Computer-aided diagnosis for prostate cancer using support vector machine

Abstract

The work in this paper aims for analyzing texture features of the prostate using Trans-Rectal Ultra-Sound images (TRUS) images for tissue characterization. This research is expected to assist beginner radiologists with the decision making. Moreover it will also assist in determining the biopsy locations. Texture feature analysis is composed of four stages. The first stage is automatically identifying Regions Of Interest (ROI), a step that was usually done either by an expert radiologist or by dividing the whole image into smaller squares that represent regions of interest. The second stage is extracting the statistical features from the identified ROIs. Two different statistical feature sets were used in this study; the first is Grey Level Dependence Matrix features. The second feature set is Grey level difference vector features. These constructed features are then ranked using Mutual Information (MI) feature selection algorithm that maximizes MI between feature and class. The obtained feature sets, the combined feature set as well as the reduced feature subset were examined using Support Vector Machine (SVM) classifier, a well established classifier that is suitable for noisy data such as those obtained from the ultrasound images. The obtained sensitivity is 83.3%, specificity ranges from 90% to 100% and accuracy ranges from 87.5% to 93.75%.