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.