Classification of bladder cancer on radiotherapy planning CT images using textural features

Abstract

Highly reliable classification of anatomical regions is an important step in the delineation of the gross tumour volume (GTV) in computed tomography (CT) images during radiotherapy planning. In this study pixel-based statistics such as mean and variance were insufficient for classifying the bladder, rectum and a control region. Statistical texture analysis were used to extract features from gray-tone spatial dependence matrices (GTSDM). The features were de-correlated and reduced using principal component analysis (PCA), and the principal components (PC) were classified by a naive Bayes classifier (NBC). The results suggests that the three most significant PC of the 56 features from GTSDM with distances d = 1,2,3,4 give the highest average correct classification percentage.