methods: Twenty-six patients (34 lesions) received 3D and 4D [F-18]FDG-PET scans before chemo-radiotherapy. The acquired 4D data were retrospectively binned into five breathing phases to create the 4D image sequence. Four texture features (Coarseness, Contrast, Busyness, and Complexity) were computed within the the physician-defined tumor volume. The relative difference (δ) in each measure between the 3D- and 4D-PET imaging was calculated. Wilcoxon signed-rank test (p<0.01) was used to determine if δ was significantly different from zero. Coefficient of variation (CV) was used to determine the variability in the texture features between all 4D-PET phases. Pearson correlation coefficient was used to investigate the impact of tumor size and motion amplitude on δ. Results: Significant differences (p<<0.01) between 3D and 4D imaging were found for Coarseness, Busyness, and Complexity. The difference for Contrast was not significant (p>0.24). 4D-PET increased Busyness (∼20%) and Complexity (∼20%), and decreased Coarseness (∼10%) and Contrast (∼5%) compared to 3D-PET. Nearly negligible variability (CV=3.9%) was found between the 4D phase bins for Coarseness and Complexity. Moderate variability was found for Contrast and Busyness (CV∼10%). Poor correlation was found between the tumor volume and δ for the texture features (R=−0.34−0.34). Motion amplitude had moderate impact on δ for Contrast and Busyness (R=−0.64− 0.54) and no impact for Coarseness and Complexity (R=−0.29−0.17). Conclusion: Substantial differences in textures were found between 3D and 4D-PET imaging. Moreover, the variability between phase bins for Coarseness and Complexity was negligible, suggesting that similar

Modeling Human Aesthetic Perception of Visual Textures

NARCIS (Netherlands)

Thumfart, Stefan; Jacobs, Richard H. A. H.; Lughofer, Edwin; Eitzinger, Christian; Cornelissen, Frans W.; Groissboeck, Werner; Richter, Roland

Texture is extensively used in areas such as product design and architecture to convey specific aesthetic information. Using the results of a psychological experiment, we model the relationship between computational texture features and aesthetic properties of visual textures. Contrary to previous

Texture-based segmentation with Gabor filters, wavelet and pyramid decompositions for extracting individual surface features from areal surface topography maps

International Nuclear Information System (INIS)

Senin, Nicola; Leach, Richard K; Pini, Stefano; Blunt, Liam A

2015-01-01

Areal topography segmentation plays a fundamental role in those surface metrology applications concerned with the characterisation of individual topography features. Typical scenarios include the dimensional inspection and verification of micro-structured surface features, and the identification and characterisation of localised defects and other random singularities. While morphological segmentation into hills or dales is the only partitioning operation currently endorsed by the ISO specification standards on surface texture metrology, many other approaches are possible, in particular adapted from the literature on digital image segmentation. In this work an original segmentation approach is introduced and discussed, where topography partitioning is driven by information collected through the application of texture characterisation transforms popular in digital image processing. Gabor filters, wavelets and pyramid decompositions are investigated and applied to a selected set of test cases. The behaviour, performance and limitations of the proposed approach are discussed from the viewpoint of the identification and extraction of individual surface topography features. (paper)

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

Science.gov (United States)

Zyout, Imad; Czajkowska, Joanna; Grzegorzek, Marcin

2015-12-01

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

Text-independent writer identification and verification using textural and allographic features.

Science.gov (United States)

Bulacu, Marius; Schomaker, Lambert

2007-04-01

The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates.

TU-F-12A-05: Sensitivity of Textural Features to 3D Vs. 4D FDG-PET/CT Imaging in NSCLC Patients

Energy Technology Data Exchange (ETDEWEB)

Yang, F; Nyflot, M; Bowen, S; Kinahan, P; Sandison, G [University of Washington Medical Center, Seattle, WA (United States)

2014-06-15

Purpose: Neighborhood Gray-level difference matrices (NGLDM) based texture parameters extracted from conventional (3D) 18F-FDG PET scans in patients with NSCLC have been previously shown to associate with response to chemoradiation and poorer patient outcome. However, the change in these parameters when utilizing respiratory-correlated (4D) FDG-PET scans has not yet been characterized for NSCLC. The Objectives: of this study was to assess the extent to which NGLDM-based texture parameters on 4D PET images vary with reference to values derived from 3D scans in NSCLC. Methods: Eight patients with newly diagnosed NSCLC treated with concomitant chemoradiotherapy were included in this study. 4D PET scans were reconstructed with OSEM-IR in 5 respiratory phase-binned images and corresponding CT data of each phase were employed for attenuation correction. NGLDM-based texture features, consisting of coarseness, contrast, busyness, complexity and strength, were evaluated for gross tumor volumes defined on 3D/4D PET scans by radiation oncologists. Variation of the obtained texture parameters over the respiratory cycle were examined with respect to values extracted from 3D scans. Results: Differences between texture parameters derived from 4D scans at different respiratory phases and those extracted from 3D scans ranged from −30% to 13% for coarseness, −12% to 40% for contrast, −5% to 50% for busyness, −7% to 38% for complexity, and −43% to 20% for strength. Furthermore, no evident correlations were observed between respiratory phase and 4D scan texture parameters. Conclusion: Results of the current study showed that NGLDM-based texture parameters varied considerably based on choice of 3D PET and 4D PET reconstruction of NSCLC patient images, indicating that standardized image acquisition and analysis protocols need to be established for clinical studies, especially multicenter clinical trials, intending to validate prognostic values of texture features for NSCLC.

Shape and Texture Based Classification of Fish Species

DEFF Research Database (Denmark)

Larsen, Rasmus; Ólafsdóttir, Hildur; Ersbøll, Bjarne Kjær

2009-01-01

In this paper we conduct a case study of ¯sh species classi- fication based on shape and texture. We consider three fish species: cod, haddock, and whiting. We derive shape and texture features from an appearance model of a set of training data. The fish in the training images were manual outlined......, and a few features including the eye and backbone contour were also annotated. From these annotations an optimal MDL curve correspondence and a subsequent image registration were derived. We have analyzed a series of shape and texture and combined shape and texture modes of variation for their ability...

Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

Science.gov (United States)

Szantoi, Zoltan; Escobedo, Francisco J; Abd-Elrahman, Amr; Pearlstine, Leonard; Dewitt, Bon; Smith, Scot

2015-05-01

Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (αtexture features.

Evaluation of color representation for texture analysis

NARCIS (Netherlands)

Verbrugge, R.; van den Broek, Egon; van Rikxoort, E.M.; Taatgen, N.; Schomaker, L.

2004-01-01

Since more than 50 years texture in image material is a topic of research. Hereby, color was ignored mostly. This study compares 70 different configurations for texture analysis, using four features. For the configurations we used: (i) a gray value texture descriptor: the co-occurrence matrix and a

Touching Textured Surfaces: Cells in Somatosensory Cortex Respond Both to Finger Movement and to Surface Features

Science.gov (United States)

Darian-Smith, Ian; Sugitani, Michio; Heywood, John; Karita, Keishiro; Goodwin, Antony

1982-11-01

Single neurons in Brodmann's areas 3b and 1 of the macaque postcentral gyrus discharge when the monkey rubs the contralateral finger pads across a textured surface. Both the finger movement and the spatial pattern of the surface determine this discharge in each cell. The spatial features of the surface are represented unambiguously only in the responses of populations of these neurons, and not in the responses of the constituent cells.

Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features.

Science.gov (United States)

Su, Yanni; Wang, Yuanyuan; Jiao, Jing; Guo, Yi

2011-01-01

Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.

Investigation of attenuation correction in SPECT using textural features, Monte Carlo simulations, and computational anthropomorphic models.

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Spirou, Spiridon V; Papadimitroulas, Panagiotis; Liakou, Paraskevi; Georgoulias, Panagiotis; Loudos, George

2015-09-01

To present and evaluate a new methodology to investigate the effect of attenuation correction (AC) in single-photon emission computed tomography (SPECT) using textural features analysis, Monte Carlo techniques, and a computational anthropomorphic model. The GATE Monte Carlo toolkit was used to simulate SPECT experiments using the XCAT computational anthropomorphic model, filled with a realistic biodistribution of (99m)Tc-N-DBODC. The simulated gamma camera was the Siemens ECAM Dual-Head, equipped with a parallel hole lead collimator, with an image resolution of 3.54 × 3.54 mm(2). Thirty-six equispaced camera positions, spanning a full 360° arc, were simulated. Projections were calculated after applying a ± 20% energy window or after eliminating all scattered photons. The activity of the radioisotope was reconstructed using the MLEM algorithm. Photon attenuation was accounted for by calculating the radiological pathlength in a perpendicular line from the center of each voxel to the gamma camera. Twenty-two textural features were calculated on each slice, with and without AC, using 16 and 64 gray levels. A mask was used to identify only those pixels that belonged to each organ. Twelve of the 22 features showed almost no dependence on AC, irrespective of the organ involved. In both the heart and the liver, the mean and SD were the features most affected by AC. In the liver, six features were affected by AC only on some slices. Depending on the slice, skewness decreased by 22-34% with AC, kurtosis by 35-50%, long-run emphasis mean by 71-91%, and long-run emphasis range by 62-95%. In contrast, gray-level non-uniformity mean increased by 78-218% compared with the value without AC and run percentage mean by 51-159%. These results were not affected by the number of gray levels (16 vs. 64) or the data used for reconstruction: with the energy window or without scattered photons. The mean and SD were the main features affected by AC. In the heart, no other feature was

Diagnostic and prognostic value of amyloid PET textural and shape features: comparison with classical semi-quantitative rating in 760 patients from the ADNI-2 database.

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Ben Bouallègue, Fayçal; Vauchot, Fabien; Mariano-Goulart, Denis; Payoux, Pierre

2018-02-09

We evaluated the performance of amyloid PET textural and shape features in discriminating normal and Alzheimer's disease (AD) subjects, and in predicting conversion to AD in subjects with mild cognitive impairment (MCI) or significant memory concern (SMC). Subjects from the Alzheimer's Disease Neuroimaging Initiative with available baseline 18 F-florbetapir and T1-MRI scans were included. The cross-sectional cohort consisted of 181 controls and 148 AD subjects. The longitudinal cohort consisted of 431 SMC/MCI subjects, 85 of whom converted to AD during follow-up. PET images were normalized to MNI space and post-processed using in-house software. Relative retention indices (SUVr) were computed with respect to pontine, cerebellar, and composite reference regions. Several textural and shape features were extracted then combined using a support vector machine (SVM) to build a predictive model of AD conversion. Diagnostic and prognostic performance was evaluated using ROC analysis and survival analysis with the Cox proportional hazard model. The three SUVr and all the tested features effectively discriminated AD subjects in cross-sectional analysis (all p textural and shape features predict conversion to AD with at least as good accuracy as classical SUVr.

Comparative study on the performance of textural image features for active contour segmentation.

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Moraru, Luminita; Moldovanu, Simona

2012-07-01

We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard deviation textural feature and a 5×5 kernel, our curve evolution was able to produce results close to the minimal area error rate (namely 8.88% for breast images and 10.82% for liver images). The image resolution was evaluated using the contrast-to-gradient method. The experiments showed promising segmentation results.

Visual perception of procedural textures: identifying perceptual dimensions and predicting generation models.

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Liu, Jun; Dong, Junyu; Cai, Xiaoxu; Qi, Lin; Chantler, Mike

2015-01-01

Procedural models are widely used in computer graphics for generating realistic, natural-looking textures. However, these mathematical models are not perceptually meaningful, whereas the users, such as artists and designers, would prefer to make descriptions using intuitive and perceptual characteristics like "repetitive," "directional," "structured," and so on. To make up for this gap, we investigated the perceptual dimensions of textures generated by a collection of procedural models. Two psychophysical experiments were conducted: free-grouping and rating. We applied Hierarchical Cluster Analysis (HCA) and Singular Value Decomposition (SVD) to discover the perceptual features used by the observers in grouping similar textures. The results suggested that existing dimensions in literature cannot accommodate random textures. We therefore utilized isometric feature mapping (Isomap) to establish a three-dimensional perceptual texture space which better explains the features used by humans in texture similarity judgment. Finally, we proposed computational models to map perceptual features to the perceptual texture space, which can suggest a procedural model to produce textures according to user-defined perceptual scales.

Visual perception of procedural textures: identifying perceptual dimensions and predicting generation models.

Directory of Open Access Journals (Sweden)

Jun Liu

Full Text Available Procedural models are widely used in computer graphics for generating realistic, natural-looking textures. However, these mathematical models are not perceptually meaningful, whereas the users, such as artists and designers, would prefer to make descriptions using intuitive and perceptual characteristics like "repetitive," "directional," "structured," and so on. To make up for this gap, we investigated the perceptual dimensions of textures generated by a collection of procedural models. Two psychophysical experiments were conducted: free-grouping and rating. We applied Hierarchical Cluster Analysis (HCA and Singular Value Decomposition (SVD to discover the perceptual features used by the observers in grouping similar textures. The results suggested that existing dimensions in literature cannot accommodate random textures. We therefore utilized isometric feature mapping (Isomap to establish a three-dimensional perceptual texture space which better explains the features used by humans in texture similarity judgment. Finally, we proposed computational models to map perceptual features to the perceptual texture space, which can suggest a procedural model to produce textures according to user-defined perceptual scales.

Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment 18F-FDG PET/CT Imaging.

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Beukinga, Roelof J; Hulshoff, Jan B; van Dijk, Lisanne V; Muijs, Christina T; Burgerhof, Johannes G M; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Slump, Cornelis H; Mul, Véronique E M; Plukker, John Th M

2017-05-01

Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is SUV max in 18 F-FDG PET/CT imaging. To improve the prediction of response, we constructed a model to predict complete response to nCRT in EC based on pretreatment clinical parameters and 18 F-FDG PET/CT-derived textural features. Methods: From a prospectively maintained single-institution database, we reviewed 97 consecutive patients with locally advanced EC and a pretreatment 18 F-FDG PET/CT scan between 2009 and 2015. All patients were treated with nCRT (carboplatin/paclitaxel/41.4 Gy) followed by esophagectomy. We analyzed clinical, geometric, and pretreatment textural features extracted from both 18 F-FDG PET and CT. The current most accurate prediction model with SUV max as a predictor variable was compared with 6 different response prediction models constructed using least absolute shrinkage and selection operator regularized logistic regression. Internal validation was performed to estimate the model's performances. Pathologic response was defined as complete versus incomplete response (Mandard tumor regression grade system 1 vs. 2-5). Results: Pathologic examination revealed 19 (19.6%) complete and 78 (80.4%) incomplete responders. Least absolute shrinkage and selection operator regularization selected the clinical parameters: histologic type and clinical T stage, the 18 F-FDG PET-derived textural feature long run low gray level emphasis, and the CT-derived textural feature run percentage. Introducing these variables to a logistic regression analysis showed areas under the receiver-operating-characteristic curve (AUCs) of 0.78 compared with 0.58 in the SUV max model. The discrimination slopes were 0.17 compared with 0.01, respectively. After internal validation, the AUCs decreased to 0.74 and 0.54, respectively. Conclusion

Biometric recognition via texture features of eye movement trajectories in a visual searching task.

Science.gov (United States)

Li, Chunyong; Xue, Jiguo; Quan, Cheng; Yue, Jingwei; Zhang, Chenggang

2018-01-01

Biometric recognition technology based on eye-movement dynamics has been in development for more than ten years. Different visual tasks, feature extraction and feature recognition methods are proposed to improve the performance of eye movement biometric system. However, the correct identification and verification rates, especially in long-term experiments, as well as the effects of visual tasks and eye trackers' temporal and spatial resolution are still the foremost considerations in eye movement biometrics. With a focus on these issues, we proposed a new visual searching task for eye movement data collection and a new class of eye movement features for biometric recognition. In order to demonstrate the improvement of this visual searching task being used in eye movement biometrics, three other eye movement feature extraction methods were also tested on our eye movement datasets. Compared with the original results, all three methods yielded better results as expected. In addition, the biometric performance of these four feature extraction methods was also compared using the equal error rate (EER) and Rank-1 identification rate (Rank-1 IR), and the texture features introduced in this paper were ultimately shown to offer some advantages with regard to long-term stability and robustness over time and spatial precision. Finally, the results of different combinations of these methods with a score-level fusion method indicated that multi-biometric methods perform better in most cases.

Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features

Science.gov (United States)

Qin, Xulei; Lu, Guolan; Sechopoulos, Ioannis; Fei, Baowei

2014-03-01

Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.

Textural features of 18F-FDG PET after two cycles of neoadjuvant chemotherapy can predict pCR in patients with locally advanced breast cancer.

Science.gov (United States)

Cheng, Lin; Zhang, Jianping; Wang, Yujie; Xu, Xiaoli; Zhang, Yongping; Zhang, Yingjian; Liu, Guangyu; Cheng, Jingyi

2017-08-01

This study was designed to evaluate the utility of textural features for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Sixty-one consecutive patients with locally advanced breast cancer underwent 18 F-FDG PET/CT scanning at baseline and after the second course of NAC. Changes to imaging parameters [maximum standardized uptake value (SUV max ), metabolic tumor volume (MTV), total lesion glycolysis (TLG)] and textural features (entropy, coarseness, skewness) between the 2 scans were measured by two independent radiologists. Pathological responses were reviewed by one pathologist, and the significance of the predictive value of each parameter was analyzed using a Chi-squared test. Receiver operating characteristic curve analysis was used to compare the area under the curve (AUC) for each parameter. pCR was observed more often in patients with HER2-positive tumors (22 patients) than in patients with HER2-negative tumors (5 patients) (71.0 vs. 16.7%, p textural features of 18 F-FDG PET images after two cycles of NAC are predictive of pCR in both HER2-negative and HER2-positive patients; this evidence warrants confirmation by further research.

Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing

Directory of Open Access Journals (Sweden)

Jaakko T. Astola

2005-05-01

Full Text Available Textural features are one of the most important types of useful information contained in images. In practice, these features are commonly masked by noise. Relatively little attention has been paid to texture preserving properties of noise attenuation methods. This stimulates solving the following tasks: (1 to analyze the texture preservation properties of various filters; and (2 to design image processing methods capable to preserve texture features well and to effectively reduce noise. This paper deals with examining texture feature preserving properties of different filters. The study is performed for a set of texture samples and different noise variances. The locally adaptive three-state schemes are proposed for which texture is considered as a particular class. For “detection” of texture regions, several classifiers are proposed and analyzed. As shown, an appropriate trade-off of the designed filter properties is provided. This is demonstrated quantitatively for artificial test images and is confirmed visually for real-life images.

Fabricating a multi-level barrier-integrated microfluidic device using grey-scale photolithography

International Nuclear Information System (INIS)

Nam, Yoonkwang; Kim, Minseok; Kim, Taesung

2013-01-01

Most polymer-replica-based microfluidic devices are mainly fabricated by using standard soft-lithography technology so that multi-level masters (MLMs) require multiple spin-coatings, mask alignments, exposures, developments, and bakings. In this paper, we describe a simple method for fabricating MLMs for planar microfluidic channels with multi-level barriers (MLBs). A single photomask is necessary for standard photolithography technology to create a polydimethylsiloxane grey-scale photomask (PGSP), which adjusts the total amount of UV absorption in a negative-tone photoresist via a wide range of dye concentrations. Since the PGSP in turn adjusts the degree of cross-linking of the photoresist, this method enables the fabrication of MLMs for an MLB-integrated microfluidic device. Since the PGSP-based soft-lithography technology provides a simple but powerful fabrication method for MLBs in a microfluidic device, we believe that the fabrication method can be widely used for micro total analysis systems that benefit from MLBs. We demonstrate an MLB-integrated microfluidic device that can separate microparticles. (paper)

Associations of Tumor PD-1 Ligands, Immunohistochemical Studies, and Textural Features in 18F-FDG PET in Squamous Cell Carcinoma of the Head and Neck.

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Chen, Rui-Yun; Lin, Ying-Chun; Shen, Wei-Chih; Hsieh, Te-Chun; Yen, Kuo-Yang; Chen, Shang-Wen; Kao, Chia-Hung

2018-01-08

To know tumor PD-L1 expression through IHC or the FDG-PET related radiomics, we investigated the association between programmed cell death protein 1 ligand (PD-L1) expression and immunohistochemical (IHC) biomarkers or textural features of 18F-fluoro-2-deoxdeoxyglucose positron emission tomography ( 18 F-FDG PET) in 53 oropharyngeal or hypopharyngeal cancer patients who were ready to undergo radiotherapy-based treatment. Differences in textural features or biomarkers between tumors with and without PD-L1 expression were tested using a Mann-Whitney U test. The predicted values for PD-L1 expression were examined using logistic regression analysis. The mean percentages of tumor PD-L1 expression were 6.2 ± 13.5. Eighteen tumors had PD-L1 expression ≥5%, whereas 30 tumors ≥1%. Using a 5% cutoff, the p16 staining percentage and the textural index of correlation were two factors associated with PD-L1 expression. The odds ratios (ORs) were 17.00 (p = 0.028) and 0.009 (p = 0.015), respectively. When dichotomizing PD-L1 at 1%, the p16 and Ki-67 staining percentages were two predictors for PD-L1 expression with ORs of 11.41 (p = 0.035) and 757.77 (p = 0.045). p16 and Ki-67 staining percentages and several PET/CT-derived textural features can provide supplemental information to determine tumor PD-L1 expression in HNCs.

Quantitative characterization of texture used for identification of eggs of bovine parasitic nematodes

DEFF Research Database (Denmark)

Sommer, C.

1998-01-01

This study investigates the use of texture, i.e. the grey level variation in digital images, as a basis for identification of strongylid eggs. Texture features were defined by algorithms applied to digital images of eggs from the bovine parasitic nematodes, Ostertagia ostertagi, Cooperia oncophora...... criterion based on these ten texture features, an average of 91.2% of eggs from the three species were correctly classified. All O. radiatum eggs were correctly classified, 11.8% of O. ostertagi and C. oncophora were reciprocally misclassified, and 2.9% of O. ostertagi were identified as O. radiatum. When...... the ten texture features were used singly an average of 51.2 to 37.9% of the species could be classified correctly. When texture was used together with the shape and size features, a higher percentage of eggs were correctly classified compared with the classification based on either texture, or shape...

Appearance and characterization of fruit image textures for quality sorting using wavelet transform and genetic algorithms.

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Khoje, Suchitra

2018-02-01

Images of four qualities of mangoes and guavas are evaluated for color and textural features to characterize and classify them, and to model the fruit appearance grading. The paper discusses three approaches to identify most discriminating texture features of both the fruits. In the first approach, fruit's color and texture features are selected using Mahalanobis distance. A total of 20 color features and 40 textural features are extracted for analysis. Using Mahalanobis distance and feature intercorrelation analyses, one best color feature (mean of a* [L*a*b* color space]) and two textural features (energy a*, contrast of H*) are selected as features for Guava while two best color features (R std, H std) and one textural features (energy b*) are selected as features for mangoes with the highest discriminate power. The second approach studies some common wavelet families for searching the best classification model for fruit quality grading. The wavelet features extracted from five basic mother wavelets (db, bior, rbior, Coif, Sym) are explored to characterize fruits texture appearance. In third approach, genetic algorithm is used to select only those color and wavelet texture features that are relevant to the separation of the class, from a large universe of features. The study shows that image color and texture features which were identified using a genetic algorithm can distinguish between various qualities classes of fruits. The experimental results showed that support vector machine classifier is elected for Guava grading with an accuracy of 97.61% and artificial neural network is elected from Mango grading with an accuracy of 95.65%. The proposed method is nondestructive fruit quality assessment method. The experimental results has proven that Genetic algorithm along with wavelet textures feature has potential to discriminate fruit quality. Finally, it can be concluded that discussed method is an accurate, reliable, and objective tool to determine fruit

AN ILLUMINATION INVARIANT TEXTURE BASED FACE RECOGNITION

Directory of Open Access Journals (Sweden)

K. Meena

2013-11-01

Full Text Available Automatic face recognition remains an interesting but challenging computer vision open problem. Poor illumination is considered as one of the major issue, since illumination changes cause large variation in the facial features. To resolve this, illumination normalization preprocessing techniques are employed in this paper to enhance the face recognition rate. The methods such as Histogram Equalization (HE, Gamma Intensity Correction (GIC, Normalization chain and Modified Homomorphic Filtering (MHF are used for preprocessing. Owing to great success, the texture features are commonly used for face recognition. But these features are severely affected by lighting changes. Hence texture based models Local Binary Pattern (LBP, Local Derivative Pattern (LDP, Local Texture Pattern (LTP and Local Tetra Patterns (LTrPs are experimented under different lighting conditions. In this paper, illumination invariant face recognition technique is developed based on the fusion of illumination preprocessing with local texture descriptors. The performance has been evaluated using YALE B and CMU-PIE databases containing more than 1500 images. The results demonstrate that MHF based normalization gives significant improvement in recognition rate for the face images with large illumination conditions.

SU-F-R-38: Impact of Smoothing and Noise On Robustness of CBCT Textural Features for Prediction of Response to Radiotherapy Treatment of Head and Neck Cancers

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Bagher-Ebadian, H; Chetty, I; Liu, C; Movsas, B; Siddiqui, F [Henry Ford Health System, Detroit, MI (United States)

2016-06-15

Purpose: To examine the impact of image smoothing and noise on the robustness of textural information extracted from CBCT images for prediction of radiotherapy response for patients with head/neck (H/N) cancers. Methods: CBCT image datasets for 14 patients with H/N cancer treated with radiation (70 Gy in 35 fractions) were investigated. A deformable registration algorithm was used to fuse planning CT’s to CBCT’s. Tumor volume was automatically segmented on each CBCT image dataset. Local control at 1-year was used to classify 8 patients as responders (R), and 6 as non-responders (NR). A smoothing filter [2D Adaptive Weiner (2DAW) with 3 different windows (ψ=3, 5, and 7)], and two noise models (Poisson and Gaussian, SNR=25) were implemented, and independently applied to CBCT images. Twenty-two textural features, describing the spatial arrangement of voxel intensities calculated from gray-level co-occurrence matrices, were extracted for all tumor volumes. Results: Relative to CBCT images without smoothing, none of 22 textural features extracted showed any significant differences when smoothing was applied (using the 2DAW with filtering parameters of ψ=3 and 5), in the responder and non-responder groups. When smoothing, 2DAW with ψ=7 was applied, one textural feature, Information Measure of Correlation, was significantly different relative to no smoothing. Only 4 features (Energy, Entropy, Homogeneity, and Maximum-Probability) were found to be statistically different between the R and NR groups (Table 1). These features remained statistically significant discriminators for R and NR groups in presence of noise and smoothing. Conclusion: This preliminary work suggests that textural classifiers for response prediction, extracted from H&N CBCT images, are robust to low-power noise and low-pass filtering. While other types of filters will alter the spatial frequencies differently, these results are promising. The current study is subject to Type II errors. A much

Extraction of Homogeneous Fine-Grained Texture Segments in Visual Images

Czech Academy of Sciences Publication Activity Database

Golcev, A.; Gritsenko, V.; Húsek, Dušan

2017-01-01

Roč. 27, č. 5 (2017), s. 447-477 ISSN 1210-0552 Institutional support: RVO:67985807 Keywords : texture feature * texture window * homogeneous fine-grained texture segment * extraction of texture segment * texture segmentation * ”float” coding method Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.394, year: 2016

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

Science.gov (United States)

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

1988-01-01

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

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

Science.gov (United States)

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

2017-10-01

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

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

Science.gov (United States)

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

2011-03-01

(18)F-FDG PET is often used in clinical routine for diagnosis, staging, and response to therapy assessment or prediction. The standardized uptake value (SUV) in the primary or regional area is the most common quantitative measurement derived from PET images used for those purposes. The aim of this study was to propose and evaluate new parameters obtained by textural analysis of baseline PET scans for the prediction of therapy response in esophageal cancer. Forty-one patients with newly diagnosed esophageal cancer treated with combined radiochemotherapy were included in this study. All patients underwent pretreatment whole-body (18)F-FDG PET. Patients were treated with radiotherapy and alkylatinlike agents (5-fluorouracil-cisplatin or 5-fluorouracil-carboplatin). Patients were classified as nonresponders (progressive or stable disease), partial responders, or complete responders according to the Response Evaluation Criteria in Solid Tumors. Different image-derived indices obtained from the pretreatment PET tumor images were considered. These included usual indices such as maximum SUV, peak SUV, and mean SUV and a total of 38 features (such as entropy, size, and magnitude of local and global heterogeneous and homogeneous tumor regions) extracted from the 5 different textures considered. The capacity of each parameter to classify patients with respect to response to therapy was assessed using the Kruskal-Wallis test (P textural analysis can provide nonresponder, partial-responder, and complete-responder patient identification with higher sensitivity (76%-92%) than any SUV measurement. Textural features of tumor metabolic distribution extracted from baseline (18)F-FDG PET images allow for the best stratification of esophageal carcinoma patients in the context of therapy-response prediction.

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

Energy Technology Data Exchange (ETDEWEB)

Lohmann, Philipp; Stoffels, Gabriele; Stegmayr, Carina; Neumaier, Bernd [Forschungszentrum Juelich, Institute of Neuroscience and Medicine, Juelich (Germany); Ceccon, Garry [University of Cologne, Department of Neurology, Cologne (Germany); Rapp, Marion; Sabel, Michael; Kamp, Marcel A. [Heinrich Heine University Duesseldorf, Department of Neurosurgery, Duesseldorf (Germany); Filss, Christian P. [Forschungszentrum Juelich, Institute of Neuroscience and Medicine, Juelich (Germany); RWTH Aachen University Hospital, Department of Nuclear Medicine, Aachen (Germany); Shah, Nadim J. [Forschungszentrum Juelich, Institute of Neuroscience and Medicine, Juelich (Germany); RWTH Aachen University Hospital, Department of Neurology, Aachen (Germany); Juelich-Aachen Research Alliance (JARA) - Section JARA-Brain, Department of Neurology, Juelich (Germany); Langen, Karl-Josef [Forschungszentrum Juelich, Institute of Neuroscience and Medicine, Juelich (Germany); RWTH Aachen University Hospital, Department of Nuclear Medicine, Aachen (Germany); Juelich-Aachen Research Alliance (JARA) - Section JARA-Brain, Department of Neurology, Juelich (Germany); Galldiks, Norbert [Forschungszentrum Juelich, Institute of Neuroscience and Medicine, Juelich (Germany); University of Cologne, Department of Neurology, Cologne (Germany); University of Cologne, Center of Integrated Oncology (CIO), Cologne (Germany)

2017-07-15

We investigated the potential of textural feature analysis of O-(2-[{sup 18}F]fluoroethyl)-L-tyrosine ({sup 18}F-FET) PET to differentiate radiation injury from brain metastasis recurrence. Forty-seven patients with contrast-enhancing brain lesions (n = 54) on MRI after radiotherapy of brain metastases underwent dynamic {sup 18}F-FET PET. Tumour-to-brain ratios (TBRs) of {sup 18}F-FET uptake and 62 textural parameters were determined on summed images 20-40 min post-injection. Tracer uptake kinetics, i.e., time-to-peak (TTP) and patterns of time-activity curves (TAC) were evaluated on dynamic PET data from 0-50 min post-injection. Diagnostic accuracy of investigated parameters and combinations thereof to discriminate between brain metastasis recurrence and radiation injury was compared. Diagnostic accuracy increased from 81 % for TBR{sub mean} alone to 85 % when combined with the textural parameter Coarseness or Short-zone emphasis. The accuracy of TBR{sub max} alone was 83 % and increased to 85 % after combination with the textural parameters Coarseness, Short-zone emphasis, or Correlation. Analysis of TACs resulted in an accuracy of 70 % for kinetic pattern alone and increased to 83 % when combined with TBR{sub max}. Textural feature analysis in combination with TBRs may have the potential to increase diagnostic accuracy for discrimination between brain metastasis recurrence and radiation injury, without the need for dynamic {sup 18}F-FET PET scans. (orig.)

Topological patterns of mesh textures in serpentinites

Science.gov (United States)

Miyazawa, M.; Suzuki, A.; Shimizu, H.; Okamoto, A.; Hiraoka, Y.; Obayashi, I.; Tsuji, T.; Ito, T.

2017-12-01

Serpentinization is a hydration process that forms serpentine minerals and magnetite within the oceanic lithosphere. Microfractures crosscut these minerals during the reactions, and the structures look like mesh textures. It has been known that the patterns of microfractures and the system evolutions are affected by the hydration reaction and fluid transport in fractures and within matrices. This study aims at quantifying the topological patterns of the mesh textures and understanding possible conditions of fluid transport and reaction during serpentinization in the oceanic lithosphere. Two-dimensional simulation by the distinct element method (DEM) generates fracture patterns due to serpentinization. The microfracture patterns are evaluated by persistent homology, which measures features of connected components of a topological space and encodes multi-scale topological features in the persistence diagrams. The persistence diagrams of the different mesh textures are evaluated by principal component analysis to bring out the strong patterns of persistence diagrams. This approach help extract feature values of fracture patterns from high-dimensional and complex datasets.

Statistical representative elementary volumes of porous media determined using greyscale analysis of 3D tomograms

Science.gov (United States)

Bruns, S.; Stipp, S. L. S.; Sørensen, H. O.

2017-09-01

Digital rock physics carries the dogmatic concept of having to segment volume images for quantitative analysis but segmentation rejects huge amounts of signal information. Information that is essential for the analysis of difficult and marginally resolved samples, such as materials with very small features, is lost during segmentation. In X-ray nanotomography reconstructions of Hod chalk we observed partial volume voxels with an abundance that limits segmentation based analysis. Therefore, we investigated the suitability of greyscale analysis for establishing statistical representative elementary volumes (sREV) for the important petrophysical parameters of this type of chalk, namely porosity, specific surface area and diffusive tortuosity, by using volume images without segmenting the datasets. Instead, grey level intensities were transformed to a voxel level porosity estimate using a Gaussian mixture model. A simple model assumption was made that allowed formulating a two point correlation function for surface area estimates using Bayes' theory. The same assumption enables random walk simulations in the presence of severe partial volume effects. The established sREVs illustrate that in compacted chalk, these simulations cannot be performed in binary representations without increasing the resolution of the imaging system to a point where the spatial restrictions of the represented sample volume render the precision of the measurement unacceptable. We illustrate this by analyzing the origins of variance in the quantitative analysis of volume images, i.e. resolution dependence and intersample and intrasample variance. Although we cannot make any claims on the accuracy of the approach, eliminating the segmentation step from the analysis enables comparative studies with higher precision and repeatability.

TEXTURAL DESCRIPTORS FOR MULTIPHASIC ORE PARTICLES

Directory of Open Access Journals (Sweden)

Laura Pérez-Barnuevo

2012-11-01

Full Text Available Monitoring of mineral processing circuits by means of particle liberation analysis through quantitative image analysis has become a routine technique within the last decades. Usually, liberation indices are computed as weight proportions, which is not informative enough when complex texture ores are treated by flotation. In these cases, liberation has to be computed as phase surface exposed to reactants, and textural relationships between minerals have to be characterized to determine the possibility of increasing exposure. In this paper, some indices to achieve a complete texture characterization have been developed in terms of 2D phase contact and mineral surfaces exposure. Indices suggested by other authors are also compared. The response of this set of parameters against textural changes has been explored on simple synthetic textures ranging from single to multiple inclusions and single to multiple veins and their ability to discriminate between different textural features is analyzed over real mineral particles with known internal structure.

Adaptive Texture Synthesis for Large Scale City Modeling

Science.gov (United States)

Despine, G.; Colleu, T.

2015-02-01

Large scale city models textured with aerial images are well suited for bird-eye navigation but generally the image resolution does not allow pedestrian navigation. One solution to face this problem is to use high resolution terrestrial photos but it requires huge amount of manual work to remove occlusions. Another solution is to synthesize generic textures with a set of procedural rules and elementary patterns like bricks, roof tiles, doors and windows. This solution may give realistic textures but with no correlation to the ground truth. Instead of using pure procedural modelling we present a method to extract information from aerial images and adapt the texture synthesis to each building. We describe a workflow allowing the user to drive the information extraction and to select the appropriate texture patterns. We also emphasize the importance to organize the knowledge about elementary pattern in a texture catalogue allowing attaching physical information, semantic attributes and to execute selection requests. Roofs are processed according to the detected building material. Façades are first described in terms of principal colours, then opening positions are detected and some window features are computed. These features allow selecting the most appropriate patterns from the texture catalogue. We experimented this workflow on two samples with 20 cm and 5 cm resolution images. The roof texture synthesis and opening detection were successfully conducted on hundreds of buildings. The window characterization is still sensitive to the distortions inherent to the projection of aerial images onto the facades.

Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients.

Science.gov (United States)

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

2016-01-01

Although change in standardized uptake value (SUV) measures and PET-based textural features during treatment have shown promise in tumor response prediction, it is unclear which quantitative measure is the most predictive. We compared the relationship between PET-based features and pathologic response and overall survival with the SUV measures in esophageal cancer. Fifty-four esophageal cancer patients received PET/CT scans before and after chemoradiotherapy. Of these, 45 patients underwent surgery and were classified into complete, partial, and non-responders to the preoperative chemoradiation. SUVmax and SUVmean, two cooccurrence matrix (Entropy and Homogeneity), two run-length matrix (RLM) (high-gray-run emphasis and Short-run high-gray-run emphasis), and two size-zone matrix (high-gray-zone emphasis and short-zone high-gray emphasis) textures were computed. The relationship between the relative difference of each measure at different treatment time points and the pathologic response and overall survival was assessed using the area under the receiver-operating-characteristic curve (AUC) and Kaplan-Meier statistics, respectively. All Textures, except Homogeneity, were better related to pathologic response than SUVmax and SUVmean. Entropy was found to significantly distinguish non-responders from the complete (AUC = 0.79, p = 1.7 × 10(-4)) and partial (AUC = 0.71, p = 0.01) responders. Non-responders can also be significantly differentiated from partial and complete responders by the change in the run-length and size-zone matrix textures (AUC = 0.71-0.76, p ≤ 0.02). Homogeneity, SUVmax, and SUVmean failed to differentiate between any of the responders (AUC = 0.50-0.57, p ≥ 0.46). However, none of the measures were found to significantly distinguish between complete and partial responders with AUC textures significantly discriminated patients with good and poor survival (log-rank p textures and survival were poorly related

Automatic inspection of textured surfaces by support vector machines

Science.gov (United States)

Jahanbin, Sina; Bovik, Alan C.; Pérez, Eduardo; Nair, Dinesh

2009-08-01

Automatic inspection of manufactured products with natural looking textures is a challenging task. Products such as tiles, textile, leather, and lumber project image textures that cannot be modeled as periodic or otherwise regular; therefore, a stochastic modeling of local intensity distribution is required. An inspection system to replace human inspectors should be flexible in detecting flaws such as scratches, cracks, and stains occurring in various shapes and sizes that have never been seen before. A computer vision algorithm is proposed in this paper that extracts local statistical features from grey-level texture images decomposed with wavelet frames into subbands of various orientations and scales. The local features extracted are second order statistics derived from grey-level co-occurrence matrices. Subsequently, a support vector machine (SVM) classifier is trained to learn a general description of normal texture from defect-free samples. This algorithm is implemented in LabVIEW and is capable of processing natural texture images in real-time.

Conical surface textures formed by ion bombarding 2% Be-Cu alloy

International Nuclear Information System (INIS)

Panitz, J.K.G.

1991-01-01

A homogeneous, micrometer-sized conical surface texture forms on 2% Be-Cu alloy which is bombarded with an argon beam produced by a Kaufman ion source. The dimensions of the features that form depend strongly on argon energy (from 250 to 1500 eV); argon fluence (10 19 to 10 20 ions cm -2 ); and argon flux (0.1 to 1 mA cm -2 ). The texture morphology depends less strongly on the background ambient (Mo versus graphite), earlier alloy heat treatments and the temperature during bombardment (100 o C and 450 o C). As the texture matures with increasing fluence, the number of large features increases at the expense of the number of small features. The observed relationship between texture formation and ion flux suggests that the evolution of these features is not adequately described by theories predicting that the mature conical side-wall angle is related to the angle of the maximum sputtering yield. These textured surfaces can be coated with other metals for a variety of possible applications including pulsed power Li + beam anodes; cold cathode field emission devices; optical absorbers and catalysis supports. (author)

Conical surface textures formed by ion bombarding 2% Be Cu alloy

International Nuclear Information System (INIS)

Panitz, J.K.G.

1990-01-01

A homogeneous, micrometer-sized conical surface texture forms on 2% Be-Cu alloy which is bombarded with an argon beam produced by a Kaufman ion source. The dimensions of the features that form strongly depend on: (1) argon energy (from 250 to 1500 eV), (2) fluence (10 19 to 10 20 ions/cm 2 ), and (3) flux (0.1 to 1 mA/cm 2 ). The texture morphology depends less strongly on the background ambient (Mo vs graphite), earlier alloy heat treatments and the temperature during bombardment (100 degree C and 450 degree C). As the texture matures with increasing fluence, the number of large features increases at the expense of the number of small features. The observed relationship between texture formation and ion flux suggests that the evolution of these features is not adequately described by theories predicting that the mature conical sidewall angle is related to the angle of the maximum sputtering yield. These textured surfaces can be coated with other metals for a variety of possible applications including: (1) pulsed power Li+ beam anodes, (2) cold cathode field emission devices, (3) optical absorbers and (4) catalysis supports. 18 refs., 5 figs

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

Science.gov (United States)

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

2017-11-01

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

Biometric feature extraction using local fractal auto-correlation

International Nuclear Information System (INIS)

Chen Xi; Zhang Jia-Shu

2014-01-01

Image texture feature extraction is a classical means for biometric recognition. To extract effective texture feature for matching, we utilize local fractal auto-correlation to construct an effective image texture descriptor. Three main steps are involved in the proposed scheme: (i) using two-dimensional Gabor filter to extract the texture features of biometric images; (ii) calculating the local fractal dimension of Gabor feature under different orientations and scales using fractal auto-correlation algorithm; and (iii) linking the local fractal dimension of Gabor feature under different orientations and scales into a big vector for matching. Experiments and analyses show our proposed scheme is an efficient biometric feature extraction approach. (condensed matter: structural, mechanical, and thermal properties)

Texture, microstructure, and fractal features of the low-cycle fatigue failure of the metal in pipeline welded joints

Science.gov (United States)

Usov, V. V.; Gopkalo, E. E.; Shkatulyak, N. M.; Gopkalo, A. P.; Cherneva, T. S.

2015-09-01

Crystallographic texture and fracture features are studied after low-cycle fatigue tests of laboratory specimens cut from the base metal and the characteristic zones of a welded joint in a pipeline after its longterm operation. The fractal dimensions of fracture surfaces are determined. The fractal dimension is shown to increase during the transition from ductile to quasi-brittle fracture, and a relation between the fractal dimension of a fracture surface and the fatigue life of the specimen is found.

Texture analysis using Gabor wavelets

Science.gov (United States)

Naghdy, Golshah A.; Wang, Jian; Ogunbona, Philip O.

1996-04-01

Receptive field profiles of simple cells in the visual cortex have been shown to resemble even- symmetric or odd-symmetric Gabor filters. Computational models employed in the analysis of textures have been motivated by two-dimensional Gabor functions arranged in a multi-channel architecture. More recently wavelets have emerged as a powerful tool for non-stationary signal analysis capable of encoding scale-space information efficiently. A multi-resolution implementation in the form of a dyadic decomposition of the signal of interest has been popularized by many researchers. In this paper, Gabor wavelet configured in a 'rosette' fashion is used as a multi-channel filter-bank feature extractor for texture classification. The 'rosette' spans 360 degrees of orientation and covers frequencies from dc. In the proposed algorithm, the texture images are decomposed by the Gabor wavelet configuration and the feature vectors corresponding to the mean of the outputs of the multi-channel filters extracted. A minimum distance classifier is used in the classification procedure. As a comparison the Gabor filter has been used to classify the same texture images from the Brodatz album and the results indicate the superior discriminatory characteristics of the Gabor wavelet. With the test images used it can be concluded that the Gabor wavelet model is a better approximation of the cortical cell receptive field profiles.

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

Science.gov (United States)

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

2017-10-01

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

A cosmic microwave background feature consistent with a cosmic texture.

Science.gov (United States)

Cruz, M; Turok, N; Vielva, P; Martínez-González, E; Hobson, M

2007-12-07

The Cosmic Microwave Background provides our most ancient image of the universe and our best tool for studying its early evolution. Theories of high-energy physics predict the formation of various types of topological defects in the very early universe, including cosmic texture, which would generate hot and cold spots in the Cosmic Microwave Background. We show through a Bayesian statistical analysis that the most prominent 5 degrees -radius cold spot observed in all-sky images, which is otherwise hard to explain, is compatible with having being caused by a texture. From this model, we constrain the fundamental symmetry-breaking energy scale to be (0) approximately 8.7 x 10(15) gigaelectron volts. If confirmed, this detection of a cosmic defect will probe physics at energies exceeding any conceivable terrestrial experiment.

Texture, Color, and Sensory Features of Low-Sugar Gooseberry Jams Enriched with Plant Ingredients with Prohealth Properties

Directory of Open Access Journals (Sweden)

Anna Banaś

2018-01-01

Full Text Available The aim of this research was to evaluate texture, color, and sensory parameters of low-sugar gooseberry jams with added black chokeberry, elderberry, Japanese quince, flax seeds, wheat germ, and inulin. The jams were stored at two temperatures of 10°C and 20°C. The highest gel strength (Fe was recorded in the jams with wheat germ (2.75 N, flax seeds (2.74 N, and inulin (1.95 N. The brightest color L⁎ was noted in the gooseberry jams enriched with flax seeds and wheat germ, while the darkest color was noted in those with added black chokeberry and elderberry fruit. In the sensory evaluation, the gooseberry jam without plant ingredients, along with the products enriched with black chokeberry, elderberry, and inulin, scored high at almost 5 on a 5-point scale. The remaining jams had scores of 4.4–4.8 points. Cool storage of jams had a better effect on color and texture, while sensory features were affected to a lesser degree.

Nonuniform multiview color texture mapping of image sequence and three-dimensional model for faded cultural relics with sift feature points

Science.gov (United States)

Li, Na; Gong, Xingyu; Li, Hongan; Jia, Pengtao

2018-01-01

For faded relics, such as Terracotta Army, the 2D-3D registration between an optical camera and point cloud model is an important part for color texture reconstruction and further applications. This paper proposes a nonuniform multiview color texture mapping for the image sequence and the three-dimensional (3D) model of point cloud collected by Handyscan3D. We first introduce nonuniform multiview calibration, including the explanation of its algorithm principle and the analysis of its advantages. We then establish transformation equations based on sift feature points for the multiview image sequence. At the same time, the selection of nonuniform multiview sift feature points is introduced in detail. Finally, the solving process of the collinear equations based on multiview perspective projection is given with three steps and the flowchart. In the experiment, this method is applied to the color reconstruction of the kneeling figurine, Tangsancai lady, and general figurine. These results demonstrate that the proposed method provides an effective support for the color reconstruction of the faded cultural relics and be able to improve the accuracy of 2D-3D registration between the image sequence and the point cloud model.

ADAPTIVE TEXTURE SYNTHESIS FOR LARGE SCALE CITY MODELING

Directory of Open Access Journals (Sweden)

G. Despine

2015-02-01

Full Text Available Large scale city models textured with aerial images are well suited for bird-eye navigation but generally the image resolution does not allow pedestrian navigation. One solution to face this problem is to use high resolution terrestrial photos but it requires huge amount of manual work to remove occlusions. Another solution is to synthesize generic textures with a set of procedural rules and elementary patterns like bricks, roof tiles, doors and windows. This solution may give realistic textures but with no correlation to the ground truth. Instead of using pure procedural modelling we present a method to extract information from aerial images and adapt the texture synthesis to each building. We describe a workflow allowing the user to drive the information extraction and to select the appropriate texture patterns. We also emphasize the importance to organize the knowledge about elementary pattern in a texture catalogue allowing attaching physical information, semantic attributes and to execute selection requests. Roofs are processed according to the detected building material. Façades are first described in terms of principal colours, then opening positions are detected and some window features are computed. These features allow selecting the most appropriate patterns from the texture catalogue. We experimented this workflow on two samples with 20 cm and 5 cm resolution images. The roof texture synthesis and opening detection were successfully conducted on hundreds of buildings. The window characterization is still sensitive to the distortions inherent to the projection of aerial images onto the facades.

Fast Image Texture Classification Using Decision Trees

Science.gov (United States)

Thompson, David R.

2011-01-01

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

Effect of Q-switched Laser Surface Texturing of Titanium on Osteoblast Cell Response

Science.gov (United States)

Voisey, K. T.; Scotchford, C. A.; Martin, L.; Gill, H. S.

Titanium and its alloys are important biomedical materials. It is known that the surface texture of implanted medical devices affects cell response. Control of cell response has the potential to enhance fixation of implants into bone and, in other applications, to prevent undesired cell adhesion. The potential use of a 100W Q-switched YAG laser miller (DMG Lasertec 60 HSC) for texturing titanium is investigated. A series of regular features with dimensions of the order of tens of micrometers are generated in the surface of titanium samples and the cell response to these features is determined. Characterisation of the laser milled features reveals features with a lengthscale of a few microns superposed on the larger scale structures, this is attributed to resolidification of molten droplets generated and propelled over the surface by individual laser pulses. The laser textured samples are exposed to osteoblast cells and it is seen that cells do respond to the features in the laser textured surfaces.

EFFECTIVE MULTI-RESOLUTION TRANSFORM IDENTIFICATION FOR CHARACTERIZATION AND CLASSIFICATION OF TEXTURE GROUPS

Directory of Open Access Journals (Sweden)

S. Arivazhagan

2011-11-01

Full Text Available Texture classification is important in applications of computer image analysis for characterization or classification of images based on local spatial variations of intensity or color. Texture can be defined as consisting of mutually related elements. This paper proposes an experimental approach for identification of suitable multi-resolution transform for characterization and classification of different texture groups based on statistical and co-occurrence features derived from multi-resolution transformed sub bands. The statistical and co-occurrence feature sets are extracted for various multi-resolution transforms such as Discrete Wavelet Transform (DWT, Stationary Wavelet Transform (SWT, Double Density Wavelet Transform (DDWT and Dual Tree Complex Wavelet Transform (DTCWT and then, the transform that maximizes the texture classification performance for the particular texture group is identified.

Filter and Filter Bank Design for Image Texture Recognition

Energy Technology Data Exchange (ETDEWEB)

Randen, Trygve

1997-12-31

The relevance of this thesis to energy and environment lies in its application to remote sensing such as for instance sea floor mapping and seismic pattern recognition. The focus is on the design of two-dimensional filters for feature extraction, segmentation, and classification of digital images with textural content. The features are extracted by filtering with a linear filter and estimating the local energy in the filter response. The thesis gives a review covering broadly most previous approaches to texture feature extraction and continues with proposals of some new techniques. 143 refs., 59 figs., 7 tabs.

Hardwood species classification with DWT based hybrid texture ...

Indian Academy of Sciences (India)

to decompose the image up to 7 levels using Daubechies (db3) wavelet as decom ... binary pattern (DWTFOSLBPu2) texture features at the 4th level of image decomposi ...... In addition, inclusion of further levels of image decomposition gives rise to ..... Texture analysis of SAR sea ice imagery using gray level co-occurrence.

Poor textural image tie point matching via graph theory

Science.gov (United States)

Yuan, Xiuxiao; Chen, Shiyu; Yuan, Wei; Cai, Yang

2017-07-01

Feature matching aims to find corresponding points to serve as tie points between images. Robust matching is still a challenging task when input images are characterized by low contrast or contain repetitive patterns, occlusions, or homogeneous textures. In this paper, a novel feature matching algorithm based on graph theory is proposed. This algorithm integrates both geometric and radiometric constraints into an edge-weighted (EW) affinity tensor. Tie points are then obtained by high-order graph matching. Four pairs of poor textural images covering forests, deserts, bare lands, and urban areas are tested. For comparison, three state-of-the-art matching techniques, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), and features from accelerated segment test (FAST), are also used. The experimental results show that the matching recall obtained by SIFT, SURF, and FAST varies from 0 to 35% in different types of poor textures. However, through the integration of both geometry and radiometry and the EW strategy, the recall obtained by the proposed algorithm is better than 50% in all four image pairs. The better matching recall improves the number of correct matches, dispersion, and positional accuracy.

Diagnosis and prognosis of Ostheoarthritis by texture analysis using sparse linear models

DEFF Research Database (Denmark)

Marques, Joselene; Clemmensen, Line Katrine Harder; Dam, Erik

We present a texture analysis methodology that combines uncommitted machine-learning techniques and sparse feature transformation methods in a fully automatic framework. We compare the performances of a partial least squares (PLS) forward feature selection strategy to a hard threshold sparse PLS...... algorithm and a sparse linear discriminant model. The texture analysis framework was applied to diagnosis of knee osteoarthritis (OA) and prognosis of cartilage loss. For this investigation, a generic texture feature bank was extracted from magnetic resonance images of tibial knee bone. The features were...... used as input to the sparse algorithms, which dened the best features to retain in the model. To cope with the limited number of samples, the data was evaluated using 10 fold cross validation (CV). The diagnosis evaluation using sparse PLS reached a generalization area-under-the-ROC curve (AUC) of 0...

TU-F-CAMPUS-J-02: Evaluation of Textural Feature Extraction for Radiotherapy Response Assessment of Early Stage Breast Cancer Patients Using Diffusion Weighted MRI and Dynamic Contrast Enhanced MRI

Energy Technology Data Exchange (ETDEWEB)

Xie, Y; Wang, C; Horton, J; Chang, Z [Duke University Medical Center, Durham, NC (United States)

2015-06-15

Purpose: To investigate the feasibility of using classic textural feature extraction in radiotherapy response assessment, we studied a unique cohort of early stage breast cancer patients with paired pre - and post-radiation Diffusion Weighted MRI (DWI-MRI) and Dynamic Contrast Enhanced MRI (DCE-MRI). Methods: 15 female patients from our prospective phase I trial evaluating preoperative radiotherapy were included in this retrospective study. Each patient received a single-fraction radiation treatment, and DWI and DCE scans were conducted before and after the radiotherapy. DWI scans were acquired using a spin-echo EPI sequence with diffusion weighting factors of b = 0 and b = 500 mm{sup 2} /s, and the apparent diffusion coefficient (ADC) maps were calculated. DCE-MRI scans were acquired using a T{sub 1}-weighted 3D SPGR sequence with a temporal resolution of about 1 minute. The contrast agent (CA) was intravenously injected with a 0.1 mmol/kg bodyweight dose at 2 ml/s. Two parameters, volume transfer constant (K{sup trans} ) and k{sub ep} were analyzed using the two-compartment Tofts kinetic model. For DCE parametric maps and ADC maps, 33 textural features were generated from the clinical target volume (CTV) in a 3D fashion using the classic gray level co-occurrence matrix (GLCOM) and gray level run length matrix (GLRLM). Wilcoxon signed-rank test was used to determine the significance of each texture feature’s change after the radiotherapy. The significance was set to 0.05 with Bonferroni correction. Results: For ADC maps calculated from DWI-MRI, 24 out of 33 CTV features changed significantly after the radiotherapy. For DCE-MRI pharmacokinetic parameters, all 33 CTV features of K{sup trans} and 33 features of k{sub ep} changed significantly. Conclusion: Initial results indicate that those significantly changed classic texture features are sensitive to radiation-induced changes and can be used for assessment of radiotherapy response in breast cancer.

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

Science.gov (United States)

An, Jubai; Zhu, Lisong

2006-01-01

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

The promise and limits of PET texture analysis.

Science.gov (United States)

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

2013-11-01

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

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

Science.gov (United States)

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 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 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.

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

Science.gov (United States)

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.

Correlation of pretreatment 18F-FDG PET tumor textural features with gene expression in pharyngeal cancer and implications for radiotherapy-based treatment outcomes.

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Chen, Shang-Wen; Shen, Wei-Chih; Lin, Ying-Chun; Chen, Rui-Yun; Hsieh, Te-Chun; Yen, Kuo-Yang; Kao, Chia-Hung

2017-04-01

This study investigated the correlation of the matrix heterogeneity of tumors on 18 F-fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) with gene-expression profiling in patients with pharyngeal cancer and determined the prognostic factors for radiotherapy-based treatment outcomes. We retrospectively reviewed the records of 57 patients with stage III-IV oropharyngeal or hypopharyngeal cancer who had completed definitive therapy. Four groups of the textural features as well as 31 indices were studied in addition to maximum standard uptake value, metastatic tumor volume, and total lesion glycolysis. Immunohistochemical data from pretreatment biopsy specimens (Glut1, CAIX, VEGF, HIF-1α, EGFR, Ki-67, Bcl-2, CLAUDIN-4, YAP-1, c-Met, and p16) were analyzed. The relationships between the indices and genomic expression were studied, and the robustness of various textural features relative to cause-specific survival and primary relapse-free survival was analyzed. The overexpression of VEGF was positively associated with the increased values of the matrix heterogeneity obtained using gray-level nonuniformity for zone (GLNUz) and run-length nonuniformity (RLNU). Advanced T stage (p = 0.01, hazard ratio [HR] = 3.38), a VEGF immunoreactive score of >2 (p = 0.03, HR = 2.79), and a higher GLNUz value (p = 0.04, HR = 2.51) were prognostic factors for low cause-specific survival, whereas advanced T stage, a HIF-1α staining percentage of ≥80%, and a higher GLNUz value were prognostic factors for low primary-relapse free survival. The overexpression of VEGF was associated with the increased matrix index of GLNUz and RLNU. For patients with pharyngeal cancer requiring radiotherapy, the treatment outcome can be stratified according to the textural features, T stage, and biomarkers.

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

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

2018-04-01

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

The analysis of image feature robustness using cometcloud

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Xin Qi

2012-01-01

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

A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

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Huai Yu

2016-03-01

Full Text Available Scene classification plays an important role in understanding high-resolution satellite (HRS remotely sensed imagery. For remotely sensed scenes, both color information and texture information provide the discriminative ability in classification tasks. In recent years, substantial performance gains in HRS image classification have been reported in the literature. One branch of research combines multiple complementary features based on various aspects such as texture, color and structure. Two methods are commonly used to combine these features: early fusion and late fusion. In this paper, we propose combining the two methods under a tree of regions and present a new descriptor to encode color, texture and structure features using a hierarchical structure-Color Binary Partition Tree (CBPT, which we call the CTS descriptor. Specifically, we first build the hierarchical representation of HRS imagery using the CBPT. Then we quantize the texture and color features of dense regions. Next, we analyze and extract the co-occurrence patterns of regions based on the hierarchical structure. Finally, we encode local descriptors to obtain the final CTS descriptor and test its discriminative capability using object categorization and scene classification with HRS images. The proposed descriptor contains the spectral, textural and structural information of the HRS imagery and is also robust to changes in illuminant color, scale, orientation and contrast. The experimental results demonstrate that the proposed CTS descriptor achieves competitive classification results compared with state-of-the-art algorithms.

Heterogeneous patterns enhancing static and dynamic texture classification

International Nuclear Information System (INIS)

Silva, Núbia Rosa da; Martinez Bruno, Odemir

2013-01-01

Some mixtures, such as colloids like milk, blood, and gelatin, have homogeneous appearance when viewed with the naked eye, however, to observe them at the nanoscale is possible to understand the heterogeneity of its components. The same phenomenon can occur in pattern recognition in which it is possible to see heterogeneous patterns in texture images. However, current methods of texture analysis can not adequately describe such heterogeneous patterns. Common methods used by researchers analyse the image information in a global way, taking all its features in an integrated manner. Furthermore, multi-scale analysis verifies the patterns at different scales, but still preserving the homogeneous analysis. On the other hand various methods use textons to represent the texture, breaking texture down into its smallest unit. To tackle this problem, we propose a method to identify texture patterns not small as textons at distinct scales enhancing the separability among different types of texture. We find sub patterns of texture according to the scale and then group similar patterns for a more refined analysis. Tests were performed in four static texture databases and one dynamical one. Results show that our method provide better classification rate compared with conventional approaches both in static and in dynamic texture.

SU-F-R-17: Advancing Glioblastoma Multiforme (GBM) Recurrence Detection with MRI Image Texture Feature Extraction and Machine Learning

Energy Technology Data Exchange (ETDEWEB)

Yu, V; Ruan, D; Nguyen, D; Kaprealian, T; Chin, R; Sheng, K [UCLA School of Medicine, Los Angeles, CA (United States)

2016-06-15

Purpose: To test the potential of early Glioblastoma Multiforme (GBM) recurrence detection utilizing image texture pattern analysis in serial MR images post primary treatment intervention. Methods: MR image-sets of six time points prior to the confirmed recurrence diagnosis of a GBM patient were included in this study, with each time point containing T1 pre-contrast, T1 post-contrast, T2-Flair, and T2-TSE images. Eight Gray-level co-occurrence matrix (GLCM) texture features including Contrast, Correlation, Dissimilarity, Energy, Entropy, Homogeneity, Sum-Average, and Variance were calculated from all images, resulting in a total of 32 features at each time point. A confirmed recurrent volume was contoured, along with an adjacent non-recurrent region-of-interest (ROI) and both volumes were propagated to all prior time points via deformable image registration. A support vector machine (SVM) with radial-basis-function kernels was trained on the latest time point prior to the confirmed recurrence to construct a model for recurrence classification. The SVM model was then applied to all prior time points and the volumes classified as recurrence were obtained. Results: An increase in classified volume was observed over time as expected. The size of classified recurrence maintained at a stable level of approximately 0.1 cm{sup 3} up to 272 days prior to confirmation. Noticeable volume increase to 0.44 cm{sup 3} was demonstrated at 96 days prior, followed by significant increase to 1.57 cm{sup 3} at 42 days prior. Visualization of the classified volume shows the merging of recurrence-susceptible region as the volume change became noticeable. Conclusion: Image texture pattern analysis in serial MR images appears to be sensitive to detecting the recurrent GBM a long time before the recurrence is confirmed by a radiologist. The early detection may improve the efficacy of targeted intervention including radiosurgery. More patient cases will be included to create a generalizable

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

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Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

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

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

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Tae-Yun Kim

2014-01-01

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

Relationship between the temporal changes in positron-emission-tomography-imaging-based textural features and pathologic response and survival in esophageal cancer patients

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Stephen ShingFan Yip

2016-03-01

Full Text Available Purpose: Although change in SUV measures and PET-based textural features during treatment have shown promise in tumor response prediction, it is unclear which quantitative measure is the most predictive. We compared the relationship between PET-based features and pathologic response and overall survival with the SUV measures in esophageal cancer. Methods: Fifty-four esophageal cancer patients received PET/CT scans before and after chemo-radiotherapy. Of these, 45 patients underwent surgery and were classified into complete, partial, and non-responders to the preoperative chemoradiation. SUVmax and SUVmean, two co-occurrence matrix (Entropy and Homogeneity, two run-length-matrix (High-gray-run-emphasis and Short-run-high-gray-run-emphasis, and two size-zone-matrix (High-gray-zone-emphasis and Short-zone-high-gray-emphasis textures were computed. The relationship between the relative difference of each measure at different treatment time points and the pathologic response and overall survival was assessed using the area under the receiver-operating-characteristic curve (AUC and Kaplan-Meier statistics respectively. Results: All Textures, except Homogeneity, were better related to pathologic response than SUVmax and SUVmean. Entropy was found to significantly distinguish non-responders from the complete (AUC=0.79, p=1.7x10^-4 and partial (AUC=0.71, p=0.01 responders. Non-responders can also be significantly differentiated from partial and complete responders by the change in the run length and size zone matrix textures (AUC=0.71‒0.76, p≤0.02. Homogeneity, SUVmax and SUVmean failed to differentiate between any of the responders (AUC=0.50‒0.57, p≥0.46. However, none of the measures were found to significantly distinguish between complete and partial responders with AUC0.25. Conclusions: For the patients studied, temporal change in Entropy and all Run length matrix were better correlated with pathological response and survival than the SUV

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

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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.

Multi Voxel Descriptor for 3D Texture Retrieval

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Hero Yudo Martono

2016-08-01

Full Text Available In this paper, we present a new feature descriptors  which exploit voxels for 3D textured retrieval system when models vary either by geometric shape or texture or both. First, we perform pose normalisation to modify arbitrary 3D models  in order to have same orientation. We then map the structure of 3D models into voxels. This purposes to make all the 3D models have the same dimensions. Through this voxels, we can capture information from a number of ways.  First, we build biner voxel histogram and color voxel histogram.  Second, we compute distance from centre voxel into other voxels and generate histogram. Then we also compute fourier transform in spectral space.  For capturing texture feature, we apply voxel tetra pattern. Finally, we merge all features by linear combination. For experiment, we use standard evaluation measures such as Nearest Neighbor (NN, First Tier (FT, Second Tier (ST, Average Dynamic Recall (ADR. Dataset in SHREC 2014  and its evaluation program is used to verify the proposed method. Experiment result show that the proposed method  is more accurate when compared with some methods of state-of-the-art.

Temporal resolution of orientation-defined texture segregation: a VEP study.

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Lachapelle, Julie; McKerral, Michelle; Jauffret, Colin; Bach, Michael

2008-09-01

Orientation is one of the visual dimensions that subserve figure-ground discrimination. A spatial gradient in orientation leads to "texture segregation", which is thought to be concurrent parallel processing across the visual field, without scanning. In the visual-evoked potential (VEP) a component can be isolated which is related to texture segregation ("tsVEP"). Our objective was to evaluate the temporal frequency dependence of the tsVEP to compare processing speed of low-level features (e.g., orientation, using the VEP, here denoted llVEP) with texture segregation because of a recent literature controversy in that regard. Visual-evoked potentials (VEPs) were recorded in seven normal adults. Oriented line segments of 0.1 degrees x 0.8 degrees at 100% contrast were presented in four different arrangements: either oriented in parallel for two homogeneous stimuli (from which were obtained the low-level VEP (llVEP)) or with a 90 degrees orientation gradient for two textured ones (from which were obtained the texture VEP). The orientation texture condition was presented at eight different temporal frequencies ranging from 7.5 to 45 Hz. Fourier analysis was used to isolate low-level components at the pattern-change frequency and texture-segregation components at half that frequency. For all subjects, there was lower high-cutoff frequency for tsVEP than for llVEPs, on average 12 Hz vs. 17 Hz (P = 0.017). The results suggest that the processing of feature gradients to extract texture segregation requires additional processing time, resulting in a lower fusion frequency.

Detecting PHG frames in wireless capsule endoscopy video by integrating rough global dominate-color with fine local texture features

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Liu, Xiaoqi; Wang, Chengliang; Bai, Jianying; Liao, Guobin

2018-02-01

Portal hypertensive gastropathy (PHG) is common in gastrointestinal (GI) diseases, and a severe stage of PHG (S-PHG) is a source of gastrointestinal active bleeding. Generally, the diagnosis of PHG is made visually during endoscopic examination; compared with traditional endoscopy, (wireless capsule endoscopy) WCE with noninvasive and painless is chosen as a prevalent tool for visual observation of PHG. However, accurate measurement of WCE images with PHG is a difficult task due to faint contrast and confusing variations in background gastric mucosal tissue for physicians. Therefore, this paper proposes a comprehensive methodology to automatically detect S-PHG images in WCE video to help physicians accurately diagnose S-PHG. Firstly, a rough dominatecolor-tone extraction approach is proposed for better describing global color distribution information of gastric mucosa. Secondly, a hybrid two-layer texture acquisition model is designed by integrating co-occurrence matrix into local binary pattern to depict complex and unique gastric mucosal microstructure local variation. Finally, features of mucosal color and microstructure texture are merged into linear support vector machine to accomplish this automatic classification task. Experiments were implemented on an annotated data set including 1,050 SPHG and 1,370 normal images collected from 36 real patients of different nationalities, ages and genders. By comparison with three traditional texture extraction methods, our method, combined with experimental results, performs best in detection of S-PHG images in WCE video: the maximum of accuracy, sensitivity and specificity reach 0.90, 0.92 and 0.92 respectively.

Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma.

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Cheng, Nai-Ming; Fang, Yu-Hua Dean; Chang, Joseph Tung-Chieh; Huang, Chung-Guei; Tsan, Din-Li; Ng, Shu-Hang; Wang, Hung-Ming; Lin, Chien-Yu; Liao, Chun-Ta; Yen, Tzu-Chen

2013-10-01

Previous studies have shown that total lesion glycolysis (TLG) may serve as a prognostic indicator in oropharyngeal squamous cell carcinoma (OPSCC). We sought to investigate whether the textural features of pretreatment (18)F-FDG PET/CT images can provide any additional prognostic information over TLG and clinical staging in patients with advanced T-stage OPSCC. We retrospectively analyzed the pretreatment (18)F-FDG PET/CT images of 70 patients with advanced T-stage OPSCC who had completed concurrent chemoradiotherapy, bioradiotherapy, or radiotherapy with curative intent. All of the patients had data on human papillomavirus (HPV) infection and were followed up for at least 24 mo or until death. A standardized uptake value (SUV) of 2.5 was taken as a cutoff for tumor boundary. The textural features of pretreatment (18)F-FDG PET/CT images were extracted from histogram analysis (SUV variance and SUV entropy), normalized gray-level cooccurrence matrix (uniformity, entropy, dissimilarity, contrast, homogeneity, inverse different moment, and correlation), and neighborhood gray-tone difference matrix (coarseness, contrast, busyness, complexity, and strength). Receiver-operating-characteristic curves were used to identify the optimal cutoff values for the textural features and TLG. Thirteen patients were HPV-positive. Multivariate Cox regression analysis showed that age, tumor TLG, and uniformity were independently associated with progression-free survival (PFS) and disease-specific survival (DSS). TLG, uniformity, and HPV positivity were significantly associated with overall survival (OS). A prognostic scoring system based on TLG and uniformity was derived. Patients who presented with TLG > 121.9 g and uniformity ≤ 0.138 experienced significantly worse PFS, DSS, and OS rates than those without (P 121.9 g or uniformity ≤ 0.138 were further divided according to age, and different PFS and DSS were observed. Uniformity extracted from the normalized gray

Support Vector Machine and Parametric Wavelet-Based Texture Classification of Stem Cell Images

National Research Council Canada - National Science Library

Jeffreys, Christopher

2004-01-01

.... Since colony texture is a major discriminating feature in determining quality, we introduce a non-invasive, semi-automated texture-based stem cell colony classification methodology to aid researchers...

Texture operator for snow particle classification into snowflake and graupel

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

2012-11-01

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

Correlation of pretreatment {sup 18}F-FDG PET tumor textural features with gene expression in pharyngeal cancer and implications for radiotherapy-based treatment outcomes

Energy Technology Data Exchange (ETDEWEB)

Chen, Shang-Wen [China Medical University Hospital, Department of Radiation Oncology, Taichung (China); China Medical University, School of Medicine, Taichung (China); Taipei Medical University, School of Medicine, Taipei (China); China Medical University, Graduate Institute of Clinical Medical Science, School of Medicine, College of Medicine, Taichung (China); Shen, Wei-Chih [China Medical University, Cancer Center and Department of Medical Research, China Medical University Hospital, Taichung (China); Asia University, Department of Computer Science and Information Engineering, Taichung (China); Lin, Ying-Chun [China Medical University Hospital, Department of Radiation Oncology, Taichung (China); China Medical University and Academia Sinica, The Ph.D. Program for Cancer Biology and Drug Discovery, Taichung (China); Chen, Rui-Yun [China Medical University Hospital, Department of Pathology, Taichung (China); Hsieh, Te-Chun; Yen, Kuo-Yang [China Medical University Hospital, Department of Nuclear Medicine and PET Center, Taichung (China); China Medical University, Department of Biomedical Imaging and Radiological Science, Taichung (China); Kao, Chia-Hung [China Medical University, Graduate Institute of Clinical Medical Science, School of Medicine, College of Medicine, Taichung (China); China Medical University Hospital, Department of Nuclear Medicine and PET Center, Taichung (China); Asia University, Department of Bioinformatics and Medical Engineering, Taichung (China)

2017-04-15

This study investigated the correlation of the matrix heterogeneity of tumors on {sup 18}F-fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) with gene-expression profiling in patients with pharyngeal cancer and determined the prognostic factors for radiotherapy-based treatment outcomes. We retrospectively reviewed the records of 57 patients with stage III-IV oropharyngeal or hypopharyngeal cancer who had completed definitive therapy. Four groups of the textural features as well as 31 indices were studied in addition to maximum standard uptake value, metastatic tumor volume, and total lesion glycolysis. Immunohistochemical data from pretreatment biopsy specimens (Glut1, CAIX, VEGF, HIF-1α, EGFR, Ki-67, Bcl-2, CLAUDIN-4, YAP-1, c-Met, and p16) were analyzed. The relationships between the indices and genomic expression were studied, and the robustness of various textural features relative to cause-specific survival and primary relapse-free survival was analyzed. The overexpression of VEGF was positively associated with the increased values of the matrix heterogeneity obtained using gray-level nonuniformity for zone (GLNUz) and run-length nonuniformity (RLNU). Advanced T stage (p = 0.01, hazard ratio [HR] = 3.38), a VEGF immunoreactive score of >2 (p = 0.03, HR = 2.79), and a higher GLNUz value (p = 0.04, HR = 2.51) were prognostic factors for low cause-specific survival, whereas advanced T stage, a HIF-1α staining percentage of ≥80%, and a higher GLNUz value were prognostic factors for low primary-relapse free survival. The overexpression of VEGF was associated with the increased matrix index of GLNUz and RLNU. For patients with pharyngeal cancer requiring radiotherapy, the treatment outcome can be stratified according to the textural features, T stage, and biomarkers. (orig.)

Depth perception: cuttlefish (Sepia officinalis) respond to visual texture density gradients.

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Josef, Noam; Mann, Ofri; Sykes, António V; Fiorito, Graziano; Reis, João; Maccusker, Steven; Shashar, Nadav

2014-11-01

Studies concerning the perceptual processes of animals are not only interesting, but are fundamental to the understanding of other developments in information processing among non-humans. Carefully used visual illusions have been proven to be an informative tool for understanding visual perception. In this behavioral study, we demonstrate that cuttlefish are responsive to visual cues involving texture gradients. Specifically, 12 out of 14 animals avoided swimming over a solid surface with a gradient picture that to humans resembles an illusionary crevasse, while only 5 out of 14 avoided a non-illusionary texture. Since texture gradients are well-known cues for depth perception in vertebrates, we suggest that these cephalopods were responding to the depth illusion created by the texture density gradient. Density gradients and relative densities are key features in distance perception in vertebrates. Our results suggest that they are fundamental features of vision in general, appearing also in cephalopods.

Texture classification by texton: statistical versus binary.

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Zhenhua Guo

Full Text Available Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8, image patch (Statistical_Joint and locally invariant fractal (Statistical_Fractal are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor.

Deep neural networks for texture classification-A theoretical analysis.

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

2018-01-01

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

Computer Texture Mapping for Laser Texturing of Injection Mold

Directory of Open Access Journals (Sweden)

Yongquan Zhou

2014-04-01

Full Text Available Laser texturing is a relatively new multiprocess technique that has been used for machining 3D curved surfaces; it is more flexible and efficient to create decorative texture on 3D curved surfaces of injection molds so as to improve the surface quality and achieve cosmetic surface of molded plastic parts. In this paper, a novel method of laser texturing 3D curved surface based on 3-axis galvanometer scanning unit has been presented to prevent the texturing of injection mold surface from much distortion which is often caused by traditional texturing processes. The novel method has been based on the computer texture mapping technology which has been developed and presented. The developed texture mapping algorithm includes surface triangulation, notations, distortion measurement, control, and numerical method. An interface of computer texture mapping has been built to implement the algorithm of texture mapping approach to controlled distortion rate of 3D texture math model from 2D original texture applied to curvature surface. Through a case study of laser texturing of a high curvature surface of injection mold of a mice top case, it shows that the novel method of laser texturing meets the quality standard of laser texturing of injection mold.

Synthesized interstitial lung texture for use in anthropomorphic computational phantoms

Science.gov (United States)

Becchetti, Marc F.; Solomon, Justin B.; Segars, W. Paul; Samei, Ehsan

2016-04-01

A realistic model of the anatomical texture from the pulmonary interstitium was developed with the goal of extending the capability of anthropomorphic computational phantoms (e.g., XCAT, Duke University), allowing for more accurate image quality assessment. Contrast-enhanced, high dose, thorax images for a healthy patient from a clinical CT system (Discovery CT750HD, GE healthcare) with thin (0.625 mm) slices and filtered back- projection (FBP) were used to inform the model. The interstitium which gives rise to the texture was defined using 24 volumes of interest (VOIs). These VOIs were selected manually to avoid vasculature, bronchi, and bronchioles. A small scale Hessian-based line filter was applied to minimize the amount of partial-volumed supernumerary vessels and bronchioles within the VOIs. The texture in the VOIs was characterized using 8 Haralick and 13 gray-level run length features. A clustered lumpy background (CLB) model with added noise and blurring to match CT system was optimized to resemble the texture in the VOIs using a genetic algorithm with the Mahalanobis distance as a similarity metric between the texture features. The most similar CLB model was then used to generate the interstitial texture to fill the lung. The optimization improved the similarity by 45%. This will substantially enhance the capabilities of anthropomorphic computational phantoms, allowing for more realistic CT simulations.

Texture-based classification of different gastric tumors at contrast-enhanced CT

Energy Technology Data Exchange (ETDEWEB)

Ba-Ssalamah, Ahmed, E-mail: ahmed.ba-ssalamah@meduniwien.ac.at [Department of Radiology, Medical University of Vienna (Austria); Muin, Dina; Schernthaner, Ruediger; Kulinna-Cosentini, Christiana; Bastati, Nina [Department of Radiology, Medical University of Vienna (Austria); Stift, Judith [Department of Pathology, Medical University of Vienna (Austria); Gore, Richard [Department of Radiology, University of Chicago Pritzker School of Medicine, Chicago, IL (United States); Mayerhoefer, Marius E. [Department of Radiology, Medical University of Vienna (Austria)

2013-10-01

Purpose: To determine the feasibility of texture analysis for the classification of gastric adenocarcinoma, lymphoma, and gastrointestinal stromal tumors on contrast-enhanced hydrodynamic-MDCT images. Materials and methods: The arterial phase scans of 47 patients with adenocarcinoma (AC) and a histologic tumor grade of [AC-G1, n = 4, G1, n = 4; AC-G2, n = 7; AC-G3, n = 16]; GIST, n = 15; and lymphoma, n = 5, and the venous phase scans of 48 patients with AC-G1, n = 3; AC-G2, n = 6; AC-G3, n = 14; GIST, n = 17; lymphoma, n = 8, were retrospectively reviewed. Based on regions of interest, texture analysis was performed, and features derived from the gray-level histogram, run-length and co-occurrence matrix, absolute gradient, autoregressive model, and wavelet transform were calculated. Fisher coefficients, probability of classification error, average correlation coefficients, and mutual information coefficients were used to create combinations of texture features that were optimized for tumor differentiation. Linear discriminant analysis in combination with a k-nearest neighbor classifier was used for tumor classification. Results: On arterial-phase scans, texture-based lesion classification was highly successful in differentiating between AC and lymphoma, and GIST and lymphoma, with misclassification rates of 3.1% and 0%, respectively. On venous-phase scans, texture-based classification was slightly less successful for AC vs. lymphoma (9.7% misclassification) and GIST vs. lymphoma (8% misclassification), but enabled the differentiation between AC and GIST (10% misclassification), and between the different grades of AC (4.4% misclassification). No texture feature combination was able to adequately distinguish between all three tumor types. Conclusion: Classification of different gastric tumors based on textural information may aid radiologists in establishing the correct diagnosis, at least in cases where the differential diagnosis can be narrowed down to two

Texture-based classification of different gastric tumors at contrast-enhanced CT

International Nuclear Information System (INIS)

Ba-Ssalamah, Ahmed; Muin, Dina; Schernthaner, Ruediger; Kulinna-Cosentini, Christiana; Bastati, Nina; Stift, Judith; Gore, Richard; Mayerhoefer, Marius E.

2013-01-01

Purpose: To determine the feasibility of texture analysis for the classification of gastric adenocarcinoma, lymphoma, and gastrointestinal stromal tumors on contrast-enhanced hydrodynamic-MDCT images. Materials and methods: The arterial phase scans of 47 patients with adenocarcinoma (AC) and a histologic tumor grade of [AC-G1, n = 4, G1, n = 4; AC-G2, n = 7; AC-G3, n = 16]; GIST, n = 15; and lymphoma, n = 5, and the venous phase scans of 48 patients with AC-G1, n = 3; AC-G2, n = 6; AC-G3, n = 14; GIST, n = 17; lymphoma, n = 8, were retrospectively reviewed. Based on regions of interest, texture analysis was performed, and features derived from the gray-level histogram, run-length and co-occurrence matrix, absolute gradient, autoregressive model, and wavelet transform were calculated. Fisher coefficients, probability of classification error, average correlation coefficients, and mutual information coefficients were used to create combinations of texture features that were optimized for tumor differentiation. Linear discriminant analysis in combination with a k-nearest neighbor classifier was used for tumor classification. Results: On arterial-phase scans, texture-based lesion classification was highly successful in differentiating between AC and lymphoma, and GIST and lymphoma, with misclassification rates of 3.1% and 0%, respectively. On venous-phase scans, texture-based classification was slightly less successful for AC vs. lymphoma (9.7% misclassification) and GIST vs. lymphoma (8% misclassification), but enabled the differentiation between AC and GIST (10% misclassification), and between the different grades of AC (4.4% misclassification). No texture feature combination was able to adequately distinguish between all three tumor types. Conclusion: Classification of different gastric tumors based on textural information may aid radiologists in establishing the correct diagnosis, at least in cases where the differential diagnosis can be narrowed down to two

Ion-beam texturing of uniaxially textured Ni films

International Nuclear Information System (INIS)

Park, S.J.; Norton, D.P.; Selvamanickam, Venkat

2005-01-01

The formation of biaxial texture in uniaxially textured Ni thin films via Ar-ion irradiation is reported. The ion-beam irradiation was not simultaneous with deposition. Instead, the ion beam irradiates the uniaxially textured film surface with no impinging deposition flux, which differs from conventional ion-beam-assisted deposition. The uniaxial texture is established via a nonion beam process, with the in-plane texture imposed on the uniaxial film via ion beam bombardment. Within this sequential ion beam texturing method, grain alignment is driven by selective etching and grain overgrowth

Statistical Texture Model for mass Detection in Mammography

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Nicolás Gallego-Ortiz

2013-12-01

Full Text Available In the context of image processing algorithms for mass detection in mammography, texture is a key feature to be used to distinguish abnormal tissue from normal tissue. Recently, a texture model based on a multivariate Gaussian mixture was proposed, of which the parameters are learned in an unsupervised way from the pixel intensities of images. The model produces images that are probabilistic maps of texture normality and it was proposed as a visualization aid for diagnostic by clinical experts. In this paper, the usability of the model is studied for automatic mass detection. A segmentation strategy is proposed and evaluated using 79 mammography cases.

A methodology for texture feature-based quality assessment in nucleus segmentation of histopathology image

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Si Wen

2017-01-01

Full Text Available Context: Image segmentation pipelines often are sensitive to algorithm input parameters. Algorithm parameters optimized for a set of images do not necessarily produce good-quality-segmentation results for other images. Even within an image, some regions may not be well segmented due to a number of factors, including multiple pieces of tissue with distinct characteristics, differences in staining of the tissue, normal versus tumor regions, and tumor heterogeneity. Evaluation of quality of segmentation results is an important step in image analysis. It is very labor intensive to do quality assessment manually with large image datasets because a whole-slide tissue image may have hundreds of thousands of nuclei. Semi-automatic mechanisms are needed to assist researchers and application developers to detect image regions with bad segmentations efficiently. Aims: Our goal is to develop and evaluate a machine-learning-based semi-automated workflow to assess quality of nucleus segmentation results in a large set of whole-slide tissue images. Methods: We propose a quality control methodology, in which machine-learning algorithms are trained with image intensity and texture features to produce a classification model. This model is applied to image patches in a whole-slide tissue image to predict the quality of nucleus segmentation in each patch. The training step of our methodology involves the selection and labeling of regions by a pathologist in a set of images to create the training dataset. The image regions are partitioned into patches. A set of intensity and texture features is computed for each patch. A classifier is trained with the features and the labels assigned by the pathologist. At the end of this process, a classification model is generated. The classification step applies the classification model to unlabeled test images. Each test image is partitioned into patches. The classification model is applied to each patch to predict the patch

Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network.

Science.gov (United States)

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Jiang, Yuan Yuan; Kim, Sung Min

2015-01-01

This paper focuses on the improvement of the diagnostic accuracy of focal liver lesions by quantifying the key features of cysts, hemangiomas, and malignant lesions on ultrasound images. The focal liver lesions were divided into 29 cysts, 37 hemangiomas, and 33 malignancies. A total of 42 hybrid textural features that composed of 5 first order statistics, 18 gray level co-occurrence matrices, 18 Law's, and echogenicity were extracted. A total of 29 key features that were selected by principal component analysis were used as a set of inputs for a feed-forward neural network. For each lesion, the performance of the diagnosis was evaluated by using the positive predictive value, negative predictive value, sensitivity, specificity, and accuracy. The results of the experiment indicate that the proposed method exhibits great performance, a high diagnosis accuracy of over 96% among all focal liver lesion groups (cyst vs. hemangioma, cyst vs. malignant, and hemangioma vs. malignant) on ultrasound images. The accuracy was slightly increased when echogenicity was included in the optimal feature set. These results indicate that it is possible for the proposed method to be applied clinically.

Quantitative evaluation methods of skin condition based on texture feature parameters

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Hui Pang

2017-03-01

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

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

Science.gov (United States)

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

2017-03-01

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

UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis

Directory of Open Access Journals (Sweden)

Quanlong Feng

2015-01-01

Full Text Available Unmanned aerial vehicle (UAV remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be reduced by incorporating texture features and robust classifiers. Random Forest has been widely used in satellite remote sensing applications, but its usage in UAV image classification has not been well documented. The objectives of this paper were to propose a hybrid method using Random Forest and texture analysis to accurately differentiate land covers of urban vegetated areas, and analyze how classification accuracy changes with texture window size. Six least correlated second-order texture measures were calculated at nine different window sizes and added to original Red-Green-Blue (RGB images as ancillary data. A Random Forest classifier consisting of 200 decision trees was used for classification in the spectral-textural feature space. Results indicated the following: (1 Random Forest outperformed traditional Maximum Likelihood classifier and showed similar performance to object-based image analysis in urban vegetation classification; (2 the inclusion of texture features improved classification accuracy significantly; (3 classification accuracy followed an inverted U relationship with texture window size. The results demonstrate that UAV provides an efficient and ideal platform for urban vegetation mapping. The hybrid method proposed in this paper shows good performance in differentiating urban vegetation mapping. The drawbacks of off-the-shelf digital cameras can be reduced by adopting Random Forest and texture analysis at the same time.

Implementation of Texture Based Image Retrieval Using M-band Wavelet Transform

Institute of Scientific and Technical Information of China (English)

LiaoYa-li; Yangyan; CaoYang

2003-01-01

Wavelet transform has attracted attention because it is a very useful tool for signal analyzing. As a fundamental characteristic of an image, texture traits play an important role in the human vision system for recognition and interpretation of images. The paper presents an approach to implement texture-based image retrieval using M-band wavelet transform. Firstly the traditional 2-band wavelet is extended to M-band wavelet transform. Then the wavelet moments are computed by M-band wavelet coefficients in the wavelet domain. The set of wavelet moments forms the feature vector related to the texture distribution of each wavelet images. The distances between the feature vectors describe the similarities of different images. The experimental result shows that the M-band wavelet moment features of the images are effective for image indexing.The retrieval method has lower computational complexity, yet it is capable of giving better retrieval performance for a given medical image database.

Deep-learning derived features for lung nodule classification with limited datasets

Science.gov (United States)

Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

2018-02-01

Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.

SU-E-J-262: Variability in Texture Analysis of Gynecological Tumors in the Context of An 18F-FDG PET Adaptive Protocol

Energy Technology Data Exchange (ETDEWEB)

Nawrocki, J [Duke University Medical Physics Graduate Program, Durham, NC (United States); Chino, J; Das, S; Craciunescu, O [Duke University Medical Center, Durham, NC (United States)

2015-06-15

Purpose: This study examines the effect on texture analysis due to variable reconstruction of PET images in the context of an adaptive FDG PET protocol for node positive gynecologic cancer patients. By measuring variability in texture features from baseline and intra-treatment PET-CT, we can isolate unreliable texture features due to large variation. Methods: A subset of seven patients with node positive gynecological cancers visible on PET was selected for this study. Prescribed dose varied between 45–50.4Gy, with a 55–70Gy boost to the PET positive nodes. A baseline and intratreatment (between 30–36Gy) PET-CT were obtained on a Siemens Biograph mCT. Each clinical PET image set was reconstructed 6 times using a TrueX+TOF algorithm with varying iterations and Gaussian filter. Baseline and intra-treatment primary GTVs were segmented using PET Edge (MIM Software Inc., Cleveland, OH), a semi-automatic gradient-based algorithm, on the clinical PET and transferred to the other reconstructed sets. Using an in-house MATLAB program, four 3D texture matrices describing relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 textural features characterizing texture were calculated in addition to SUV histogram features. The percent variability among parameters was first calculated. Each reconstructed texture feature from baseline and intra-treatment per patient was normalized to the clinical baseline scan and compared using the Wilcoxon signed-rank test in order to isolate variations due to reconstruction parameters. Results: For the baseline scans, 13 texture features showed a mean range greater than 10%. For the intra scans, 28 texture features showed a mean range greater than 10%. Comparing baseline to intra scans, 25 texture features showed p <0.05. Conclusion: Variability due to different reconstruction parameters increased with treatment, however, the majority of texture

SU-E-J-262: Variability in Texture Analysis of Gynecological Tumors in the Context of An 18F-FDG PET Adaptive Protocol

International Nuclear Information System (INIS)

Nawrocki, J; Chino, J; Das, S; Craciunescu, O

2015-01-01

Purpose: This study examines the effect on texture analysis due to variable reconstruction of PET images in the context of an adaptive FDG PET protocol for node positive gynecologic cancer patients. By measuring variability in texture features from baseline and intra-treatment PET-CT, we can isolate unreliable texture features due to large variation. Methods: A subset of seven patients with node positive gynecological cancers visible on PET was selected for this study. Prescribed dose varied between 45–50.4Gy, with a 55–70Gy boost to the PET positive nodes. A baseline and intratreatment (between 30–36Gy) PET-CT were obtained on a Siemens Biograph mCT. Each clinical PET image set was reconstructed 6 times using a TrueX+TOF algorithm with varying iterations and Gaussian filter. Baseline and intra-treatment primary GTVs were segmented using PET Edge (MIM Software Inc., Cleveland, OH), a semi-automatic gradient-based algorithm, on the clinical PET and transferred to the other reconstructed sets. Using an in-house MATLAB program, four 3D texture matrices describing relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 textural features characterizing texture were calculated in addition to SUV histogram features. The percent variability among parameters was first calculated. Each reconstructed texture feature from baseline and intra-treatment per patient was normalized to the clinical baseline scan and compared using the Wilcoxon signed-rank test in order to isolate variations due to reconstruction parameters. Results: For the baseline scans, 13 texture features showed a mean range greater than 10%. For the intra scans, 28 texture features showed a mean range greater than 10%. Comparing baseline to intra scans, 25 texture features showed p <0.05. Conclusion: Variability due to different reconstruction parameters increased with treatment, however, the majority of texture

3D texture analysis reveals imperceptible MRI textural alterations in the thalamus and putamen in progressive myoclonic epilepsy type 1, EPM1.

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Sanna Suoranta

Full Text Available Progressive myoclonic epilepsy type 1 (EPM1 is an autosomal recessively inherited neurodegenerative disorder characterized by young onset age, myoclonus and tonic-clonic epileptic seizures. At the time of diagnosis, the visual assessment of the brain MRI is usually normal, with no major changes found later. Therefore, we utilized texture analysis (TA to characterize and classify the underlying properties of the affected brain tissue by means of 3D texture features. Sixteen genetically verified patients with EPM1 and 16 healthy controls were included in the study. TA was performed upon 3D volumes of interest that were placed bilaterally in the thalamus, amygdala, hippocampus, caudate nucleus and putamen. Compared to the healthy controls, EPM1 patients had significant textural differences especially in the thalamus and right putamen. The most significantly differing texture features included parameters that measure the complexity and heterogeneity of the tissue, such as the co-occurrence matrix-based entropy and angular second moment, and also the run-length matrix-based parameters of gray-level non-uniformity, short run emphasis and long run emphasis. This study demonstrates the usability of 3D TA for extracting additional information from MR images. Textural alterations which suggest complex, coarse and heterogeneous appearance were found bilaterally in the thalamus, supporting the previous literature on thalamic pathology in EPM1. The observed putamenal involvement is a novel finding. Our results encourage further studies on the clinical applications, feasibility, reproducibility and reliability of 3D TA.

Texture of low temperature isotropic pyrocarbons

International Nuclear Information System (INIS)

Pelissier, Joseph; Lombard, Louis.

1976-01-01

Isotropic pyrocarbon deposited on fuel particles was studied by transmission electron microscopy in order to determine its texture. The material consists of an agglomerate of spherical growth features similar to those of carbon black. The spherical growth features are formed from the cristallites of turbostratic carbon and the distribution gives an isotropic structure. Neutron irradiation modifies the morphology of the pyrocarbon. The spherical growth features are deformed and the coating becomes strongly anisotropic. The transformation leads to the rupture of the coating caused by strong irradiation doses [fr

Forest Classification Based on Forest texture in Northwest Yunnan Province

Science.gov (United States)

Wang, Jinliang; Gao, Yan; Wang, Xiaohua; Fu, Lei

2014-03-01

Forest texture is an intrinsic characteristic and an important visual feature of a forest ecological system. Full utilization of forest texture will be a great help in increasing the accuracy of forest classification based on remote sensed data. Taking Shangri-La as a study area, forest classification has been based on the texture. The results show that: (1) From the texture abundance, texture boundary, entropy as well as visual interpretation, the combination of Grayscale-gradient co-occurrence matrix and wavelet transformation is much better than either one of both ways of forest texture information extraction; (2) During the forest texture information extraction, the size of the texture-suitable window determined by the semi-variogram method depends on the forest type (evergreen broadleaf forest is 3×3, deciduous broadleaf forest is 5×5, etc.). (3)While classifying forest based on forest texture information, the texture factor assembly differs among forests: Variance Heterogeneity and Correlation should be selected when the window is between 3×3 and 5×5 Mean, Correlation, and Entropy should be used when the window in the range of 7×7 to 19×19 and Correlation, Second Moment, and Variance should be used when the range is larger than 21×21.

Forest Classification Based on Forest texture in Northwest Yunnan Province

International Nuclear Information System (INIS)

Wang, Jinliang; Gao, Yan; Fu, Lei; Wang, Xiaohua

2014-01-01

Forest texture is an intrinsic characteristic and an important visual feature of a forest ecological system. Full utilization of forest texture will be a great help in increasing the accuracy of forest classification based on remote sensed data. Taking Shangri-La as a study area, forest classification has been based on the texture. The results show that: (1) From the texture abundance, texture boundary, entropy as well as visual interpretation, the combination of Grayscale-gradient co-occurrence matrix and wavelet transformation is much better than either one of both ways of forest texture information extraction; (2) During the forest texture information extraction, the size of the texture-suitable window determined by the semi-variogram method depends on the forest type (evergreen broadleaf forest is 3×3, deciduous broadleaf forest is 5×5, etc.). (3)While classifying forest based on forest texture information, the texture factor assembly differs among forests: Variance Heterogeneity and Correlation should be selected when the window is between 3×3 and 5×5; Mean, Correlation, and Entropy should be used when the window in the range of 7×7 to 19×19; and Correlation, Second Moment, and Variance should be used when the range is larger than 21×21

A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data

Directory of Open Access Journals (Sweden)

Salman Qadri

2016-01-01

Full Text Available The main objective of this study is to find out the importance of machine vision approach for the classification of five types of land cover data such as bare land, desert rangeland, green pasture, fertile cultivated land, and Sutlej river land. A novel spectra-statistical framework is designed to classify the subjective land cover data types accurately. Multispectral data of these land covers were acquired by using a handheld device named multispectral radiometer in the form of five spectral bands (blue, green, red, near infrared, and shortwave infrared while texture data were acquired with a digital camera by the transformation of acquired images into 229 texture features for each image. The most discriminant 30 features of each image were obtained by integrating the three statistical features selection techniques such as Fisher, Probability of Error plus Average Correlation, and Mutual Information (F + PA + MI. Selected texture data clustering was verified by nonlinear discriminant analysis while linear discriminant analysis approach was applied for multispectral data. For classification, the texture and multispectral data were deployed to artificial neural network (ANN: n-class. By implementing a cross validation method (80-20, we received an accuracy of 91.332% for texture data and 96.40% for multispectral data, respectively.

Strings, texture, and inflation

International Nuclear Information System (INIS)

Hodges, H.M.; Primack, J.R.

1991-01-01

We examine mechanisms, several of which are proposed here, to generate structure formation, or to just add large-scale features, through either gauged or global cosmic strings or global texture, within the framework of inflation. We first explore the possibility that strings or texture form if there is no coupling between the topological theory and the inflaton or spacetime curvature, via (1) quantum creation, and (2) a sufficiently high reheat temperature. In addition, we examine the prospects for the inflaton field itself to generate strings or texture. Then, models with the string/texture field coupled to the curvature, and an equivalent model with coupling to the inflaton field, are considered in detail. The requirement that inflationary density fluctuations are not so large as to conflict with observations leads to a number of constraints on model parameters. We find that strings of relevance for structure formation can form in the absence of coupling to the inflaton or curvature through the process of quantum creation, but only if the strings are strongly type I, or if they are global strings. If formed after reheating, naturalness suggests that gauged cosmic strings correspond to a type-I superconductor. Similarly, gauged strings formed during inflation via conformal coupling ξ=1/6 to the spacetime curvature (in a model suggested by Yokoyama in order to evade the millisecond pulsar constraint on cosmic strings) are expected to be strongly type I

The role of textures in the forming of automotive sheet steels

International Nuclear Information System (INIS)

Sanak Mishra

1996-01-01

Crystallographic textures generally have a strong bearing on the drawability of sheet steels. Particularly in the case of automotive sheets, texture control is of paramount importance. In the last two decades, therefore, texture research has assumed much significance in the steel industry. X-ray diffraction continues to remain the most used tool for the study of textures. Early researches, from about 1940 to 1980, were invariably carried out by the pole figure method. However, for more quantitative results the ODF (Orientation Distribution Functions) analysis technique was developed. Since 1980, the ODF analysis has come to be used extensively. In the present paper, several unique features of textures in automotive grade deep drawing steels, as revealed from X-ray ODFS, will be presented. The relative importance of the various textural components with respect to forming will also be dealt with

Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans.

Science.gov (United States)

Oddo, Calogero Maria; Raspopovic, Stanisa; Artoni, Fiorenzo; Mazzoni, Alberto; Spigler, Giacomo; Petrini, Francesco; Giambattistelli, Federica; Vecchio, Fabrizio; Miraglia, Francesca; Zollo, Loredana; Di Pino, Giovanni; Camboni, Domenico; Carrozza, Maria Chiara; Guglielmelli, Eugenio; Rossini, Paolo Maria; Faraguna, Ugo; Micera, Silvestro

2016-03-08

Restoration of touch after hand amputation is a desirable feature of ideal prostheses. Here, we show that texture discrimination can be artificially provided in human subjects by implementing a neuromorphic real-time mechano-neuro-transduction (MNT), which emulates to some extent the firing dynamics of SA1 cutaneous afferents. The MNT process was used to modulate the temporal pattern of electrical spikes delivered to the human median nerve via percutaneous microstimulation in four intact subjects and via implanted intrafascicular stimulation in one transradial amputee. Both approaches allowed the subjects to reliably discriminate spatial coarseness of surfaces as confirmed also by a hybrid neural model of the median nerve. Moreover, MNT-evoked EEG activity showed physiologically plausible responses that were superimposable in time and topography to the ones elicited by a natural mechanical tactile stimulation. These findings can open up novel opportunities for sensory restoration in the next generation of neuro-prosthetic hands.

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

International Nuclear Information System (INIS)

Lee, S; Markel, D; Hegyi, G; El Naqa, I

2016-01-01

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

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

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Lee, S; Markel, D; Hegyi, G [Medical Physics Unit, McGill University, Montreal, Quebec (Canada); El Naqa, I [University of Michigan, Ann Arbor, MI (United States)

2016-06-15

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

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

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

2014-03-01

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

Selective Extraction of Entangled Textures via Adaptive PDE Transform

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Yang Wang

2012-01-01

Full Text Available Texture and feature extraction is an important research area with a wide range of applications in science and technology. Selective extraction of entangled textures is a challenging task due to spatial entanglement, orientation mixing, and high-frequency overlapping. The partial differential equation (PDE transform is an efficient method for functional mode decomposition. The present work introduces adaptive PDE transform algorithm to appropriately threshold the statistical variance of the local variation of functional modes. The proposed adaptive PDE transform is applied to the selective extraction of entangled textures. Successful separations of human face, clothes, background, natural landscape, text, forest, camouflaged sniper and neuron skeletons have validated the proposed method.

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

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

2017-06-01

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

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

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

2018-04-01

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

Textural pattern classification for oral squamous cell carcinoma.

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Rahman, T Y; Mahanta, L B; Chakraborty, C; DAS, A K; Sarma, J D

2018-01-01

Despite being an area of cancer with highest worldwide incidence, oral cancer yet remains to be widely researched. Studies on computer-aided analysis of pathological slides of oral cancer contribute a lot to the diagnosis and treatment of the disease. Some researches in this direction have been carried out on oral submucous fibrosis. In this work an approach for analysing abnormality based on textural features present in squamous cell carcinoma histological slides have been considered. Histogram and grey-level co-occurrence matrix approaches for extraction of textural features from biopsy images with normal and malignant cells are used here. Further, we have used linear support vector machine classifier for automated diagnosis of the oral cancer, which gives 100% accuracy. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society.

Usefulness of texture analysis in differentiating transient from persistent part-solid nodules(PSNs: a retrospective study.

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Sang Hwan Lee

Full Text Available BACKGROUND: Early discrimination between transient and persistent par-solid ground-glass nodules (PSNs at CT is essential for patient management. The objective of our study was to retrospectively investigate the value of texture analysis in differentiating pulmonary transient and persistent PSNs in addition to clinical and CT features. METHODS: This retrospective study was performed with IRB approval and a waiver of the requirement for patients' informed consent. From January 2007 to October 2009, we identified 77 individuals (39 men and 38 women; mean age, 55 years with 86 PSNs on thin-section chest CT. Thirty-nine PSNs in 31 individuals were transient and 47 PSNs in 46 patients were persistent. The clinical, CT, and texture features of PSNs were evaluated. To investigate the additional value of texture analysis in differentiating transient from persistent PSNs, logistic regression analysis and C-statistics were performed. RESULTS: Between transient and persistent PSNs, there were significant differences in age, gender, smoking history, and eosinophil count among the clinical features. As for thin-section CT features, there were significant differences in lesion size, solid portion size, and lesion multiplicity. In terms of texture features, there were significant differences in mean attenuation, skewness of whole PSN, attenuation ratio of whole PSN to inner solid portion, and 5-, 10-, 25-, 50-percentile CT numbers of whole PSN. Multivariate analysis revealed eosinophilia, lesion size, lesion multiplicity, mean attenuation of whole PSN, skewness of whole PSN, and 5-percentile CT number were significant independent predictors of transient PSNs. (P<0.05 C-statistics revealed that texture analysis incorporating clinical and CT features (AUC, 92.9% showed significantly higher differentiating performance of transient from persistent PSNs compared with the clinical and CT features alone (AUC, 79.0%. (P =  0.004. CONCLUSION: Texture analysis of

An Active Patch Model for Real World Texture and Appearance Classification.

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Mao, Junhua; Zhu, Jun; Yuille, Alan L

2014-09-06

This paper addresses the task of natural texture and appearance classification. Our goal is to develop a simple and intuitive method that performs at state of the art on datasets ranging from homogeneous texture (e.g., material texture), to less homogeneous texture (e.g., the fur of animals), and to inhomogeneous texture (the appearance patterns of vehicles). Our method uses a bag-of-words model where the features are based on a dictionary of active patches. Active patches are raw intensity patches which can undergo spatial transformations (e.g., rotation and scaling) and adjust themselves to best match the image regions. The dictionary of active patches is required to be compact and representative, in the sense that we can use it to approximately reconstruct the images that we want to classify. We propose a probabilistic model to quantify the quality of image reconstruction and design a greedy learning algorithm to obtain the dictionary. We classify images using the occurrence frequency of the active patches. Feature extraction is fast (about 100 ms per image) using the GPU. The experimental results show that our method improves the state of the art on a challenging material texture benchmark dataset (KTH-TIPS2). To test our method on less homogeneous or inhomogeneous images, we construct two new datasets consisting of appearance image patches of animals and vehicles cropped from the PASCAL VOC dataset. Our method outperforms competing methods on these datasets.

Application of CT texture analysis in predicting histopathological characteristics of gastric cancers

International Nuclear Information System (INIS)

Liu, Shunli; Liu, Song; Ji, Changfeng; Zheng, Huanhuan; Pan, Xia; Zhang, Yujuan; He, Jian; Zhou, Zhengyang; Guan, Wenxian; Chen, Ling; Guan, Yue; Li, Weifeng; Ge, Yun

2017-01-01

To explore the application of computed tomography (CT) texture analysis in predicting histopathological features of gastric cancers. Preoperative contrast-enhanced CT images and postoperative histopathological features of 107 patients (82 men, 25 women) with gastric cancers were retrospectively reviewed. CT texture analysis generated: (1) mean attenuation, (2) standard deviation, (3) max frequency, (4) mode, (5) minimum attenuation, (6) maximum attenuation, (7) the fifth, 10th, 25th, 50th, 75th and 90th percentiles, and (8) entropy. Correlations between CT texture parameters and histopathological features were analysed. Mean attenuation, maximum attenuation, all percentiles and mode derived from portal venous CT images correlated significantly with differentiation degree and Lauren classification of gastric cancers (r, -0.231 ∝-0.324, 0.228 ∝ 0.321, respectively). Standard deviation and entropy derived from arterial CT images also correlated significantly with Lauren classification of gastric cancers (r = -0.265, -0.222, respectively). In arterial phase analysis, standard deviation and entropy were significantly lower in gastric cancers with than those without vascular invasion; however, minimum attenuation was significantly higher in gastric cancers with than those without vascular invasion. CT texture analysis held great potential in predicting differentiation degree, Lauren classification and vascular invasion status of gastric cancers. (orig.)

Application of CT texture analysis in predicting histopathological characteristics of gastric cancers

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Liu, Shunli; Liu, Song; Ji, Changfeng; Zheng, Huanhuan; Pan, Xia; Zhang, Yujuan; He, Jian; Zhou, Zhengyang [The Affiliated Hospital of Nanjing University Medical School, Department of Radiology, Nanjing Drum Tower Hospital, Nanjing, Jiangsu Province (China); Guan, Wenxian [The Affiliated Hospital of Nanjing University Medical School, Department of Gastrointestinal Surgery, Nanjing Drum Tower Hospital, Nanjing (China); Chen, Ling [The Affiliated Hospital of Nanjing University Medical School, Department of Pathology, Nanjing Drum Tower Hospital, Nanjing, Jiangsu Province (China); Guan, Yue; Li, Weifeng; Ge, Yun [Nanjing University, School of Electronic Science and Engineering, Nanjing (China)

2017-12-15

To explore the application of computed tomography (CT) texture analysis in predicting histopathological features of gastric cancers. Preoperative contrast-enhanced CT images and postoperative histopathological features of 107 patients (82 men, 25 women) with gastric cancers were retrospectively reviewed. CT texture analysis generated: (1) mean attenuation, (2) standard deviation, (3) max frequency, (4) mode, (5) minimum attenuation, (6) maximum attenuation, (7) the fifth, 10th, 25th, 50th, 75th and 90th percentiles, and (8) entropy. Correlations between CT texture parameters and histopathological features were analysed. Mean attenuation, maximum attenuation, all percentiles and mode derived from portal venous CT images correlated significantly with differentiation degree and Lauren classification of gastric cancers (r, -0.231 ∝-0.324, 0.228 ∝ 0.321, respectively). Standard deviation and entropy derived from arterial CT images also correlated significantly with Lauren classification of gastric cancers (r = -0.265, -0.222, respectively). In arterial phase analysis, standard deviation and entropy were significantly lower in gastric cancers with than those without vascular invasion; however, minimum attenuation was significantly higher in gastric cancers with than those without vascular invasion. CT texture analysis held great potential in predicting differentiation degree, Lauren classification and vascular invasion status of gastric cancers. (orig.)

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

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Bates, Anthony; Miles, Kenneth

2017-12-01

To validate MR textural analysis (MRTA) for detection of transition zone (TZ) prostate cancer through comparison with co-registered prostate-specific membrane antigen (PSMA) PET-MR. Retrospective analysis was performed for 30 men who underwent simultaneous PSMA PET-MR imaging for staging of prostate cancer. Thirty texture features were derived from each manually contoured T2-weighted, transaxial, prostatic TZ using texture analysis software that applies a spatial band-pass filter and quantifies texture through histogram analysis. Texture features of the TZ were compared to PSMA expression on the corresponding PET images. The Benjamini-Hochberg correction controlled the false discovery rate at prostate cancer. • Prostate transition zone (TZ) MR texture analysis may assist in prostate cancer detection. • Abnormal transition zone PSMA expression correlates with altered texture on T2-weighted MR. • TZ with abnormal PSMA expression demonstrates significantly reduced MI, SD and MPP.

Clustering document fragments using background color and texture information

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Chanda, Sukalpa; Franke, Katrin; Pal, Umapada

2012-01-01

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

Predicting neo-adjuvant chemotherapy response and progression-free survival of locally advanced breast cancer using textural features of intratumoral heterogeneity on F-18 FDG PET/CT and diffusion-weighted MR imaging.

Science.gov (United States)

Yoon, Hai-Jeon; Kim, Yemi; Chung, Jin; Kim, Bom Sahn

2018-03-30

Predicting response to neo-adjuvant chemotherapy (NAC) and survival in locally advanced breast cancer (LABC) is important. This study investigated the prognostic value of tumor heterogeneity evaluated with textural analysis through F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and diffusion-weighted imaging (DWI). We enrolled 83 patients with LABC who had completed NAC and curative surgery. Tumor texture indices from pretreatment FDG PET and DWI were extracted from histogram analysis and 7 different parent matrices: co-occurrence matrix, the voxel-alignment matrix, neighborhood intensity difference matrix, intensity size-zone matrix (ISZM), normalized gray-level co-occurrence matrix (NGLCM), neighboring gray-level dependence matrix (NGLDM), and texture spectrum matrix. The predictive values of textural features were tested regarding both pathologic NAC response and progression-free survival. Among 83 patients, 46 were pathologic responders, while 37 were nonresponders. The PET texture indices from 7 parent matrices, DWI texture indices from histogram, and 1 parent matrix (NGLCM) showed significant differences according to NAC response. On multivariable analysis, number nonuniformity of PET extracted from the NGLDM was an independent predictor of pathologic response (P = .009). During a median follow-up period of 17.3 months, 14 patients experienced recurrence. High-intensity zone emphasis (HIZE) and high-intensity short-zone emphasis (HISZE) from PET extracted from ISZM were significant textural predictors (P = .011 and P = .033). On Cox regression analysis, only HIZE was a significant predictor of recurrence (P = .027), while HISZE showed borderline significance (P = .107). Tumor texture indices are useful for NAC response prediction in LABC. Moreover, PET texture indices can help to predict disease recurrence. © 2018 Wiley Periodicals, Inc.

Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images

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Ahmad Chaddad

2018-04-01

Full Text Available PurposeColorectal cancer (CRC is markedly heterogeneous and develops progressively toward malignancy through several stages which include stroma (ST, benign hyperplasia (BH, intraepithelial neoplasia (IN or precursor cancerous lesion, and carcinoma (CA. Identification of the malignancy stage of CRC pathology tissues (PT allows the most appropriate therapeutic intervention.MethodsThis study investigates multiscale texture features extracted from CRC pathology sections using 3D wavelet transform (3D-WT filter. Multiscale features were extracted from digital whole slide images of 39 patients that were segmented in a pre-processing step using an active contour model. The capacity for multiscale texture to compare and classify between PTs was investigated using ANOVA significance test and random forest classifier models, respectively.Results12 significant features derived from the multiscale texture (i.e., variance, entropy, and energy were found to discriminate between CRC grades at a significance value of p < 0.01 after correction. Combining multiscale texture features lead to a better predictive capacity compared to prediction models based on individual scale features with an average (±SD classification accuracy of 93.33 (±3.52%, sensitivity of 88.33 (± 4.12%, and specificity of 96.89 (± 3.88%. Entropy was found to be the best classifier feature across all the PT grades with an average of the area under the curve (AUC value of 91.17, 94.21, 97.70, 100% for ST, BH, IN, and CA, respectively.ConclusionOur results suggest that multiscale texture features based on 3D-WT are sensitive enough to discriminate between CRC grades with the entropy feature, the best predictor of pathology grade.

Texture analysis of articular cartilage traumatic changes in the knee calculated from morphological 3.0 T MR imaging

International Nuclear Information System (INIS)

Boutsikou, Konstantina; Kostopoulos, Spiros; Glotsos, Dimitris; Cavouras, Dionisis; Lavdas, Eleftherios; Oikonomou, Georgia; Malizos, Konstantinos; Fezoulidis, Ioannis V.; Vlychou, Marianna

2013-01-01

Objectives: In the present work, we aim to identify changes in the cartilage texture of the injured knee in young, physically active, patients by computer analysis of MRI images based on 3.0 T morphological sequences. Methods: Fifty-three young patients with training injury or trauma in one knee underwent MRI and arthroscopy. Textural features were computed from the MRI images of the knee-cartilages and two classes were formed of 28 normal and 16 with pathology only in the medial femoral condyle (MFC) cartilage. Results: Textural features with statistically significant differences between the two classes were found only at the MFC and the medial tibial condyle (MTC) areas. Three features-combinations, at the MFC or the MTC, maximized the between classes separation, thus, rendering alterations in cartilage texture due to injury more evident. The MFC cartilage in the pathology class was found more inhomogeneous in the distribution of gray-levels and of lower texture anisotropy and the opposed MTC cartilage, though normal on MRI and arthroscopy, was found to have lower texture anisotropy than cartilage in the normal class. Conclusion: Texture analysis may be used as an adjunct to morphological MR imaging for improving the detection of subtle cartilage changes and contributes to early therapeutic approach

Texture analysis of articular cartilage traumatic changes in the knee calculated from morphological 3.0 T MR imaging

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Boutsikou, Konstantina [Department of Medical Radiologic Technology, Technological Educational Institute of Athens, Ag.Spyridonos, Egaleo, Athens 12210 (Greece); Kostopoulos, Spiros; Glotsos, Dimitris; Cavouras, Dionisis [Department of Medical Instruments Technology, Technological Educational Institute of Athens, Ag.Spyridonos, Egaleo, Athens 12210 (Greece); Lavdas, Eleftherios; Oikonomou, Georgia [Department of Medical Radiologic Technology, Technological Educational Institute of Athens, Ag.Spyridonos, Egaleo, Athens 12210 (Greece); Malizos, Konstantinos [Department of Orthopaedic Surgery, University of Thessaly, School of Health Sciences, University Hospital of Larissa, Biopolis, Larissa 41110 (Greece); Fezoulidis, Ioannis V. [Department of Radiology, University of Thessaly, School of Health Sciences, University Hospital of Larissa, Biopolis, Larissa 41110 (Greece); Vlychou, Marianna, E-mail: mvlychou@med.uth.gr [Department of Radiology, University of Thessaly, School of Health Sciences, University Hospital of Larissa, Biopolis, Larissa 41110 (Greece)

2013-08-15

Objectives: In the present work, we aim to identify changes in the cartilage texture of the injured knee in young, physically active, patients by computer analysis of MRI images based on 3.0 T morphological sequences. Methods: Fifty-three young patients with training injury or trauma in one knee underwent MRI and arthroscopy. Textural features were computed from the MRI images of the knee-cartilages and two classes were formed of 28 normal and 16 with pathology only in the medial femoral condyle (MFC) cartilage. Results: Textural features with statistically significant differences between the two classes were found only at the MFC and the medial tibial condyle (MTC) areas. Three features-combinations, at the MFC or the MTC, maximized the between classes separation, thus, rendering alterations in cartilage texture due to injury more evident. The MFC cartilage in the pathology class was found more inhomogeneous in the distribution of gray-levels and of lower texture anisotropy and the opposed MTC cartilage, though normal on MRI and arthroscopy, was found to have lower texture anisotropy than cartilage in the normal class. Conclusion: Texture analysis may be used as an adjunct to morphological MR imaging for improving the detection of subtle cartilage changes and contributes to early therapeutic approach.

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

International Nuclear Information System (INIS)

Huber, M. B.; Carballido-Gamio, J.; Fritscher, K.; Schubert, R.; Haenni, M.; Hengg, C.; Majumdar, S.; Link, T. M.

2009-01-01

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

SU-E-J-113: Effects of Deformable Registration On First-Order Texture Maps Calculated From Thoracic Lung CT Scans

International Nuclear Information System (INIS)

Smith, C; Cunliffe, A; Al-Hallaq, H; Armato, S

2015-01-01

Purpose: To determine the stability of eight first-order texture features following the deformable registration of serial computed tomography (CT) scans. Methods: CT scans at two different time points from 10 patients deemed to have no lung abnormalities by a radiologist were collected. Following lung segmentation using an in-house program, texture maps were calculated from 32×32-pixel regions of interest centered at every pixel in the lungs. The texture feature value of the ROI was assigned to the center pixel of the ROI in the corresponding location of the texture map. Pixels in the square ROI not contained within the segmented lung were not included in the calculation. To quantify the agreement between ROI texture features in corresponding pixels of the baseline and follow-up texture maps, the Fraunhofer MEVIS EMPIRE10 deformable registration algorithm was used to register the baseline and follow-up scans. Bland-Altman analysis was used to compare registered scan pairs by computing normalized bias (nBias), defined as the feature value change normalized to the mean feature value, and normalized range of agreement (nRoA), defined as the range spanned by the 95% limits of agreement normalized to the mean feature value. Results: Each patient’s scans contained between 6.8–15.4 million ROIs. All of the first-order features investigated were found to have an nBias value less than 0.04% and an nRoA less than 19%, indicating that the variability introduced by deformable registration was low. Conclusion: The eight first-order features investigated were found to be registration stable. Changes in CT texture maps could allow for temporal-spatial evaluation of the evolution of lung abnormalities relating to a variety of diseases on a patient-by-patient basis. SGA and HA receives royalties and licensing fees through the University of Chicago for computer-aided diagnosis technology. Research reported in this publication was supported by the National Institute Of General

SU-E-J-113: Effects of Deformable Registration On First-Order Texture Maps Calculated From Thoracic Lung CT Scans

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Smith, C; Cunliffe, A; Al-Hallaq, H; Armato, S [The University of Chicago, Chicago, IL (United States)

2015-06-15

Purpose: To determine the stability of eight first-order texture features following the deformable registration of serial computed tomography (CT) scans. Methods: CT scans at two different time points from 10 patients deemed to have no lung abnormalities by a radiologist were collected. Following lung segmentation using an in-house program, texture maps were calculated from 32×32-pixel regions of interest centered at every pixel in the lungs. The texture feature value of the ROI was assigned to the center pixel of the ROI in the corresponding location of the texture map. Pixels in the square ROI not contained within the segmented lung were not included in the calculation. To quantify the agreement between ROI texture features in corresponding pixels of the baseline and follow-up texture maps, the Fraunhofer MEVIS EMPIRE10 deformable registration algorithm was used to register the baseline and follow-up scans. Bland-Altman analysis was used to compare registered scan pairs by computing normalized bias (nBias), defined as the feature value change normalized to the mean feature value, and normalized range of agreement (nRoA), defined as the range spanned by the 95% limits of agreement normalized to the mean feature value. Results: Each patient’s scans contained between 6.8–15.4 million ROIs. All of the first-order features investigated were found to have an nBias value less than 0.04% and an nRoA less than 19%, indicating that the variability introduced by deformable registration was low. Conclusion: The eight first-order features investigated were found to be registration stable. Changes in CT texture maps could allow for temporal-spatial evaluation of the evolution of lung abnormalities relating to a variety of diseases on a patient-by-patient basis. SGA and HA receives royalties and licensing fees through the University of Chicago for computer-aided diagnosis technology. Research reported in this publication was supported by the National Institute Of General

The effect of texture on face identification and configural information processing

Directory of Open Access Journals (Sweden)

Tzschaschel Eva Alica

2014-01-01

Full Text Available Shape and texture are an integral part of face identity. In the present study, the importance of face texture for face identification and detection of configural manipulation (i.e., spatial relation among facial features was examined by comparing grayscale face photographs (i.e., real faces and line drawings of the same faces. Whereas real faces provide information about texture and shape of faces, line drawings are lacking texture cues. A change-detection task and a forced-choice identification task were used with both stimuli categories. Within the change detection task, participants had to decide whether the size of the eyes of two sequentially presented faces had changed or not. After having made this decision, three faces were shown to the subjects and they had to identify the previously shown face among them. Furthermore, context (full vs. cropped faces and orientation (upright vs. inverted were manipulated. The results obtained in the change detection task suggest that configural information was used in processing real faces, while part-based and featural information was used in processing line-drawings. Additionally, real faces were identified more accurately than line drawings, and identification was less context but more orientation sensitive than identification of line drawings. Taken together, the results of the present study provide new evidence stressing the importance of face texture for identity encoding and configural face processing.

TU-A-12A-04: Quantitative Texture Features Calculated in Lung Tissue From CT Scans Demonstrate Consistency Between Two Databases From Different Institutions

International Nuclear Information System (INIS)

Cunliffe, A; Armato, S; Castillo, R; Pham, N; Guerrero, T; Al-Hallaq, H

2014-01-01

Purpose: To evaluate the consistency of computed tomography (CT) scan texture features, previously identified as stable in a healthy patient cohort, in esophageal cancer patient CT scans. Methods: 116 patients receiving radiation therapy (median dose: 50.4Gy) for esophageal cancer were retrospectively identified. For each patient, diagnostic-quality pre-therapy (0-183 days) and post-therapy (5-120 days) scans (mean voxel size: 0.8mm×0.8mm×2.5mm) and a treatment planning scan and associated dose map were collected. An average of 501 32x32-pixel ROIs were placed randomly in the lungs of each pre-therapy scan. ROI centers were mapped to corresponding locations in post-therapy and planning scans using the displacement vector field output by demons deformable registration. Only ROIs with mean dose <5Gy were analyzed, as these were expected to contain minimal post-treatment damage. 140 texture features were calculated in pre-therapy and post-therapy scan ROIs and compared using Bland-Altman analysis. For each feature, the mean feature value change and the distance spanned by the 95% limits of agreement were normalized to the mean feature value, yielding normalized range of agreement (nRoA) and normalized bias (nBias). Using Wilcoxon signed rank tests, nRoA and nBias were compared with values computed previously in 27 healthy patient scans (mean voxel size: 0.67mm×0.67mm×1mm) acquired at a different institution. Results: nRoA was significantly (p<0.001) larger in cancer patients than healthy patients. Differences in nBias were not significant (p=0.23). The 20 features identified previously as having nRoA<20% for healthy patients had the lowest nRoA values in the current database, with an average increase of 5.6%. Conclusion: Despite differences in CT scanner type, scan resolution, and patient health status, the same 20 features remained stable (i.e., low variability and bias) in the absence of disease changes for databases from two institutions. Identification of

Defect features, texture and mechanical properties of friction stir welded lap joints of 2A97 Al-Li alloy thin sheets

Energy Technology Data Exchange (ETDEWEB)

Chen, Haiyan [State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi' an 710072 (China); Shaanxi Key Laboratory of Friction Welding Technologies, Northwestern Polytechnical University, Xi' an 710072 (China); Fu, Li, E-mail: fuli@nwpu.edu.cn [State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi' an 710072 (China); Shaanxi Key Laboratory of Friction Welding Technologies, Northwestern Polytechnical University, Xi' an 710072 (China); Liang, Pei; Liu, Fenjun [Shaanxi Key Laboratory of Friction Welding Technologies, Northwestern Polytechnical University, Xi' an 710072 (China)

2017-03-15

1.4 mm 2A97 Al-Li alloy thin sheets were welded by friction stir lap welding using the stirring tools with different pin length at different rotational speeds. The influence of pin length and rotational speed on the defect features and mechanical properties of lap joints were investigated in detail. Microstructure observation shows that the hook defect geometry and size mainly varies with the pin length instead of the rotational speed. The size of hook defects on both the advancing side (AS) and the retreating side (RS) increased with increasing the pin length, leading to the effective sheet thickness decreased accordingly. Electron backscatter diffraction analysis reveals that the weld zones, especially the nugget zone (NZ), have the much lower texture intensity than the base metal. Some new texture components are formed in the thermo-mechanical affected zone (TMAZ) and the NZ of joint. Lap shear test results show that the failure load of joints generally decreases with increasing the pin length and the rotational speed. The joints failed during the lap shear tests at three locations: the lap interface, the RS of the top sheet and the AS of the bottom sheet. The fracture locations are mainly determined by the hook defects. - Highlights: • Hook defect size mainly varies with the pin length of stirring tool. • The proportion of LAGBs and substructured grains increases from NZ to TMAZ. • Weld zones, especially the NZ, have the much lower texture intensity than the BM. • Lap shear failure load and fracture location of joints is relative to the hook defects.

Defect features, texture and mechanical properties of friction stir welded lap joints of 2A97 Al-Li alloy thin sheets

International Nuclear Information System (INIS)

Chen, Haiyan; Fu, Li; Liang, Pei; Liu, Fenjun

2017-01-01

1.4 mm 2A97 Al-Li alloy thin sheets were welded by friction stir lap welding using the stirring tools with different pin length at different rotational speeds. The influence of pin length and rotational speed on the defect features and mechanical properties of lap joints were investigated in detail. Microstructure observation shows that the hook defect geometry and size mainly varies with the pin length instead of the rotational speed. The size of hook defects on both the advancing side (AS) and the retreating side (RS) increased with increasing the pin length, leading to the effective sheet thickness decreased accordingly. Electron backscatter diffraction analysis reveals that the weld zones, especially the nugget zone (NZ), have the much lower texture intensity than the base metal. Some new texture components are formed in the thermo-mechanical affected zone (TMAZ) and the NZ of joint. Lap shear test results show that the failure load of joints generally decreases with increasing the pin length and the rotational speed. The joints failed during the lap shear tests at three locations: the lap interface, the RS of the top sheet and the AS of the bottom sheet. The fracture locations are mainly determined by the hook defects. - Highlights: • Hook defect size mainly varies with the pin length of stirring tool. • The proportion of LAGBs and substructured grains increases from NZ to TMAZ. • Weld zones, especially the NZ, have the much lower texture intensity than the BM. • Lap shear failure load and fracture location of joints is relative to the hook defects.

Parametrization of textural patterns in 123I-ioflupane imaging for the automatic detection of Parkinsonism

International Nuclear Information System (INIS)

Martinez-Murcia, F. J.; Górriz, J. M.; Ramírez, J.; Moreno-Caballero, M.; Gómez-Río, M.

2014-01-01

Purpose: A novel approach to a computer aided diagnosis system for the Parkinson's disease is proposed. This tool is intended as a supporting tool for physicians, based on fully automated methods that lead to the classification of 123 I-ioflupane SPECT images. Methods: 123 I-ioflupane images from three different databases are used to train the system. The images are intensity and spatially normalized, then subimages are extracted and a 3D gray-level co-occurrence matrix is computed over these subimages, allowing the characterization of the texture using Haralick texture features. Finally, different discrimination estimation methods are used to select a feature vector that can be used to train and test the classifier. Results: Using the leave-one-out cross-validation technique over these three databases, the system achieves results up to a 97.4% of accuracy, and 99.1% of sensitivity, with positive likelihood ratios over 27. Conclusions: The system presents a robust feature extraction method that helps physicians in the diagnosis task by providing objective, operator-independent textural information about 123 I-ioflupane images, commonly used in the diagnosis of the Parkinson's disease. Textural features computation has been optimized by using a subimage selection algorithm, and the discrimination estimation methods used here makes the system feature-independent, allowing us to extend it to other databases and diseases

LOCAL TEXTURE DESCRIPTION FRAMEWORK FOR TEXTURE BASED FACE RECOGNITION

Directory of Open Access Journals (Sweden)

R. Reena Rose

2014-02-01

Full Text Available Texture descriptors have an important role in recognizing face images. However, almost all the existing local texture descriptors use nearest neighbors to encode a texture pattern around a pixel. But in face images, most of the pixels have similar characteristics with that of its nearest neighbors because the skin covers large area in a face and the skin tone at neighboring regions are same. Therefore this paper presents a general framework called Local Texture Description Framework that uses only eight pixels which are at certain distance apart either circular or elliptical from the referenced pixel. Local texture description can be done using the foundation of any existing local texture descriptors. In this paper, the performance of the proposed framework is verified with three existing local texture descriptors Local Binary Pattern (LBP, Local Texture Pattern (LTP and Local Tetra Patterns (LTrPs for the five issues viz. facial expression, partial occlusion, illumination variation, pose variation and general recognition. Five benchmark databases JAFFE, Essex, Indian faces, AT&T and Georgia Tech are used for the experiments. Experimental results demonstrate that even with less number of patterns, the proposed framework could achieve higher recognition accuracy than that of their base models.

A robust automatic leukocyte recognition method based on island-clustering texture

Directory of Open Access Journals (Sweden)

Xiaoshun Li

2016-01-01

Full Text Available A leukocyte recognition method for human peripheral blood smear based on island-clustering texture (ICT is proposed. By analyzing the features of the five typical classes of leukocyte images, a new ICT model is established. Firstly, some feature points are extracted in a gray leukocyte image by mean-shift clustering to be the centers of islands. Secondly, the growing region is employed to create regions of the islands in which the seeds are just these feature points. These islands distribution can describe a new texture. Finally, a distinguished parameter vector of these islands is created as the ICT features by combining the ICT features with the geometric features of the leukocyte. Then the five typical classes of leukocytes can be recognized successfully at the correct recognition rate of more than 92.3% with a total sample of 1310 leukocytes. Experimental results show the feasibility of the proposed method. Further analysis reveals that the method is robust and results can provide important information for disease diagnosis.

Image-based non-contact monitoring of skin texture changed by piloerection for emotion estimation

Science.gov (United States)

Uchida, Mihiro; Akaho, Rina; Ogawa, Keiko; Tsumura, Norimichi

2018-02-01

In this paper, we find the effective feature values of skin textures captured by non-contact camera to monitor piloerection on the skin for emotion estimation. Recently, emotion estimation is required for service robots to interact with human more naturally. There are a lot of researches of estimating emotion and additional methods are required to improve emotion estimation because using only a few methods may not give enough information for emotion estimation. In the previous study, it is necessary to fix a device on the subject's arm for detecting piloerection, but the contact monitoring can be stress itself and distract the subject from concentrating in the stimuli and evoking strong emotion. So, we focused on the piloerection as the object obtained with non-contact methods. The piloerection is observed as goose bumps on the skin when the subject is emotionally moved, scared and so on. This phenomenon is caused by contraction of arrector pili muscles with the activation of sympathetic nervous system. This piloerection changes skin texture. Skin texture is important in the cosmetic industry to evaluate skin condition. Therefore, we thought that it will be effective to evaluate the condition of skin texture for emotion estimation. The evaluations were performed by extracting the effective feature values from skin textures captured with a high resolution camera. The effective feature values should have high correlation with the degree of piloerection. In this paper, we found that standard deviation of short-line inclination angles in the texture is well correlated with the degree of piloerection.

Use of Textured Surfaces to Mitigate Sliding Friction and Wear of Lubricated and Non-Lubricated Contacts

Energy Technology Data Exchange (ETDEWEB)

Blau, Peter Julian [ORNL

2012-03-01

If properly employed, the placement of three-dimensional feature patterns, also referred to as textures, on relatively-moving, load-bearing surfaces can be beneficial to their friction and wear characteristics. For example, geometric patterns can function as lubricant supply channels or depressions in which to trap debris. They can also alter lubricant flow in a manner that produces thicker load-bearing films locally. Considering the area occupied by solid areas and spaces, textures also change the load distribution on surfaces. At least ten different attributes of textures can be specified, and their combinations offer wide latitude in surface engineering. By employing directional machining and grinding procedures, texturing has been used on bearings and seals for well over a half century, and the size scales of texturing vary widely. This report summarizes past work on the texturing of load-bearing surfaces, including past research on laser surface dimpling of ceramics done at ORNL. Textured surfaces generally show most pronounced effects when they are used in conformal or nearly conformal contacts, like that in face seals. Combining textures with other forms of surface modification and lubrication methods can offer additional benefits in surface engineering for tribology. As the literature and past work at ORNL shows, texturing does not always provide benefits. Rather, the selected pattern and arrangement of features must be matched to characteristics of the proposed application, bearing materials, and lubricants.

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

Energy Technology Data Exchange (ETDEWEB)

Huber, M. B.; Carballido-Gamio, J.; Fritscher, K.; Schubert, R.; Haenni, M.; Hengg, C.; Majumdar, S.; Link, T. M. [Department of Radiology and Biomedical Imaging, University of California, 400 Parnassus Avenue, San Francisco, California 94143 (United States); University of Health Sciences, Medical Informatics and Technology, 6060 Hall (Austria); AO Development Institute, 7270 Davos Platz (Switzerland); Medical University Innsbruck, 6020 Innsbruck (Austria); Department of Radiology and Biomedical Imaging, University of California, 400 Parnassus Avenue, San Francisco, California 94143 (United States)

2009-11-15

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

Research of second harmonic generation images based on texture analysis

Science.gov (United States)

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.

Ion beam texturing

Science.gov (United States)

Hudson, W. R.

1977-01-01

A microscopic surface texture was created by sputter-etching a surface while simultaneously sputter-depositing a lower sputter yield material onto the surface. A xenon ion-beam source was used to perform the texturing process on samples as large as 3-cm diameter. Textured surfaces have been characterized with SEM photomicrographs for a large number of materials including Cu, Al, Si, Ti, Ni, Fe, stainless steel, Au, and Ag. A number of texturing parameters are studied including the variation of texture with ion-beam powder, surface temperature, and the rate of texture growth with sputter etching time.

An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features

Directory of Open Access Journals (Sweden)

MALIK, F.

2018-02-01

Full Text Available Alzheimer's is a neurodegenerative disease caused by the destruction and death of brain neurons resulting in memory loss, impaired thinking ability, and in certain behavioral changes. Alzheimer disease is a major cause of dementia and eventually death all around the world. Early diagnosis of the disease is crucial which can help the victims to maintain their level of independence for comparatively longer time and live a best life possible. For early detection of Alzheimer's disease, we are proposing a novel approach based on fusion of multiple types of features including hemodynamic, volumetric and textural features of the brain. Our approach uses non-invasive fMRI with ensemble of classifiers, for the classification of the normal controls and the Alzheimer patients. For performance evaluation, ten-fold cross validation is used. Individual feature sets and fusion of features have been investigated with ensemble classifiers for successful classification of Alzheimer's patients from normal controls. It is observed that fusion of features resulted in improved results for accuracy, specificity and sensitivity.

Colour and rotation invariant textural features based on Markov random fields

Czech Academy of Sciences Publication Activity Database

Vácha, Pavel; Haindl, Michal; Suk, Tomáš

2011-01-01

Roč. 32, č. 6 (2011), s. 771-779 ISSN 0167-8655 R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/0593 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Image modelling * colour texture * Illumination invariance * Markov random field * rotation invariance Subject RIV: BD - Theory of Information Impact factor: 1.034, year: 2011 http://library.utia.cas.cz/separaty/2011/RO/vacha-0357314.pdf

The brass-type texture and its deviation from the copper-type texture

DEFF Research Database (Denmark)

Leffers, Torben; Ray, R.K.

2009-01-01

Our basic aim with the present review is to address the classical problem of the “fcc rolling texture transition” – the fact that fcc materials may, depending on material parameters and rolling conditions, develop two different types of rolling textures, the copper-type texture and the brass...... the subject and sketch our approach for dealing with it. We then recapitulate the decisive progress made during the nineteen sixties in the empirical description of the fcc rolling texture transition and in lining up a number of possible explanations. Then follows a section about experimental investigations...... of the brass-type texture after the nineteen sixties covering texture measurements and microstructural investigations. The main observations are: (1) The brass-type texture deviates from the copper-type texture from an early stage of texture development. (2) Deformation twinning has a decisive effect...

Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

Science.gov (United States)

Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

2015-01-01

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

Parametrization of textural patterns in {sup 123}I-ioflupane imaging for the automatic detection of Parkinsonism

Energy Technology Data Exchange (ETDEWEB)

Martinez-Murcia, F. J., E-mail: fjesusmartinez@ugr.es; Górriz, J. M.; Ramírez, J. [Signal Processing and Biomedical Applications Research Group, Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071 (Spain); Moreno-Caballero, M.; Gómez-Río, M. [Department of Nuclear Medicine, Virgen de las Nieves Hospital, Granada 18071 (Spain)

2014-01-15

Purpose: A novel approach to a computer aided diagnosis system for the Parkinson's disease is proposed. This tool is intended as a supporting tool for physicians, based on fully automated methods that lead to the classification of{sup 123}I-ioflupane SPECT images. Methods: {sup 123}I-ioflupane images from three different databases are used to train the system. The images are intensity and spatially normalized, then subimages are extracted and a 3D gray-level co-occurrence matrix is computed over these subimages, allowing the characterization of the texture using Haralick texture features. Finally, different discrimination estimation methods are used to select a feature vector that can be used to train and test the classifier. Results: Using the leave-one-out cross-validation technique over these three databases, the system achieves results up to a 97.4% of accuracy, and 99.1% of sensitivity, with positive likelihood ratios over 27. Conclusions: The system presents a robust feature extraction method that helps physicians in the diagnosis task by providing objective, operator-independent textural information about{sup 123}I-ioflupane images, commonly used in the diagnosis of the Parkinson's disease. Textural features computation has been optimized by using a subimage selection algorithm, and the discrimination estimation methods used here makes the system feature-independent, allowing us to extend it to other databases and diseases.

Evaluation of the effect of initial texture on the development of deformation texture

DEFF Research Database (Denmark)

Leffers, Torben; Juul Jensen, Dorte

1986-01-01

The authors describe a computer procedure which allows them to introduce experimental initial textures as starting conditions for texture simulation (instead of a theoretical random texture). They apply the procedure on two batches of copper with weak initial textures and on fine-grained and coarse......-grained aluminium with moderately strong initial textures. In copper the initial texture turns out to be too weak to have any significant effect. In aluminium the initial texture has a very significant effect on the simulated textures-similar to the effect it has on the experimental textures. However......, there are differences between the simulated and the experimental aluminium textures that can only be explained as a grain-size effect. Possible future applications of the procedure are discussed...

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

Energy Technology Data Exchange (ETDEWEB)

Nawrocki, J [Duke University Medical Physics Graduate Program, Durham, NC (United States); Chino, J; Craciunescu, O [Duke University Medical Center Department of Radiation Oncology, Durham, NC (United States); Das, S [University of North Carolina School of Medicine, Chapel Hill, NC (United States)

2015-06-15

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

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

International Nuclear Information System (INIS)

Nawrocki, J; Chino, J; Craciunescu, O; Das, S

2015-01-01

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

An adaptive tensor voting algorithm combined with texture spectrum

Science.gov (United States)

Wang, Gang; Su, Qing-tang; Lü, Gao-huan; Zhang, Xiao-feng; Liu, Yu-huan; He, An-zhi

2015-01-01

An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.

Fast detection of vascular plaque in optical coherence tomography images using a reduced feature set

Science.gov (United States)

Prakash, Ammu; Ocana Macias, Mariano; Hewko, Mark; Sowa, Michael; Sherif, Sherif

2018-03-01

Optical coherence tomography (OCT) images are capable of detecting vascular plaque by using the full set of 26 Haralick textural features and a standard K-means clustering algorithm. However, the use of the full set of 26 textural features is computationally expensive and may not be feasible for real time implementation. In this work, we identified a reduced set of 3 textural feature which characterizes vascular plaque and used a generalized Fuzzy C-means clustering algorithm. Our work involves three steps: 1) the reduction of a full set 26 textural feature to a reduced set of 3 textural features by using genetic algorithm (GA) optimization method 2) the implementation of an unsupervised generalized clustering algorithm (Fuzzy C-means) on the reduced feature space, and 3) the validation of our results using histology and actual photographic images of vascular plaque. Our results show an excellent match with histology and actual photographic images of vascular tissue. Therefore, our results could provide an efficient pre-clinical tool for the detection of vascular plaque in real time OCT imaging.

Finger vein recognition with personalized feature selection.

Science.gov (United States)

Xi, Xiaoming; Yang, Gongping; Yin, Yilong; Meng, Xianjing

2013-08-22

Finger veins are a promising biometric pattern for personalized identification in terms of their advantages over existing biometrics. Based on the spatial pyramid representation and the combination of more effective information such as gray, texture and shape, this paper proposes a simple but powerful feature, called Pyramid Histograms of Gray, Texture and Orientation Gradients (PHGTOG). For a finger vein image, PHGTOG can reflect the global spatial layout and local details of gray, texture and shape. To further improve the recognition performance and reduce the computational complexity, we select a personalized subset of features from PHGTOG for each subject by using the sparse weight vector, which is trained by using LASSO and called PFS-PHGTOG. We conduct extensive experiments to demonstrate the promise of the PHGTOG and PFS-PHGTOG, experimental results on our databases show that PHGTOG outperforms the other existing features. Moreover, PFS-PHGTOG can further boost the performance in comparison with PHGTOG.

Finger Vein Recognition with Personalized Feature Selection

Directory of Open Access Journals (Sweden)

Xianjing Meng

2013-08-01

Full Text Available Finger veins are a promising biometric pattern for personalized identification in terms of their advantages over existing biometrics. Based on the spatial pyramid representation and the combination of more effective information such as gray, texture and shape, this paper proposes a simple but powerful feature, called Pyramid Histograms of Gray, Texture and Orientation Gradients (PHGTOG. For a finger vein image, PHGTOG can reflect the global spatial layout and local details of gray, texture and shape. To further improve the recognition performance and reduce the computational complexity, we select a personalized subset of features from PHGTOG for each subject by using the sparse weight vector, which is trained by using LASSO and called PFS-PHGTOG. We conduct extensive experiments to demonstrate the promise of the PHGTOG and PFS-PHGTOG, experimental results on our databases show that PHGTOG outperforms the other existing features. Moreover, PFS-PHGTOG can further boost the performance in comparison with PHGTOG.

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

Science.gov (United States)

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

2016-03-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Hatt, Mathieu; Visvikis, Dimitris [University of Brest IBSAM, INSERM, UMR 1101, LaTIM, Brest (France); Tixier, Florent; Le Rest, Catherine Cheze [University Hospital, Nuclear Medicine, Poitiers (France); University of Poitiers, Medical school, EE DACTIM, Poitiers (France); Pierce, Larry; Kinahan, Paul E. [University of Washington, Imaging Research Laboratory, Seattle, WA (United States)

2017-01-15

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

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

International Nuclear Information System (INIS)

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

2017-01-01

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

A visual perceptual descriptor with depth feature for image retrieval

Science.gov (United States)

Wang, Tianyang; Qin, Zhengrui

2017-07-01

This paper proposes a visual perceptual descriptor (VPD) and a new approach to extract perceptual depth feature for 2D image retrieval. VPD mimics human visual system, which can easily distinguish regions that have different textures, whereas for regions which have similar textures, color features are needed for further differentiation. We apply VPD on the gradient direction map of an image, capture texture-similar regions to generate a VPD map. We then impose the VPD map on a quantized color map and extract color features only from the overlapped regions. To reflect the nature of perceptual distance in single 2D image, we propose and extract the perceptual depth feature by computing the nuclear norm of the sparse depth map of an image. Extracted color features and the perceptual depth feature are both incorporated to a feature vector, we utilize this vector to represent an image and measure similarity. We observe that the proposed VPD + depth method achieves a promising result, and extensive experiments prove that it outperforms other typical methods on 2D image retrieval.

Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy.

Science.gov (United States)

Pyka, Thomas; Bundschuh, Ralph A; Andratschke, Nicolaus; Mayer, Benedikt; Specht, Hanno M; Papp, Laszló; Zsótér, Norbert; Essler, Markus

2015-04-22

Textural features in FDG-PET have been shown to provide prognostic information in a variety of tumor entities. Here we evaluate their predictive value for recurrence and prognosis in NSCLC patients receiving primary stereotactic radiation therapy (SBRT). 45 patients with early stage NSCLC (T1 or T2 tumor, no lymph node or distant metastases) were included in this retrospective study and followed over a median of 21.4 months (range 3.1-71.1). All patients were considered non-operable due to concomitant disease and referred to SBRT as the primary treatment modality. Pre-treatment FDG-PET/CT scans were obtained from all patients. SUV and volume-based analysis as well as extraction of textural features based on neighborhood gray-tone difference matrices (NGTDM) and gray-level co-occurence matrices (GLCM) were performed using InterView Fusion™ (Mediso Inc., Budapest). The ability to predict local recurrence (LR), lymph node (LN) and distant metastases (DM) was measured using the receiver operating characteristic (ROC). Univariate and multivariate analysis of overall and disease-specific survival were executed. 7 out of 45 patients (16%) experienced LR, 11 (24%) LN and 11 (24%) DM. ROC revealed a significant correlation of several textural parameters with LR with an AUC value for entropy of 0.872. While there was also a significant correlation of LR with tumor size in the overall cohort, only texture was predictive when examining T1 (tumor diameter 3 cm) subgroups. No correlation of the examined PET parameters with LN or DM was shown. In univariate survival analysis, both heterogeneity and tumor size were predictive for disease-specific survival, but only texture determined by entropy was determined as an independent factor in multivariate analysis (hazard ratio 7.48, p = .016). Overall survival was not significantly correlated to any examined parameter, most likely due to the high comorbidity in our cohort. Our study adds to the growing evidence that tumor

Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy

International Nuclear Information System (INIS)

Pyka, Thomas; Bundschuh, Ralph A; Andratschke, Nicolaus; Mayer, Benedikt; Specht, Hanno M; Papp, Laszló; Zsótér, Norbert; Essler, Markus

2015-01-01

Textural features in FDG-PET have been shown to provide prognostic information in a variety of tumor entities. Here we evaluate their predictive value for recurrence and prognosis in NSCLC patients receiving primary stereotactic radiation therapy (SBRT). 45 patients with early stage NSCLC (T1 or T2 tumor, no lymph node or distant metastases) were included in this retrospective study and followed over a median of 21.4 months (range 3.1–71.1). All patients were considered non-operable due to concomitant disease and referred to SBRT as the primary treatment modality. Pre-treatment FDG-PET/CT scans were obtained from all patients. SUV and volume-based analysis as well as extraction of textural features based on neighborhood gray-tone difference matrices (NGTDM) and gray-level co-occurence matrices (GLCM) were performed using InterView Fusion™ (Mediso Inc., Budapest). The ability to predict local recurrence (LR), lymph node (LN) and distant metastases (DM) was measured using the receiver operating characteristic (ROC). Univariate and multivariate analysis of overall and disease-specific survival were executed. 7 out of 45 patients (16%) experienced LR, 11 (24%) LN and 11 (24%) DM. ROC revealed a significant correlation of several textural parameters with LR with an AUC value for entropy of 0.872. While there was also a significant correlation of LR with tumor size in the overall cohort, only texture was predictive when examining T1 (tumor diameter < = 3 cm) and T2 (>3 cm) subgroups. No correlation of the examined PET parameters with LN or DM was shown. In univariate survival analysis, both heterogeneity and tumor size were predictive for disease-specific survival, but only texture determined by entropy was determined as an independent factor in multivariate analysis (hazard ratio 7.48, p = .016). Overall survival was not significantly correlated to any examined parameter, most likely due to the high comorbidity in our cohort. Our study adds to the growing evidence

Texture Segmentation Based on Wavelet and Kohonen Network for Remotely Sensed Images

NARCIS (Netherlands)

Chen, Z.; Feng, T.J.; Feng, T.J.; Houkes, Z.

1999-01-01

In this paper, an approach based on wavelet decomposition and Kohonen's self-organizing map is developed for image segmentation. After performing the 2D wavelet transform of image, some features are extracted for texture segmentation, and the Kohonen neural network is used to accomplish feature

Diabetic peripheral neuropathy assessment through texture based analysis of corneal nerve images

Science.gov (United States)

Silva, Susana F.; Gouveia, Sofia; Gomes, Leonor; Negrão, Luís; João Quadrado, Maria; Domingues, José Paulo; Morgado, António Miguel

2015-05-01

Diabetic peripheral neuropathy (DPN) is one common complication of diabetes. Early diagnosis of DPN often fails due to the non-availability of a simple, reliable, non-invasive method. Several published studies show that corneal confocal microscopy (CCM) can identify small nerve fibre damage and quantify the severity of DPN, using nerve morphometric parameters. Here, we used image texture features, extracted from corneal sub-basal nerve plexus images, obtained in vivo by CCM, to identify DPN patients, using classification techniques. A SVM classifier using image texture features was used to identify (DPN vs. No DPN) DPN patients. The accuracies were 80.6%, when excluding diabetic patients without neuropathy, and 73.5%, when including diabetic patients without diabetic neuropathy jointly with healthy controls. The results suggest that texture analysis might be used as a complementing technique for DPN diagnosis, without requiring nerve segmentation in CCM images. The results also suggest that this technique has enough sensitivity to detect early disorders in the corneal nerves of diabetic patients.

Combining different views of mammographic texture resemblance (MTR) marker of breast cancer risk

DEFF Research Database (Denmark)

Sun, S.; Karemore, Gopal; Chernoff, Konstantin

the subsequent 4 years whereas 245 cases had a diagnosis 2-4 years post mammography. We employed the MTR supervised texture learning framework to perform risk evaluation from a single mammography view. In the framework 20,000 pixels were sampled and classified by a kNN pixel classifier. A feature selection step......PURPOSE Mammographic density is a well established breast cancer risk factor. Texture analysis in terms of the Mammographoc Texture Resemblance (MTR) marker has recently shown to add to risk segregation. Hitherto only single view MTR analysis has been performed. Standard mammography examinations...

A Spectral-Texture Kernel-Based Classification Method for Hyperspectral Images

Directory of Open Access Journals (Sweden)

Yi Wang

2016-11-01

Full Text Available Classification of hyperspectral images always suffers from high dimensionality and very limited labeled samples. Recently, the spectral-spatial classification has attracted considerable attention and can achieve higher classification accuracy and smoother classification maps. In this paper, a novel spectral-spatial classification method for hyperspectral images by using kernel methods is investigated. For a given hyperspectral image, the principle component analysis (PCA transform is first performed. Then, the first principle component of the input image is segmented into non-overlapping homogeneous regions by using the entropy rate superpixel (ERS algorithm. Next, the local spectral histogram model is applied to each homogeneous region to obtain the corresponding texture features. Because this step is performed within each homogenous region, instead of within a fixed-size image window, the obtained local texture features in the image are more accurate, which can effectively benefit the improvement of classification accuracy. In the following step, a contextual spectral-texture kernel is constructed by combining spectral information in the image and the extracted texture information using the linearity property of the kernel methods. Finally, the classification map is achieved by the support vector machines (SVM classifier using the proposed spectral-texture kernel. Experiments on two benchmark airborne hyperspectral datasets demonstrate that our method can effectively improve classification accuracies, even though only a very limited training sample is available. Specifically, our method can achieve from 8.26% to 15.1% higher in terms of overall accuracy than the traditional SVM classifier. The performance of our method was further compared to several state-of-the-art classification methods of hyperspectral images using objective quantitative measures and a visual qualitative evaluation.

Dental microwear textures: reconstructing diets of fossil mammals

International Nuclear Information System (INIS)

DeSantis, Larisa R G

2016-01-01

Dietary information of fossil mammals can be revealed via the analysis of tooth morphology, tooth wear, tooth geochemistry, and the microscopic wear patterns on tooth surfaces resulting from food processing. Although dental microwear has long been used by anthropologists and paleontologists to clarify diets in a diversity of mammals, until recently these methods focused on the counting of wear features (e.g., pits and scratches) from two-dimensional surfaces (typically via scanning electron microscopes or low-magnification light microscopes). The analysis of dental microwear textures can instead reveal dietary information in a broad range of herbivorous, omnivorous, and carnivorous mammals by characterizing microscopic tooth surfaces in three-dimensions, without the counting of individual surface features. To date, dental microwear textures in ungulates, xenarthrans, marsupials, carnivorans, and primates (including humans and their ancestors) are correlated with known dietary behavior in extant taxa and reconstruct ancient diets in a diversity of prehistoric mammals. For example, tough versus hard object feeding can be characterized across disparate phylogenetic groups and can distinguish grazers, folivorous, and flesh consumers (tougher food consumers) from woody browsers, frugivores, and bone consumers (harder object feeders). This paper reviews how dental microwear textures can be useful to reconstructing diets in a broad array of living and extinct mammals, with commentary on areas of future research. (topical review)

Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos.

Science.gov (United States)

Moghaddasi, Hanie; Nourian, Saeed

2016-06-01

Heart disease is the major cause of death as well as a leading cause of disability in the developed countries. Mitral Regurgitation (MR) is a common heart disease which does not cause symptoms until its end stage. Therefore, early diagnosis of the disease is of crucial importance in the treatment process. Echocardiography is a common method of diagnosis in the severity of MR. Hence, a method which is based on echocardiography videos, image processing techniques and artificial intelligence could be helpful for clinicians, especially in borderline cases. In this paper, we introduce novel features to detect micro-patterns of echocardiography images in order to determine the severity of MR. Extensive Local Binary Pattern (ELBP) and Extensive Volume Local Binary Pattern (EVLBP) are presented as image descriptors which include details from different viewpoints of the heart in feature vectors. Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Template Matching techniques are used as classifiers to determine the severity of MR based on textural descriptors. The SVM classifier with Extensive Uniform Local Binary Pattern (ELBPU) and Extensive Volume Local Binary Pattern (EVLBP) have the best accuracy with 99.52%, 99.38%, 99.31% and 99.59%, respectively, for the detection of Normal, Mild MR, Moderate MR and Severe MR subjects among echocardiography videos. The proposed method achieves 99.38% sensitivity and 99.63% specificity for the detection of the severity of MR and normal subjects. Copyright © 2016 Elsevier Ltd. All rights reserved.

Magnetic resonance imaging texture analysis classification of primary breast cancer

International Nuclear Information System (INIS)

Waugh, S.A.; Lerski, R.A.; Purdie, C.A.; Jordan, L.B.; Vinnicombe, S.; Martin, P.; Thompson, A.M.

2016-01-01

Patient-tailored treatments for breast cancer are based on histological and immunohistochemical (IHC) subtypes. Magnetic Resonance Imaging (MRI) texture analysis (TA) may be useful in non-invasive lesion subtype classification. Women with newly diagnosed primary breast cancer underwent pre-treatment dynamic contrast-enhanced breast MRI. TA was performed using co-occurrence matrix (COM) features, by creating a model on retrospective training data, then prospectively applying to a test set. Analyses were blinded to breast pathology. Subtype classifications were performed using a cross-validated k-nearest-neighbour (k = 3) technique, with accuracy relative to pathology assessed and receiver operator curve (AUROC) calculated. Mann-Whitney U and Kruskal-Wallis tests were used to assess raw entropy feature values. Histological subtype classifications were similar across training (n = 148 cancers) and test sets (n = 73 lesions) using all COM features (training: 75 %, AUROC = 0.816; test: 72.5 %, AUROC = 0.823). Entropy features were significantly different between lobular and ductal cancers (p < 0.001; Mann-Whitney U). IHC classifications using COM features were also similar for training and test data (training: 57.2 %, AUROC = 0.754; test: 57.0 %, AUROC = 0.750). Hormone receptor positive and negative cancers demonstrated significantly different entropy features. Entropy features alone were unable to create a robust classification model. Textural differences on contrast-enhanced MR images may reflect underlying lesion subtypes, which merits testing against treatment response. (orig.)

Magnetic resonance imaging texture analysis classification of primary breast cancer

Energy Technology Data Exchange (ETDEWEB)

Waugh, S.A.; Lerski, R.A. [Ninewells Hospital and Medical School, Department of Medical Physics, Dundee (United Kingdom); Purdie, C.A.; Jordan, L.B. [Ninewells Hospital and Medical School, Department of Pathology, Dundee (United Kingdom); Vinnicombe, S. [University of Dundee, Division of Imaging and Technology, Ninewells Hospital and Medical School, Dundee (United Kingdom); Martin, P. [Ninewells Hospital and Medical School, Department of Clinical Radiology, Dundee (United Kingdom); Thompson, A.M. [University of Texas MD Anderson Cancer Center, Department of Surgical Oncology, Houston, TX (United States)

2016-02-15

Patient-tailored treatments for breast cancer are based on histological and immunohistochemical (IHC) subtypes. Magnetic Resonance Imaging (MRI) texture analysis (TA) may be useful in non-invasive lesion subtype classification. Women with newly diagnosed primary breast cancer underwent pre-treatment dynamic contrast-enhanced breast MRI. TA was performed using co-occurrence matrix (COM) features, by creating a model on retrospective training data, then prospectively applying to a test set. Analyses were blinded to breast pathology. Subtype classifications were performed using a cross-validated k-nearest-neighbour (k = 3) technique, with accuracy relative to pathology assessed and receiver operator curve (AUROC) calculated. Mann-Whitney U and Kruskal-Wallis tests were used to assess raw entropy feature values. Histological subtype classifications were similar across training (n = 148 cancers) and test sets (n = 73 lesions) using all COM features (training: 75 %, AUROC = 0.816; test: 72.5 %, AUROC = 0.823). Entropy features were significantly different between lobular and ductal cancers (p < 0.001; Mann-Whitney U). IHC classifications using COM features were also similar for training and test data (training: 57.2 %, AUROC = 0.754; test: 57.0 %, AUROC = 0.750). Hormone receptor positive and negative cancers demonstrated significantly different entropy features. Entropy features alone were unable to create a robust classification model. Textural differences on contrast-enhanced MR images may reflect underlying lesion subtypes, which merits testing against treatment response. (orig.)

Artificial intelligence systems based on texture descriptors for vaccine development.

Science.gov (United States)

Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra

2011-02-01

The aim of this work is to analyze and compare several feature extraction methods for peptide classification that are based on the calculation of texture descriptors starting from a matrix representation of the peptide. This texture-based representation of the peptide is then used to train a support vector machine classifier. In our experiments, the best results are obtained using local binary patterns variants and the discrete cosine transform with selected coefficients. These results are better than those previously reported that employed texture descriptors for peptide representation. In addition, we perform experiments that combine standard approaches based on amino acid sequence. The experimental section reports several tests performed on a vaccine dataset for the prediction of peptides that bind human leukocyte antigens and on a human immunodeficiency virus (HIV-1). Experimental results confirm the usefulness of our novel descriptors. The matlab implementation of our approaches is available at http://bias.csr.unibo.it/nanni/TexturePeptide.zip.

Inference of segmented color and texture description by tensor voting.

Science.gov (United States)

Jia, Jiaya; Tang, Chi-Keung

2004-06-01

A robust synthesis method is proposed to automatically infer missing color and texture information from a damaged 2D image by (N)D tensor voting (N > 3). The same approach is generalized to range and 3D data in the presence of occlusion, missing data and noise. Our method translates texture information into an adaptive (N)D tensor, followed by a voting process that infers noniteratively the optimal color values in the (N)D texture space. A two-step method is proposed. First, we perform segmentation based on insufficient geometry, color, and texture information in the input, and extrapolate partitioning boundaries by either 2D or 3D tensor voting to generate a complete segmentation for the input. Missing colors are synthesized using (N)D tensor voting in each segment. Different feature scales in the input are automatically adapted by our tensor scale analysis. Results on a variety of difficult inputs demonstrate the effectiveness of our tensor voting approach.

18F-FDG PET radiomics approaches: comparing and clustering features in cervical cancer.

Science.gov (United States)

Tsujikawa, Tetsuya; Rahman, Tasmiah; Yamamoto, Makoto; Yamada, Shizuka; Tsuyoshi, Hideaki; Kiyono, Yasushi; Kimura, Hirohiko; Yoshida, Yoshio; Okazawa, Hidehiko

2017-11-01

The aims of our study were to find the textural features on 18 F-FDG PET/CT which reflect the different histological architectures between cervical cancer subtypes and to make a visual assessment of the association between 18 F-FDG PET textural features in cervical cancer. Eighty-three cervical cancer patients [62 squamous cell carcinomas (SCCs) and 21 non-SCCs (NSCCs)] who had undergone pretreatment 18 F-FDG PET/CT were enrolled. A texture analysis was performed on PET/CT images, from which 18 PET radiomics features were extracted including first-order features such as standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), second- and high-order textural features using SUV histogram, normalized gray-level co-occurrence matrix (NGLCM), and neighborhood gray-tone difference matrix, respectively. These features were compared between SCC and NSCC using a Bonferroni adjusted P value threshold of 0.0028 (0.05/18). To assess the association between PET features, a heat map analysis with hierarchical clustering, one of the radiomics approaches, was performed. Among 18 PET features, correlation, a second-order textural feature derived from NGLCM, was a stable parameter and it was the only feature which showed a robust trend toward significant difference between SCC and NSCC. Cervical SCC showed a higher correlation (0.70 ± 0.07) than NSCC (0.64 ± 0.07, P = 0.0030). The other PET features did not show any significant differences between SCC and NSCC. A higher correlation in SCC might reflect higher structural integrity and stronger spatial/linear relationship of cancer cells compared with NSCC. A heat map with a PET feature dendrogram clearly showed 5 distinct clusters, where correlation belonged to a cluster including MTV and TLG. However, the association between correlation and MTV/TLG was not strong. Correlation was a relatively independent PET feature in cervical cancer. 18 F-FDG PET textural features might reflect the

MO-DE-207B-04: Impact of Reconstruction Field of View On Radiomics Features in Computed Tomography (CT) Using a Texture Phantom

Energy Technology Data Exchange (ETDEWEB)

Shafiq ul Hassan, M; Zhang, G; Oliver, J; Moros, E [H Lee Moffitt Cancer Center and Research Institute, Tampa, FL (United States); Department of Physics, University of South Florida (United States); Latifi, K; Hunt, D; Guzman, R; Balagurunathan, Y; Gillies, R [H Lee Moffitt Cancer Center and Research Institute, Tampa, FL (United States); Mackin, D; Court, L [UT MD Anderson Cancer Center, Houston, TX (United States)

2016-06-15

Purpose: To investigate the impact of reconstruction Field of View on Radiomics features in computed tomography (CT) using a texture phantom. Methods: A rectangular Credence Cartridge Radiomics (CCR) phantom, composed of 10 different cartridges, was scanned on four different CT scanners from two manufacturers. A pre-defined scanning protocol was adopted for consistency. The slice thickness and reconstruction interval of 1.5 mm was used on all scanners. The reconstruction FOV was varied to result a voxel size ranging from 0.38 to 0.98 mm. A spherical region of interest (ROI) was contoured on the shredded rubber cartridge from CCR phantom CT scans. Ninety three Radiomics features were extracted from ROI using an in-house program. These include 10 shape, 22 intensity, 26 GLCM, 11 GLZSM, 11 RLM, 5 NGTDM and 8 fractal dimensional features. To evaluate the Interscanner variability across three scanners, a coefficient of variation (COV) was calculated for each feature group. Each group was further classified according to the COV by calculating the percentage of features in each of the following categories: COV≤ 5%, between 5 and 10% and ≥ 10%. Results: Shape features were the most robust, as expected, because of the spherical contouring of ROI. Intensity features were the second most robust with 54.5 to 64% of features with COV < 5%. GLCM features ranged from 31 to 35% for the same category. RLM features were sensitive to specific scanner and 5% variability was 9 to 54%. Almost all GLZM and NGTDM features showed COV ≥10% among the scanners. The dependence of fractal dimensions features on FOV was not consistent across different scanners. Conclusion: We concluded that reconstruction FOV greatly influence Radiomics features. The GLZSM and NGTDM are highly sensitive to FOV. funded in part by Grant NIH/NCI R01CA190105-01.

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

Science.gov (United States)

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

2016-03-01

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

TEXTURAL FRACTOGRAPHY

Directory of Open Access Journals (Sweden)

Hynek Lauschmann

2011-05-01

Full Text Available The reconstitution of the history of a fatigue process is based on the knowledge of any correspondences between the morphology of the crack surface and the velocity of the crack growth (crack growth rate - CGR. The textural fractography is oriented to mezoscopic SEM magnifications (30 to 500x. Images contain complicated textures without distinct borders. The aim is to find any characteristics of this texture, which correlate with CGR. Pre-processing of images is necessary to obtain a homogeneous texture. Three methods of textural analysis have been developed and realized as computational programs: the method based on the spectral structure of the image, the method based on a Gibbs random field (GRF model, and the method based on the idealization of light objects into a fibre process. In order to extract and analyze the fibre process, special methods - tracing fibres and a database-oriented analysis of a fibre process - have been developed.

Quantitative CT texture and shape analysis: Can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer?

Energy Technology Data Exchange (ETDEWEB)

Bayanati, Hamid; Thornhill, Rebecca E.; Souza, Carolina A.; Sethi-Virmani, Vineeta; Gupta, Ashish; Dennie, Carole [University of Ottawa, The Ottawa Hospital Research Institute, Department of Medical Imaging, The Ottawa Hospital, 501 Smyth Road, Box 232, Ottawa, ON (Canada); Maziak, Donna [University of Ottawa, The Ottawa Hospital Research Institute, Department of Surgery - Division of Thoracic Surgery, The Ottawa Hospital, Ottawa, ON (Canada); Amjadi, Kayvan [University of Ottawa, The Ottawa Hospital Research Institute, Department of Respiratory Medicine, The Ottawa Hospital, Ottawa, ON (Canada)

2014-09-13

To assess the accuracy of CT texture and shape analysis in the differentiation of benign and malignant mediastinal nodes in lung cancer. Forty-three patients with biopsy-proven primary lung malignancy with pathological mediastinal nodal staging and unenhanced CT of the thorax were studied retrospectively. Grey-level co-occurrence and run-length matrix textural features, as well as morphological features, were extracted from 72 nodes. Differences between benign and malignant features were assessed using Mann-Whitney U tests. Receiver operating characteristic (ROC) curves for each were constructed and the area under the curve (AUC) calculated with histopathology diagnosis as outcome. Combinations of features were also entered as predictors in logistic regression models and optimal threshold criteria were used to estimate sensitivity and specificity. Using optimum-threshold criteria, the combined textural and shape features identified malignant mediastinal nodes with 81 % sensitivity and 80 % specificity (AUC = 0.87, P < 0.0001). Using this combination, 84 % malignant and 71 % benign nodes were correctly classified. Quantitative CT texture and shape analysis has the potential to accurately differentiate malignant and benign mediastinal nodes in lung cancer. (orig.)

Quantitative ultrasound tissue characterization in shoulder and thigh muscles – a new approach

Directory of Open Access Journals (Sweden)

Jørgensen Kurt

2006-01-01

Full Text Available Abstract Background The echogenicity patterns of ultrasound scans contain information of tissue composition in muscles. The aim was: (1 to develop a quantitative ultrasound image analysis to characterize tissue composition in terms of intensity and structure of the ultrasound images, and (2 to use the method for characterization of ultrasound images of the supraspinatus muscle, and the vastus lateralis muscle. Methods Computerized texture analyses employing first-order and higher-order grey-scale statistics were developed to objectively characterize ultrasound images of m. supraspinatus and m. vastus lateralis from 9 healthy participants. Results The mean grey-scale intensity was higher in the vastus lateralis muscle (p -2 and for m. supraspinatus: 0.016 mm-2. Conclusion The higher intensity and the higher number of blobs in the vastus lateralis muscle indicates that the thigh muscle contained more non-contractile components than the supraspinatus muscle, and that the muscle was coarser. The image analyses supplemented each other and gave a more complete description of the tissue composition in the muscle than the mean grey-scale value alone.

Classification of Weed Species Using Artificial Neural Networks Based on Color Leaf Texture Feature

Science.gov (United States)

Li, Zhichen; An, Qiu; Ji, Changying

The potential impact of herbicide utilization compel people to use new method of weed control. Selective herbicide application is optimal method to reduce herbicide usage while maintain weed control. The key of selective herbicide is how to discriminate weed exactly. The HIS color co-occurrence method (CCM) texture analysis techniques was used to extract four texture parameters: Angular second moment (ASM), Entropy(E), Inertia quadrature (IQ), and Inverse difference moment or local homogeneity (IDM).The weed species selected for studying were Arthraxon hispidus, Digitaria sanguinalis, Petunia, Cyperus, Alternanthera Philoxeroides and Corchoropsis psilocarpa. The software of neuroshell2 was used for designing the structure of the neural network, training and test the data. It was found that the 8-40-1 artificial neural network provided the best classification performance and was capable of classification accuracies of 78%.

Human (Homo sapiens) facial attractiveness in relation to skin texture and color.

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Fink, B; Grammer, K; Thornhill, R

2001-03-01

The notion that surface texture may provide important information about the geometry of visible surfaces has attracted considerable attention for a long time. The present study shows that skin texture plays a significant role in the judgment of female facial beauty. Following research in clinical dermatology, the authors developed a computer program that implemented an algorithm based on co-occurrence matrices for the analysis of facial skin texture. Homogeneity and contrast features as well as color parameters were extracted out of stimulus faces. Attractiveness ratings of the images made by male participants relate positively to parameters of skin homogeneity. The authors propose that skin texture is a cue to fertility and health. In contrast to some previous studies, the authors found that dark skin, not light skin, was rated as most attractive.

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

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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 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.

Application of texture analysis method for mammogram density classification

Science.gov (United States)

Nithya, R.; Santhi, B.

2017-07-01

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

Possible influences on textures in unalloyed steels and their effects on steel properties

International Nuclear Information System (INIS)

Grossterlinden, R.; Imlau, K.P.; Kawalla, R.; Lotter, U.; Reip, C.-P.

1996-01-01

Textures in steels play an essential role for applications where anisotropic properties are favourable. For the example of deep-drawing steel sheet the correlation between parameters characterising the behaviour in the deep-drawing process, as Lankford r-value and planar anisotropy Δr, and the crystallographic texture is considered. Furthermore, the development of texture in the course of manufacturing cold strip is followed for unalloyed and microalloyed deep-drawing grades. For representation of typical features of textures the method of orientation distribution functions (ODF) together with the description of texture by characteristic fibres is used. In detail, the parameters influencing textures, such as chemical composition, finishing temperature in the hot-rolling mill (in relation to the austenite or ferrite region), transformation behaviour, cold-rolling reduction and the course of temperature during recrystallizing annealing, are discussed. From the given survey it may be concluded, that in the manufacturing process there are many possibilities to control the texture of the finished product. Finally, it is shown that the impact of textures on the r-value can be calculated with high precision. On the other hand, the formation of texture itself, particularly during hot-rolling, transformation and recrystallization after cold-rolling, at present can be calculated and modelled only in simple cases. (orig.)

Effect of Aqueous Extract of the Seaweed Gracilaria domingensis on the Physicochemical, Microbiological, and Textural Features of Fermented Milks.

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Tavares Estevam, Adriana Carneiro; Alonso Buriti, Flávia Carolina; de Oliveira, Tiago Almeida; Pereira, Elainy Virginia Dos Santos; Florentino, Eliane Rolim; Porto, Ana Lúcia Figueiredo

2016-04-01

The effects of the Gracilaria domingensis seaweed aqueous extract in comparison with gelatin on the physicochemical, microbial, and textural characteristics of fermented milks processed with the mixed culture SAB 440 A, composed of Streptococcus thermophilus, Lactobacillus acidophilus, and Bifidobacterium animalis ssp. lactis, were investigated. The addition of G. domingensis aqueous extract did not affect pH, titratable acidity, and microbial viability of fermented milks when compared with the control (with no texture modifier) and the products with added gelatin. Fermented milk with added the seaweed aqueous extract showed firmness, consistency, cohesiveness, and viscosity index at least 10% higher than those observed for the control product (P texture comparable to that observed for products containing only gelatin. At 5 h of fermentation, firmness and consistency increased significantly (P texture modifier in fermented milks and related dairy products. © 2016 Institute of Food Technologists®

The use of texture analysis in the vertebral morphometric study of a lordotic chub (Squalius cephalus L.

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Maurizio Manera

2014-08-01

Full Text Available Objective: To characterise vertebral aspects of a lordotic chub (Squalius cephalus L. using texture analysis by radiograph and to verify possible alterations in the vertebral microarchitecture of the lordotic tract. Methods: Four vertebrae belonging to the lordotic tract were analyzed for their texture features by means of a software package (MaZda vers. 4.6 and were compared, for the same features, to other four vertebrae cranial (anterior to the lordotic vertebrae. Results: Two representative features were selected out of 259 extracted texture features by means of the convex hull selection method. In particular, (5, 5 sum of squares (according to cooccurrence matrix method and 45° short run emphasis (according to run-length matrix method were used to classify, without classification errors, two selected spinal tracts (the lordotic one and the immediately cranial one, both corresponding to the anal fin pterigiophores vertebral portion according to the following methods: linear discriminant analysis and hierarchical clustering. Conclusions: This is the first known application of texture analysis in medical imaging applied to fish species and represents a start point for further studies in aquariology and in aquaculture, where spinal deformities occur relative frequently and are cause of economic losses.

UV laser-ablated surface textures as potential regulator of cellular response.

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Chandra, Prafulla; Lai, Karen; Sung, Hak-Joon; Murthy, N Sanjeeva; Kohn, Joachim

2010-06-01

Textured surfaces obtained by UV laser ablation of poly(ethylene terephthalate) films were used to study the effect of shape and spacing of surface features on cellular response. Two distinct patterns, cones and ripples with spacing from 2 to 25 μm, were produced. Surface features with different shapes and spacings were produced by varying pulse repetition rate, laser fluence, and exposure time. The effects of the surface texture parameters, i.e., shape and spacing, on cell attachment, proliferation, and morphology of neonatal human dermal fibroblasts and mouse fibroblasts were studied. Cell attachment was the highest in the regions with cones at ∼4 μm spacing. As feature spacing increased, cell spreading decreased, and the fibroblasts became more circular, indicating a stress-mediated cell shrinkage. This study shows that UV laser ablation is a useful alternative to lithographic techniques to produce surface patterns for controlling cell attachment and growth on biomaterial surfaces.

Structure and texture of uranium ores in exogenous deposits

International Nuclear Information System (INIS)

Danchev, V.I.

1977-01-01

Structure and texture signs of uranium rock exogenous deposits have been systematized for the first time, taking into account the slaging of the ore-formation process, connected with formation and change of containing sedimentary rocks, starting with the sedimentogenesis stage and early sediment diagenesis and their subsequent transformation in katagenesis and metamorphism processes. The main features of uranium geochemistry in the exogenous process are considered. Suggested is the genetic classification of uranium exogenous deposits in rocks of sedimentary cover, made with respect to conjugation and various ore-forming productivity of the litogenesis stage. Described are the main combinations of various rock texture and structure properties, characteristic of deposits of genetic classes and groups of the above classification. Eight most frequently occuring textures (lamellar, concretion, oolitic, coagulate, crack, mixed and impregnated) and their types are described and illustrated. Materials of soviet and foreign authors have been used to compile the atlas

Extracting Corresponding Point Based on Texture Synthesis for Nearly Flat Textureless Object Surface

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Min Mao

2015-01-01

Full Text Available Since the image feature points are always gathered at the range with significant intensity change, such as textured portions or edges of an image, which can be detected by the state-of-the-art intensity based point-detectors, there is nearly no point in the areas of low textured detected by classical interest-point detectors. In this paper we describe a novel algorithm based on affine transform and graph cut for interest point detecting and matching from wide baseline image pairs with weakly textured object. The detection and matching mechanism can be separated into three steps: firstly, the information on the large textureless areas will be enhanced by adding textures through the proposed texture synthesis algorithm TSIQ. Secondly, the initial interest-point set is detected by classical interest-point detectors. Finally, graph cuts are used to find the globally optimal set of matching points on stereo pairs. The efficacy of the proposed algorithm is verified by three kinds of experiments, that is, the influence of point detecting from synthetic texture with different texture sample, the stability under the different geometric transformations, and the performance to improve the quasi-dense matching algorithm, respectively.

The role of the background: texture segregation and figure-ground segmentation.

Science.gov (United States)

Caputo, G

1996-09-01

The effects of a texture surround composed of line elements on a stimulus within which a target line element segregates, were studied. Detection and discrimination of the target when it had the same orientation as the surround were impaired at short presentation time; on the other hand, no effect was present when they were reciprocally orthogonal. These results are interpreted as background completion in texture segregation; a texture made up of similar elements is represented as a continuous surface with contour and contrast of an embedded element inhibited. This interpretation is further confirmed with a simple line protruding from an annulus. Generally, the results are taken as evidence that local features are prevented from segmenting when they are parts of a global entity.

High throughput parallel backside contacting and periodic texturing for high-efficiency solar cells

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Daniel, Claus; Blue, Craig A.; Ott, Ronald D.

2014-08-19

Disclosed are configurations of long-range ordered features of solar cell materials, and methods for forming same. Some features include electrical access openings through a backing layer to a photovoltaic material in the solar cell. Some features include textured features disposed adjacent a surface of a solar cell material. Typically the long-range ordered features are formed by ablating the solar cell material with a laser interference pattern from at least two laser beams.

Inline inspection of textured plastics surfaces

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Michaeli, Walter; Berdel, Klaus

2011-02-01

This article focuses on the inspection of plastics web materials exhibiting irregular textures such as imitation wood or leather. They are produced in a continuous process at high speed. In this process, various defects occur sporadically. However, current inspection systems for plastics surfaces are able to inspect unstructured products or products with regular, i.e., highly periodic, textures, only. The proposed inspection algorithm uses the local binary pattern operator for texture feature extraction. For classification, semisupervised as well as supervised approaches are used. A simple concept for semisupervised classification is presented and applied for defect detection. The resulting defect-maps are presented to the operator. He assigns class labels that are used to train the supervised classifier in order to distinguish between different defect types. A concept for parallelization is presented allowing the efficient use of standard multicore processor PC hardware. Experiments with images of a typical product acquired in an industrial setting show a detection rate of 97% while achieving a false alarm rate below 1%. Real-time tests show that defects can be reliably detected even at haul-off speeds of 30 m/min. Further applications of the presented concept can be found in the inspection of other materials.

Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images.

Science.gov (United States)

Chaddad, Ahmad; Daniel, Paul; Niazi, Tamim

2018-01-01

Colorectal cancer (CRC) is markedly heterogeneous and develops progressively toward malignancy through several stages which include stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) or precursor cancerous lesion, and carcinoma (CA). Identification of the malignancy stage of CRC pathology tissues (PT) allows the most appropriate therapeutic intervention. This study investigates multiscale texture features extracted from CRC pathology sections using 3D wavelet transform (3D-WT) filter. Multiscale features were extracted from digital whole slide images of 39 patients that were segmented in a pre-processing step using an active contour model. The capacity for multiscale texture to compare and classify between PTs was investigated using ANOVA significance test and random forest classifier models, respectively. 12 significant features derived from the multiscale texture (i.e., variance, entropy, and energy) were found to discriminate between CRC grades at a significance value of p  pathology grade.

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

Science.gov (United States)

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

1999-03-01

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

Symmetric textures

International Nuclear Information System (INIS)

Ramond, P.

1993-01-01

The Wolfenstein parametrization is extended to the quark masses in the deep ultraviolet, and an algorithm to derive symmetric textures which are compatible with existing data is developed. It is found that there are only five such textures

The Influence of the Tool Surface Texture on Friction and the Surface Layers Properties of Formed Component

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Jana Šugárová

2018-03-01

Full Text Available The morphological texturing of forming tool surfaces has high potential to reduce friction and tool wear and also has impact on the surface layers properties of formed material. In order to understand the effect of different types of tool textures, produced by nanosecond fibre laser, on the tribological conditions at the interface tool-formed material and on the integrity of formed part surface layers, the series of experimental investigations have been carried out. The coefficient of friction for different texture parameters (individual feature shape, including the depth profile of the cavities and orientation of the features relative to the material flow was evaluated via a Ring Test and the surface layers integrity of formed material (surface roughness and subsurface micro hardness was also experimentally analysed. The results showed a positive effect of surface texturing on the friction coefficients and the strain hardening of test samples material. Application of surface texture consisting of dimple-like depressions arranged in radial layout contributed to the most significant friction reduction of about 40%. On the other hand, this surface texture contributed to the increase of surface roughness parameters, Ra parameter increased from 0.49 μm to 2.19 μm and the Rz parameter increased from 0.99 μm to 16.79 μm.

SU-E-T-30: Absorbed Doses Determined by Texture Analysis of Gafchromic EBT3 Films Using Scanning Electron Microscopy: A Feasibility Study

International Nuclear Information System (INIS)

Park, S; Kim, H; Ye, S

2014-01-01

Purpose: The texture analysis method is useful to estimate structural features of images as color, size, and shape. The study aims to determine a dose-response curve by texture analysis of Gafchromic EBT3 film images using scanning electron microscopy (SEM). Methods: The uncoated Gafchromic EBT3 films were prepared to directly scan over the active surface layer of EBT3 film using SEM. The EBT3 films were exposed at a dose range of 0 to 10 Gy using a 6 MV photon beam. The exposed film samples were SEM-scanned at 100X, 1000X, and 3000X magnifications. The four texture features (Homogeneity, Correlation, Contrast, and Energy) were calculated based on the gray level co-occurrence matrix (GLCM) derived from the SEM images at each dose. To validate a correlation between delivered doses and texture features, an R-squared value in linear regression was tested. Results: The results showed that the Correlation index was more suitable as dose indices than the other three texture features due to higher linearity and sensitivity of the dose response curves. Further the Correlation index of 3000X magnified SEM images with 9 pixel offsets had an R-squared value of 0.964. The differences between the delivered doses and the doses measured by this method were 0.9, 1.2, 0.2, and 0.2 Gy at 5, 10, 15, and 20 Gy, respectively. Conclusion: It seems to be feasible to convert micro-scale structural features of χ t χχχ he EBT3 films to absorbed doses using the texture analysis method

MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer - A First Preliminary Study.

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Meyer, Hans-Jonas; Schob, Stefan; Höhn, Anne Kathrin; Surov, Alexey

2017-12-01

Thyroid cancer represents the most frequent malignancy of the endocrine system with an increasing incidence worldwide. Novel imaging techniques are able to further characterize tumors and even predict histopathology features. Texture analysis is an emergent imaging technique to extract extensive data from an radiology images. The present study was therefore conducted to identify possible associations between texture analysis and histopathology parameters in thyroid cancer. The radiological database was retrospectively reviewed for thyroid carcinoma. Overall, 13 patients (3 females, 23.1%) with a mean age of 61.6 years were identified. The MaZda program was used for texture analysis. The T1-precontrast and T2-weighted images were analyzed and overall 279 texture feature for each sequence was investigated. For every patient cell count, Ki67-index and p53 count were investigated. Several significant correlations between texture features and histopathology were identified. Regarding T1-weighted images, S(0;1)Sum Averg correlated the most with cell count (r=0.82). An inverse correlations with S(5;0)AngScMom, S(5;0)DifVarnc S(5;0), DiffEntrp and GrNonZeros (r=-0.69, -0.66, -0.69 and -0.63, respectively) was also identified. For T2-weighted images, Variance with r=0.63 was the highest coefficient, WavEnLL_S3 correlated inversely with cell count (r=-0.57). WavEnLL_S2 derived from T1-weighted images was the highest coefficient r=-0.80, S(0;5)SumVarnc was positively with r=0.74. Regarding T2-weighted images WavEnHL_s-1 was inverse correlated with Ki67 index (r=-0.77). S(1;0)Correlat was with r=0.75 the best correlation with Ki67 index. For T1-weighed images S(5;0)SumofSqs was the best with r=0.65 with p53 count. For T2-weighted images S(1;-1)SumEntrp was the inverse correlation with r=-0.72, whereas S(0;4)AngScMom correlated positively with r=0.63. MRI texture analysis derived from conventional sequences reflects histopathology features in thyroid cancer. This technique

Evaluation of texture parameters for the quantitative description of multimodal nonlinear optical images from atherosclerotic rabbit arteries

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Mostaco-Guidolin, Leila B; Ko, Alex C-T; Popescu, Dan P; Smith, Michael S D; Kohlenberg, Elicia K; Sowa, Michael G [Institute for Biodiagnostics, National Research Council Canada, Winnipeg, R3B 1Y6 (Canada); Shiomi, Masashi [Institute of Experimental Animals, School of Medicine, Kobe University, Kobe 650-0017 (Japan); Major, Arkady [Department Electrical and Computer Engineering, University of Manitoba, E3-559 Engineering Building, Winnipeg, R3T 5V6 (Canada)

2011-08-21

The composition and structure of atherosclerotic lesions can be directly related to the risk they pose to the patient. Multimodal nonlinear optical (NLO) microscopy provides a powerful means to visualize the major extracellular components of the plaque that critically determine its structure. Textural features extracted from NLO images were investigated for their utility in providing quantitative descriptors of structural and compositional changes associated with plaque development. Ten texture parameters derived from the image histogram and gray level co-occurrence matrix were examined that highlight specific structural and compositional motifs that distinguish early and late stage plaques. Tonal-texture parameters could be linked to key histological features that characterize vulnerable plaque: the thickness and density of the fibrous cap, size of the atheroma, and the level of inflammation indicated through lipid deposition. Tonal and texture parameters from NLO images provide objective metrics that correspond to structural and biochemical changes that occur within the vessel wall in early and late stage atherosclerosis.

Efficient iris texture analysis method based on Gabor ordinal measures

Science.gov (United States)

Tajouri, Imen; Aydi, Walid; Ghorbel, Ahmed; Masmoudi, Nouri

2017-07-01

With the remarkably increasing interest directed to the security dimension, the iris recognition process is considered to stand as one of the most versatile technique critically useful for the biometric identification and authentication process. This is mainly due to every individual's unique iris texture. A modestly conceived efficient approach relevant to the feature extraction process is proposed. In the first place, iris zigzag "collarette" is extracted from the rest of the image by means of the circular Hough transform, as it includes the most significant regions lying in the iris texture. In the second place, the linear Hough transform is used for the eyelids' detection purpose while the median filter is applied for the eyelashes' removal. Then, a special technique combining the richness of Gabor features and the compactness of ordinal measures is implemented for the feature extraction process, so that a discriminative feature representation for every individual can be achieved. Subsequently, the modified Hamming distance is used for the matching process. Indeed, the advanced procedure turns out to be reliable, as compared to some of the state-of-the-art approaches, with a recognition rate of 99.98%, 98.12%, and 95.02% on CASIAV1.0, CASIAV3.0, and IIT Delhi V1 iris databases, respectively.

Texture classification of vegetation cover in high altitude wetlands zone

International Nuclear Information System (INIS)

Wentao, Zou; Bingfang, Wu; Hongbo, Ju; Hua, Liu

2014-01-01

The aim of this study was to investigate the utility of datasets composed of texture measures and other features for the classification of vegetation cover, specifically wetlands. QUEST decision tree classifier was applied to a SPOT-5 image sub-scene covering the typical wetlands area in Three River Sources region in Qinghai province, China. The dataset used for the classification comprised of: (1) spectral data and the components of principal component analysis; (2) texture measures derived from pixel basis; (3) DEM and other ancillary data covering the research area. Image textures is an important characteristic of remote sensing images; it can represent spatial variations with spectral brightness in digital numbers. When the spectral information is not enough to separate the different land covers, the texture information can be used to increase the classification accuracy. The texture measures used in this study were calculated from GLCM (Gray level Co-occurrence Matrix); eight frequently used measures were chosen to conduct the classification procedure. The results showed that variance, mean and entropy calculated by GLCM with a 9*9 size window were effective in distinguishing different vegetation types in wetlands zone. The overall accuracy of this method was 84.19% and the Kappa coefficient was 0.8261. The result indicated that the introduction of texture measures can improve the overall accuracy by 12.05% and the overall kappa coefficient by 0.1407 compared with the result using spectral and ancillary data

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

Science.gov (United States)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

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

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Bates, Anthony [Princess Alexandra Hospital, Brisbane (Australia); Miles, Kenneth [Princess Alexandra Hospital, Department of Diagnostic Radiology, Brisbane, QLD (Australia); University College London, Institute of Nuclear Medicine, London (United Kingdom)

2017-12-15

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

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

International Nuclear Information System (INIS)

Bates, Anthony; Miles, Kenneth

2017-01-01

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

MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study.

Science.gov (United States)

Holli-Helenius, Kirsi; Salminen, Annukka; Rinta-Kiikka, Irina; Koskivuo, Ilkka; Brück, Nina; Boström, Pia; Parkkola, Riitta

2017-12-29

The aim of this study was to use texture analysis (TA) of breast magnetic resonance (MR) images to assist in differentiating estrogen receptor (ER) positive breast cancer molecular subtypes. Twenty-seven patients with histopathologically proven invasive ductal breast cancer were selected in preliminary study. Tumors were classified into molecular subtypes: luminal A (ER-positive and/or progesterone receptor (PR)-positive, human epidermal growth factor receptor type 2 (HER2) -negative, proliferation marker Ki-67 MaZda. Texture parameters and tumour volumes were correlated with tumour prognostic factors. Textural differences were observed mainly in precontrast images. The two most discriminative texture parameters to differentiate luminal A and luminal B subtypes were sum entropy and sum variance (p = 0.003). The AUCs were 0.828 for sum entropy (p = 0.004), and 0.833 for sum variance (p = 0.003), and 0.878 for the model combining texture features sum entropy, sum variance (p = 0.001). In the LOOCV, the AUC for model combining features sum entropy and sum variance was 0.876. Sum entropy and sum variance showed positive correlation with higher Ki-67 index. Luminal B types were larger in volume and moderate correlation between larger tumour volume and higher Ki-67 index was also observed (r = 0.499, p = 0.008). Texture features which measure randomness, heterogeneity or smoothness and homogeneity may either directly or indirectly reflect underlying growth patterns of breast tumours. TA and volumetric analysis may provide a way to evaluate the biologic aggressiveness of breast tumours and provide aid in decisions regarding therapeutic efficacy.

Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier

Directory of Open Access Journals (Sweden)

Hong Wang

2015-07-01

Full Text Available The textural and spatial information extracted from very high resolution (VHR remote sensing imagery provides complementary information for applications in which the spectral information is not sufficient for identification of spectrally similar landscape features. In this study grey-level co-occurrence matrix (GLCM textures and a local statistical analysis Getis statistic (Gi, computed from IKONOS multispectral (MS imagery acquired from the Yellow River Delta in China, along with a random forest (RF classifier, were used to discriminate Robina pseudoacacia tree health levels. Specifically, eight GLCM texture features (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation were first calculated from IKONOS NIR band (Band 4 to determine an optimal window size (13 × 13 and an optimal direction (45°. Then, the optimal window size and direction were applied to the three other IKONOS MS bands (blue, green, and red for calculating the eight GLCM textures. Next, an optimal distance value (5 and an optimal neighborhood rule (Queen’s case were determined for calculating the four Gi features from the four IKONOS MS bands. Finally, different RF classification results of the three forest health conditions were created: (1 an overall accuracy (OA of 79.5% produced using the four MS band reflectances only; (2 an OA of 97.1% created with the eight GLCM features calculated from IKONOS Band 4 with the optimal window size of 13 × 13 and direction 45°; (3 an OA of 93.3% created with the all 32 GLCM features calculated from the four IKONOS MS bands with a window size of 13 × 13 and direction of 45°; (4 an OA of 94.0% created using the four Gi features calculated from the four IKONOS MS bands with the optimal distance value of 5 and Queen’s neighborhood rule; and (5 an OA of 96.9% created with the combined 16 spectral (four, spatial (four, and textural (eight features. The most important feature ranked by RF

Performance Recognition for Sulphur Flotation Process Based on Froth Texture Unit Distribution

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Mingfang He

2013-01-01

Full Text Available As an important indicator of flotation performance, froth texture is believed to be related to operational condition in sulphur flotation process. A novel fault detection method based on froth texture unit distribution (TUD is proposed to recognize the fault condition of sulphur flotation in real time. The froth texture unit number is calculated based on texture spectrum, and the probability density function (PDF of froth texture unit number is defined as texture unit distribution, which can describe the actual textual feature more accurately than the grey level dependence matrix approach. As the type of the froth TUD is unknown, a nonparametric kernel estimation method based on the fixed kernel basis is proposed, which can overcome the difficulty when comparing different TUDs under various conditions is impossible using the traditional varying kernel basis. Through transforming nonparametric description into dynamic kernel weight vectors, a principle component analysis (PCA model is established to reduce the dimensionality of the vectors. Then a threshold criterion determined by the TQ statistic based on the PCA model is proposed to realize the performance recognition. The industrial application results show that the accurate performance recognition of froth flotation can be achieved by using the proposed method.