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Texture classification

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https://www.readbyqxmd.com/read/28227864/automatic-segmentation-of-multimodal-brain-tumor-images-based-on-classification-of-super-voxels
#1
M Kadkhodaei, S Samavi, N Karimi, H Mohaghegh, S M R Soroushmehr, K Ward, A All, K Najarian, M Kadkhodaei, S Samavi, N Karimi, H Mohaghegh, S M R Soroushmehr, K Ward, A All, K Najarian, K Ward, S M R Soroushmehr, A All, S Samavi, M Kadkhodaei, H Mohaghegh, K Najarian, N Karimi
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/28227383/an-empirical-study-of-parallel-solutions-for-glcm-calculation-of-diffraction-images
#2
John Dixon, Junhua Ding, John Dixon, Junhua Ding, Junhua Ding, John Dixon
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/28227373/comparative-study-of-texture-features-in-oct-images-at-different-scales-for-human-breast-tissue-classification
#3
Yu Gan, Xinwen Yao, Ernest Chang, Syed Bin Amir, Hanina Hibshoosh, Sheldon Feldman, Christine P Hendon, Yu Gan, Xinwen Yao, Ernest Chang, Syed Bin Amir, Hanina Hibshoosh, Sheldon Feldman, Christine P Hendon, Syed Bin Amir, Christine P Hendon, Sheldon Feldman, Yu Gan, Hanina Hibshoosh, Ernest Chang, Xinwen Yao
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/28226760/a-new-approach-of-oral-cancer-detection-using-bilateral-texture-features-in-digital-infrared-thermal-images
#4
M Chakraborty, S Mukhopadhyay, A Dasgupta, S Patsa, N Anjum, J G Ray, M Chakraborty, S Mukhopadhyay, A Dasgupta, S Patsa, N Anjum, J G Ray, S Patsa, A Dasgupta, N Anjum, M Chakraborty, S Mukhopadhyay, 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/28226747/automatic-detection-of-neovascularization-on-optic-disk-region-with-feature-extraction-and-support-vector-machine
#5
Shuang Yu, Di Xiao, Yogesan Kanagasingam, Shuang Yu, Di Xiao, Yogesan Kanagasingam, Di Xiao, Yogesan Kanagasingam, Shuang Yu
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/28226735/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#6
Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Xinyang Feng, Jie Yang, 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/28226717/how-well-can-the-fusion-of-gabor-filters-and-local-binary-patterns-help-in-identifying-gastric-lesions
#7
Farhan Riaz, Ali Hassan, Pedro Pimentel-Nunes, Diogo Libnio E Jorge Lage, Miguel Tavares Coimbra, Farhan Riaz, Ali Hassan, Pedro Pimentel-Nunes, Diogo Libnio E Jorge Lage, Miguel Tavares Coimbra, Farhan Riaz, Pedro Pimentel-Nunes, Miguel Tavares Coimbra, Diogo Libnio E Jorge Lage, Ali Hassan
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
https://www.readbyqxmd.com/read/28226675/bone-texture-characterization-for-osteoporosis-diagnosis-using-digital-radiography
#8
Keni Zheng, Sokratis Makrogiannis, Keni Zheng, Sokratis Makrogiannis, Sokratis Makrogiannis, Keni Zheng
We introduce texture classification techniques to effectively diagnose osteoporosis in bone radiography data. Osteoporosis is an age-related systemic bone skeletal disorder characterized by low bone mass and bone structure deterioriation that results in increased bone fragility and higher fracture risk. Therefore, early diagnosis can effectively predict fracture risk and prevent the disease. Automated diagnosis from digital radiographs is very challenging since the scans of healthy and osteoporotic subjects show little or no visual differences, and their density histograms mostly overlap...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28222889/pre-cancer-risk-assessment-in-habitual-smokers-from-dic-images-of-oral-exfoliative-cells-using-active-contour-and-svm-analysis
#9
Susmita Dey, Ripon Sarkar, Kabita Chatterjee, Pallab Datta, Ananya Barui, Santi P Maity
Habitual smokers are known to be at higher risk for developing oral cancer, which is increasing at an alarming rate globally. Conventionally, oral cancer is associated with high mortality rates, although recent reports show the improved survival outcomes by early diagnosis of disease. An effective prediction system which will enable to identify the probability of cancer development amongst the habitual smokers, is thus expected to benefit sizable number of populations. Present work describes a non-invasive, integrated method for early detection of cellular abnormalities based on analysis of different cyto-morphological features of exfoliative oral epithelial cells...
February 9, 2017: Tissue & Cell
https://www.readbyqxmd.com/read/28217825/preoperative-prediction-of-muscular-invasiveness-of-bladder-cancer-with-radiomic-features-on-conventional-mri-and-its-high-order-derivative-maps
#10
Xiaopan Xu, Yang Liu, Xi Zhang, Qiang Tian, Yuxia Wu, Guopeng Zhang, Jiang Meng, Zengyue Yang, Hongbing Lu
PURPOSE: To determine radiomic features which are capable of reflecting muscular invasiveness of bladder cancer (BC) and propose a non-invasive strategy for the differentiation of muscular invasiveness preoperatively. METHODS: Sixty-eight patients with clinicopathologically confirmed BC were included in this retrospective study. A total of 118 cancerous volumes of interest (VOI) were segmented from patients' T2 weighted MR images (T2WI), including 34 non-muscle invasive bladder carcinomas (NMIBCs, stage <T2) and 84 muscle invasive ones (MIBCs, stage ≥T2)...
February 20, 2017: Abdominal Radiology
https://www.readbyqxmd.com/read/28216571/multivariate-feature-selection-of-image-descriptors-data-for-breast-cancer-with-computer-assisted-diagnosis
#11
Carlos E Galván-Tejada, Laura A Zanella-Calzada, Jorge I Galván-Tejada, José M Celaya-Padilla, Hamurabi Gamboa-Rosales, Idalia Garza-Veloz, Margarita L Martinez-Fierro
Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features...
February 14, 2017: Diagnostics
https://www.readbyqxmd.com/read/28209428/an-artificial-intelligence-based-improved-classification-of-two-phase-flow-patterns-with-feature-extracted-from-acquired-images
#12
C Shanthi, N Pappa
Flow pattern recognition is necessary to select design equations for finding operating details of the process and to perform computational simulations. Visual image processing can be used to automate the interpretation of patterns in two-phase flow. In this paper, an attempt has been made to improve the classification accuracy of the flow pattern of gas/ liquid two- phase flow using fuzzy logic and Support Vector Machine (SVM) with Principal Component Analysis (PCA). The videos of six different types of flow patterns namely, annular flow, bubble flow, churn flow, plug flow, slug flow and stratified flow are recorded for a period and converted to 2D images for processing...
February 13, 2017: ISA Transactions
https://www.readbyqxmd.com/read/28207752/soilgrids250m-global-gridded-soil-information-based-on-machine-learning
#13
Tomislav Hengl, Jorge Mendes de Jesus, Gerard B M Heuvelink, Maria Ruiperez Gonzalez, Milan Kilibarda, Aleksandar Blagotić, Wei Shangguan, Marvin N Wright, Xiaoyuan Geng, Bernhard Bauer-Marschallinger, Mario Antonio Guevara, Rodrigo Vargas, Robert A MacMillan, Niels H Batjes, Johan G B Leenaars, Eloi Ribeiro, Ichsani Wheeler, Stephan Mantel, Bas Kempen
This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca...
2017: PloS One
https://www.readbyqxmd.com/read/28199039/radiomics-assessment-of-bladder-cancer-grade-using-texture-features-from-diffusion-weighted-imaging
#14
Xi Zhang, Xiaopan Xu, Qiang Tian, Baojuan Li, Yuxia Wu, Zengyue Yang, Zhengrong Liang, Yang Liu, Guangbin Cui, Hongbing Lu
PURPOSE: To 1) describe textural features from diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps that can distinguish low-grade bladder cancer from high-grade, and 2) propose a radiomics-based strategy for cancer grading using texture features. MATERIALS AND METHODS: In all, 61 patients with bladder cancer (29 in high- and 32 in low-grade groups) were enrolled in this retrospective study. Histogram- and gray-level co-occurrence matrix (GLCM)-based radiomics features were extracted from cancerous volumes of interest (VOIs) on DWI and corresponding ADC maps of each patient acquired from 3...
February 15, 2017: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/28196476/automatic-mri-segmentation-of-para-pharyngeal-fat-pads-using-interactive-visual-feature-space-analysis-for-classification
#15
Muhammad Laiq Ur Rahman Shahid, Teodora Chitiboi, Tetyana Ivanovska, Vladimir Molchanov, Henry Völzke, Lars Linsen
BACKGROUND: Obstructive sleep apnea (OSA) is a public health problem. Detailed analysis of the para-pharyngeal fat pads can help us to understand the pathogenesis of OSA and may mediate the intervention of this sleeping disorder. A reliable and automatic para-pharyngeal fat pads segmentation technique plays a vital role in investigating larger data bases to identify the anatomic risk factors for the OSA. METHODS: Our research aims to develop a context-based automatic segmentation algorithm to delineate the fat pads from magnetic resonance images in a population-based study...
February 14, 2017: BMC Medical Imaging
https://www.readbyqxmd.com/read/28194800/grain-classifier-with-computer-vision-using-adaptive-neuro-fuzzy-inference-system
#16
Kadir Sabanci, Abdurrahim Toktas, Ahmet Kayabasi
BACKGROUND: A computer vision-based classifier using adaptive neuro-fuzzy inference system (ANFIS) is designed for classifying wheat grains into bread or durum. To train and test the classifier, images of 200 wheat grains (100 for bread and 100 for durum) are taken by a high resolution camera. Visual feature data of the grains related to dimension (#4), colour (#3) and texture (#5) as inputs of the classifier is mainly acquired for each grain using image processing techniques (IPTs). In addition to this main data, 9 features are reproduced from the main features to ensure a varied population...
February 13, 2017: Journal of the Science of Food and Agriculture
https://www.readbyqxmd.com/read/28187893/computer-aided-grading-of-gliomas-based-on-local-and-global-mri-features
#17
Kevin Li-Chun Hsieh, Chung-Ming Lo, Chih-Jou Hsiao
BACKGROUND AND OBJECTIVES: A computer-aided diagnosis (CAD) system based on quantitative magnetic resonance imaging (MRI) features was developed to evaluate the malignancy of diffuse gliomas, which are central nervous system tumors. METHODS: The acquired image database for the CAD performance evaluation was composed of 34 glioblastomas and 73 diffuse lower-grade gliomas. In each case, tissues enclosed in a delineated tumor area were analyzed according to their gray-scale intensities on MRI scans...
February 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28187891/automated-classification-of-maxillofacial-cysts-in-cone-beam-ct-images-using-contourlet-transformation-and-spherical-harmonics
#18
Fatemeh Abdolali, Reza Aghaeizadeh Zoroofi, Yoshito Otake, Yoshinobu Sato
BACKGROUND AND OBJECTIVE: Accurate detection of maxillofacial cysts is an essential step for diagnosis, monitoring and planning therapeutic intervention. Cysts can be of various sizes and shapes and existing detection methods lead to poor results. Customizing automatic detection systems to gain sufficient accuracy in clinical practice is highly challenging. For this purpose, integrating the engineering knowledge in efficient feature extraction is essential. METHODS: This paper presents a novel framework for maxillofacial cysts detection...
February 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28186890/3-d-solid-texture-classification-using-locally-oriented-wavelet-transforms
#19
Yashin Dicente Cid, Henning Muller, Alexandra Platon, Pierre Poletti, Adrien Depeursinge
Many image acquisition techniques used in biomedical imaging, material analysis, and structural geology are capable of acquiring 3-D solid images. Computational analysis of these images is complex but necessary since it is difficult for humans to visualize and quantify their detailed 3-D content. One of the most common methods to analyze 3-D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3-D...
February 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28186876/automated-classification-of-breast-cancer-stroma-maturity-from-histological-images
#20
Sara Reis, Patrycja Gazinska, John Hipwell, Thomy Mertzanidou, Kalnisha Naidoo, Norman Williams, Sarah Pinder, David J Hawkes
OBJECTIVE: The tumour microenvironment plays a crucial role in regulating tumour progression by a number of different mechanisms, in particular the remodelling of collagen fibres in tumour-associated stroma, which has been reported to be related to patient survival. The underlying motivation of this work is that remodelling of collagen fibres gives rise to observable patterns in Hematoxylin and Eosin (H&E) stained slides from clinical cases of invasive breast carcinoma that the pathologist can label as mature or immature stroma...
February 7, 2017: IEEE Transactions on Bio-medical Engineering
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