Review of Texture Quantification of CT Images for Classification of Lung Diseases.
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.
Department of Computing and Software, McMaster University.
Mehrdad
M
Alemzadeh
Department of Radiology, McMaster University and St. Joseph's Health Care Hamilton.
Colm
C
Boylan
Department of Medicine, McMaster University, Hamilton, Canada.
Markad V
MV
Kamath
D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008171:Lung Diseases / Q000145:classification* / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D016571:Neural Networks, Computer; D029424:Pulmonary Disease, Chronic Obstructive / Q000000981:diagnostic imaging; D011656:Pulmonary Emphysema / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed