Classification for Pathological Prostate Images Based on Fractal Analysis

This paper presents a new method to automatically grade pathological prostate images according to Gleason grading system. Two feature extraction methods were proposed based on fractal dimension to analyze the variations of intensity and texture complexity in images. Each image can be classified into appropriate grade by using Bayes classifier and k-Nearest-Neighbor (k-NN) classifier, respectively. Leaving-One-Out approach was used to estimate the correct classification rates. Experimental results showed that 92.86% of accuracy can be achieved by using Bayes classifier and 89.01% of accuracy can be achieved by using k-NN classifier for a set of 182 pathological prostate images.