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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Critical Reviews™ in Biomedical Engineering
SJR: 0.207 SNIP: 0.376 CiteScore™: 0.79

ISSN Печать: 0278-940X
ISSN Онлайн: 1943-619X

Выпуски:
Том 47, 2019 Том 46, 2018 Том 45, 2017 Том 44, 2016 Том 43, 2015 Том 42, 2014 Том 41, 2013 Том 40, 2012 Том 39, 2011 Том 38, 2010 Том 37, 2009 Том 36, 2008 Том 35, 2007 Том 34, 2006 Том 33, 2005 Том 32, 2004 Том 31, 2003 Том 30, 2002 Том 29, 2001 Том 28, 2000 Том 27, 1999 Том 26, 1998 Том 25, 1997 Том 24, 1996 Том 23, 1995

Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.2015011026
pages 183-200

Review of Texture Quantification of CT Images for Classification of Lung Diseases

Mehrdad Alemzadeh
Department of Computing and Software, McMaster University
Colm Boylan
Department of Radiology, McMaster University and St. Joseph's Health Care Hamilton
Markad V. Kamath
Department of Medicine, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8N 3Z5 Canada

Краткое описание

Computer-based identification of abnormal regions and classification of diseases using CT images of the lung has been a goal of many investigators. In this paper, we review research that has used texture analysis along with segmentation and fractal analysis. First, a review of texture methods is performed. Recent research on quantitative analysis of the lung using texture methods is categorized into six groups of computational methods: structural, statistical, model based, transform domain, texture-segmentation, and texture-fractal analysis. Finally, the applications of texture-based methods combined with either segmentation algorithms or fractal analysis is evaluated on lung CT images from patients with diseases such as emphysema, COPD, and cancer. We also discuss applications of artificial neural networks, support vector machine, k-nearest, and Bayesian methods to classify normal and diseased segments of CT images of the lung. A combination of these texture methods followed by classifiers could lead to efficient and accurate diagnosis of pulmonary diseases such as pulmonary fibrosis, emphysema, and cancer.


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