Prostate Disease Diagnosis from CT Images Using GA Optimized SMRT Based Texture Features

Abstract

Nowadays prostate disease is very common in adult and elderly men. Since all types of prostate diseases are having similar symptoms, it is difficult to diagnose malignant prostate at an early stage. In this work an attempt is made to identify the types of prostate diseases from abdomen CT images of the patients using texture analysis. Prostate region is segmented from the CT image slice. Texture features are extracted from the segmented images using an evolving transform named Sequency based Mapped Real Transform (SMRT). Six different SMRT feature sets are derived by varying sub image size and block size. Each feature set is optimized using Genetic Algorithm (GA). The best feature set is selected based on classification accuracy. KNN classifier used. SMRT texture feature set with sub image size and block size 32 gives high classification accuracy.