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Texture classification

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https://www.readbyqxmd.com/read/28333649/multi-scale-rotation-invariant-convolutional-neural-networks-for-lung-texture-classification
#1
Qiangchang Wang, Yuanjie Zheng, Gongping Yang, Weidong Jin, Xinjian Chen, Yilong Yin
We propose a new Multi-scale Rotation-invariant Convolutional Neural Network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography (HRCT). MRCNN employs Gabor-local binary pattern (Gabor-LBP) which introduces a good property in image analysis - invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches...
March 21, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28331793/texture-analysis-of-abnormal-cell-images-for-predicting-the-continuum-of-colorectal-cancer
#2
Ahmad Chaddad, Camel Tanougast
Abnormal cell (ABC) is a markedly heterogeneous tissue area and can be categorized into three main types: benign hyperplasia (BH), carcinoma (Ca), and intraepithelial neoplasia (IN) or precursor cancerous lesion. In this study, the goal is to determine and characterize the continuum of colorectal cancer by using a 3D-texture approach. ABC was segmented in preprocessing step using an active contour segmentation technique. Cell types were analyzed based on textural features extracted from the gray level cooccurrence matrices (GLCMs)...
2017: Analytical Cellular Pathology (Amsterdam)
https://www.readbyqxmd.com/read/28327449/integrated-local-binary-pattern-texture-features-for-classification-of-breast-tissue-imaged-by-optical-coherence-microscopy
#3
Sunhua Wan, Hsiang-Chieh Lee, Xiaolei Huang, Ting Xu, Tao Xu, Xianxu Zeng, Zhan Zhang, Yuri Sheikine, James L Connolly, James G Fujimoto, Chao Zhou
This paper proposes a texture analysis technique that can effectively classify different types of human breast tissue imaged by Optical Coherence Microscopy (OCM). OCM is an emerging imaging modality for rapid tissue screening and has the potential to provide high resolution microscopic images that approach those of histology. OCM images, acquired without tissue staining, however, pose unique challenges to image analysis and pattern classification. We examined multiple types of texture features and found Local Binary Pattern (LBP) features to perform better in classifying tissues imaged by OCM...
March 8, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28325604/early-prediction-of-radiotherapy-induced-parotid-shrinkage-and-toxicity-based-on-ct-radiomics-and-fuzzy-classification
#4
Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Alessia Farneti, Giovanni Mauro Cattaneo, Giovanna Rizzo, Massimo Esposito
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers...
March 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28325447/statistical-tools-for-the-temporal-analysis-and-classification-of-lung-lesions
#5
Stelmo Magalhães Barros Netto, Aristófanes Corrêa Silva, Hélio Lopes, Anselmo Cardoso de Paiva, Rodolfo Acatauassú Nunes, Marcelo Gattass
BACKGROUND AND OBJECTIVE: Lung cancer remains one of the most common cancers globally. Temporal evaluation is an important tool for analyzing the malignant behavior of lesions during treatment, or of indeterminate lesions that may be benign. This work proposes a methodology for the analysis, quantification, and visualization of small (local) and large (global) changes in lung lesions. In addition, we extract textural features for the classification of lesions as benign or malignant. METHODS: We employ the statistical concept of uncertainty to associate each voxel of a lesion to a probability that changes occur in the lesion over time...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28304378/generating-highly-accurate-prediction-hypotheses-through-collaborative-ensemble-learning
#6
Nino Arsov, Martin Pavlovski, Lasko Basnarkov, Ljupco Kocarev
Ensemble generation is a natural and convenient way of achieving better generalization performance of learning algorithms by gathering their predictive capabilities. Here, we nurture the idea of ensemble-based learning by combining bagging and boosting for the purpose of binary classification. Since the former improves stability through variance reduction, while the latter ameliorates overfitting, the outcome of a multi-model that combines both strives toward a comprehensive net-balancing of the bias-variance trade-off...
March 17, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28295386/an-integrated-segmentation-and-shape-based-classification-scheme-for-distinguishing-adenocarcinomas-from-granulomas-on-lung-ct
#7
Mehdi Alilou, Niha Beig, Mahdi Orooji, Anant Madabhushi, Prabhakar Rajiah, Michael Yang, Robert Gilkeson, Philip Linden, Vamsidhar Velcheti, Sagar Rakshit, Niyoti Reddy, Frank Jacono
PURPOSE: Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans...
March 14, 2017: Medical Physics
https://www.readbyqxmd.com/read/28288857/a-reductionist-approach-to-extract-robust-molecular-markers-from-microarray-data-series-isolating-markers-to-track-osseointegration
#8
Anwesha Barik, Satarupa Banerjee, Santanu Dhara, Nishant Chakravorty
Complexities in the full genome expression studies hinder the extraction of tracker genes to analyze the course of biological events. In this study, we demonstrate the applications of supervised machine learning methods to reduce the irrelevance in microarray data series and thereby extract robust molecular markers to track biological processes. The methodology has been illustrated by analyzing whole genome expression studies on bone-implant integration (ossointegration). Being a biological process, osseointegration is known to leave a trail of genetic footprint during the course...
March 10, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28285338/a-study-of-computer-aided-diagnosis-for-pulmonary-nodule-comparison-between-classification-accuracies-using-calculated-image-features-and-imaging-findings-annotated-by-radiologists
#9
Masami Kawagishi, Bin Chen, Daisuke Furukawa, Hiroyuki Sekiguchi, Koji Sakai, Takeshi Kubo, Masahiro Yakami, Koji Fujimoto, Ryo Sakamoto, Yutaka Emoto, Gakuto Aoyama, Yoshio Iizuka, Keita Nakagomi, Hiroyuki Yamamoto, Kaori Togashi
PURPOSE: In our previous study, we developed a computer-aided diagnosis (CADx) system using imaging findings annotated by radiologists. The system, however, requires radiologists to input many imaging findings. In order to reduce such an interaction of radiologists, we further developed a CADx system using derived imaging findings based on calculated image features, in which the system only requires few user operations. The purpose of this study is to check whether calculated image features (CFT) or derived imaging findings (DFD) can represent information in imaging findings annotated by radiologists (AFD)...
March 11, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28283997/decision-support-system-for-detection-of-papilledema-through-fundus-retinal-images
#10
Shahzad Akbar, Muhammad Usman Akram, Muhammad Sharif, Anam Tariq, Ubaid Ullah Yasin
A condition in which the optic nerve inside the eye is swelled due to increased intracranial pressure is known as papilledema. The abnormalities due to papilledema such as opacification of Retinal Nerve Fiber Layer (RNFL), dilated optic disc capillaries, blurred disc margins, absence of venous pulsations, elevation of optic disc, obscuration of optic disc vessels, dilation of optic disc veins, optic disc splinter hemorrhages, cotton wool spots and hard exudates may result in complete vision loss. The ophthalmologists detect papilledema by means of an ophthalmoscope, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound...
April 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28282641/dual-discriminative-local-coding-for-tissue-aging-analysis
#11
Yang Song, Qing Li, Fan Zhang, Heng Huang, Dagan Feng, Yue Wang, Mei Chen, Weidong Cai
In aging research, morphological age of tissue helps to characterize the effects of aging on different individuals. While currently manual evaluations are used to estimate morphological ages under microscopy, such operation is difficult and subjective due to the complex visual characteristics of tissue images. In this paper, we propose an automated method to quantify morphological ages of tissues from microscopy images. We design a new sparse representation method, namely dual discriminative local coding (DDLC), that classifies the tissue images into different chronological ages...
February 27, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28282591/a-novel-computer-aided-diagnosis-system-for-breast-mri-based-on-feature-selection-and-ensemble-learning
#12
Wei Lu, Zhe Li, Jinghui Chu
Breast cancer is a common cancer among women. With the development of modern medical science and information technology, medical imaging techniques have an increasingly important role in the early detection and diagnosis of breast cancer. In this paper, we propose an automated computer-aided diagnosis (CADx) framework for magnetic resonance imaging (MRI). The scheme consists of an ensemble of several machine learning-based techniques, including ensemble under-sampling (EUS) for imbalanced data processing, the Relief algorithm for feature selection, the subspace method for providing data diversity, and Adaboost for improving the performance of base classifiers...
March 6, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28270649/assessment-of-kidney-function-after-allograft-transplantation-by-texture-analysis
#13
Ali Abbasian Ardakani, Afshin Mohammadi, Bahareh Khalili Najafabad, Jamileh Abolghasemi
INTRODUCTION: Ultrasonography is the preferable imaging technique for monitoring and assessing complications in kidney allograft transplants. Computer-aided diagnostic system based on texture analysis in ultrasonographic imaging is recommended to identify changes in kidney function after allograft transplantation. MATERIALS AND METHODS: A total of 61 biopsy-proven kidney allograft recipients (11 rejected and 50 unrejected) were assessed by a computer-aided diagnostic system...
March 2017: Iranian Journal of Kidney Diseases
https://www.readbyqxmd.com/read/28269606/automatic-segmentation-of-multimodal-brain-tumor-images-based-on-classification-of-super-voxels
#14
M Kadkhodaei, S Samavi, N Karimi, H Mohaghegh, S M R Soroushmehr, K Ward, A All, K Najarian
Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269154/an-empirical-study-of-parallel-solutions-for-glcm-calculation-of-diffraction-images
#15
John Dixon, Junhua Ding
Feature calculation of large amount of images is time consuming. The GPU based CUDA framework offers an affordable solution for calculating image features in parallel. The research focused on an empirical study of different implementations of a general-purpose GPU-based solution for calculating Gray-Level Co-occurrence Matrices (GLCM) and associated features of diffraction images of biological cells. The GLCM features calculated from the diffraction images are used for rapid cell classification with the machine learning algorithm Support Vector Machine (SVM)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269144/comparative-study-of-texture-features-in-oct-images-at-different-scales-for-human-breast-tissue-classification
#16
Yu Gan, Xinwen Yao, Ernest Chang, Syed Bin Amir, Hanina Hibshoosh, Sheldon Feldman, Christine P Hendon
Breast cancer is the second leading cause of death in women in the United States due to cancer. Early detection of breast cancerous regions will aid the diagnosis, staging, and treatment of breast cancer. Optical coherence tomography (OCT), a non-invasive imaging modality with high resolution, has been widely used to visualize various tissue types within the human breast and has demonstrated great potential for assessing tumor margins. Imaging large resected samples with a fast imaging speed can be accomplished by under-sampling in the spatial domain, resulting in a large image scale...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268582/a-new-approach-of-oral-cancer-detection-using-bilateral-texture-features-in-digital-infrared-thermal-images
#17
M Chakraborty, S Mukhopadhyay, A Dasgupta, S Patsa, N Anjum, J G Ray
Oral cancer is one of the most prevalent form of cancer and its severity is aggrandized specially among the socio-economically backward population in developing countries. A major fraction of patient population is unable to avail diagnosis for oral cancer due to scarcity of state-of-the-art infrastructure and experienced oral and maxillofacial pathologist. Contemporary gold standard of oral cancer confirmation relies on biopsy report. But biopsy is invasive and thus patients are usually reluctant to undergo this test...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268569/automatic-detection-of-neovascularization-on-optic-disk-region-with-feature-extraction-and-support-vector-machine
#18
Shuang Yu, Di Xiao, Yogesan Kanagasingam
Neovascularization (NV) is a definitive indicator for the onset of Proliferative Diabetic Retinopathy (PDR). The new vessels are fragile and prone to bleed, leading to high risk of sudden vision loss. Automatic detection of NV is an important task in automatic Diabetic Retinopathy (DR) screening as a consequence of the unmet requirement between the growing number of DR patients and limited number of ophthalmologists. This paper focuses on the computer aided detection of neovascularization in the optic disk region...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268558/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#19
Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine
Automated texture analysis of lung computed tomography (CT) images is a critical tool in subtyping pulmonary emphysema and diagnosing chronic obstructive pulmonary disease (COPD). Texton-based methods encode lung textures with nearest-texton frequency histograms, and have achieved high performance for supervised classification of emphysema subtypes from annotated lung CT images. In this work, we first explore characterizing lung textures with sparse decomposition from texton dictionaries, using different regularization strategies, and then extend the sparsity-inducing constraint to the construction of the dictionaries...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268541/how-well-can-the-fusion-of-gabor-filters-and-local-binary-patterns-help-in-identifying-gastric-lesions
#20
Farhan Riaz, Ali Hassan, Pedro Pimentel-Nunes, Diogo Libnio E Jorge Lage, Miguel Tavares Coimbra
Gastroenterology imaging is a diagnostic procedure that incorporates various computer vision challenges for the design of assisted diagnostic systems. The most typical challenge is the design of more adequate visual descriptors that can assist the classification algorithms in getting good diagnostic results. Literature shows that most of the texture descriptors for feature extraction from gastric lesions are based on Gabor filters or local binary patterns (LBP). Although good results are obtained, these techniques have their shortcomings...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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