Support vector classification for pathological prostate images based on texture features of multi-categories

This paper presents an automated system for grading pathological images of prostatic carcinoma based on a set of texture features extracted by multi-categories of methods including multi-wavelets, Gabor-filters, GLCM, and fractal dimensions. We apply 5-fold cross-validation procedure to a set of 205 pathological prostate images for training and testing. Experimental results show that the fractal dimension (FD) feature set can achieve 92.7% of CCR without feature selection and 94.1% of CCR with feature selection by using support vector machine classifier. If features of multi-categories are considered and optimized, the CCR can be promoted to 95.6%. The CCR drops to 92.7% if FD-based features are removed from the combined feature set. Such a result suggests that features of FD category have significant contributions and should be included for consideration if features are selected from multi-categories.