Quick Reduct-ACO based feature selection for TRUS prostate cancer image classification

Abstract

Ultrasound imaging is most suitable method for early detection of prostate cancer. It is very difficult to distinguish benign and malignant in the early stage of cancer. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based classification system can provide a second opinion to the radiologists. Generally objects are described in terms of a set of measurable features in pattern recognition. Feature selection is a process of selecting the most wanted or dominating features set from the original features set in order to reduce the cost of data visualization and increasing classification efficiency and accuracy. The Region of Interest (ROI) is identified from the Transrectal Ultrasound (TRUS) images using DBSCAN clustering with morphological operators. Then the statistical texture features are extracted from the ROIs. Rough Set based Quick Reduct (QR) and Evolutionary based Ant Colony Optimization (ACO) is studied. In this paper, Hybridization of Rough Set based QR and ACO is proposed for dimensionality reduction. The selected features may have the best discriminatory power for classifying prostate cancer based on TRUS images. Support Vector Machine (SVM) is tailored for evaluation of the proposed feature selection methods through classification. Then, the comparative analysis is performed among these methods. Experimental results show that the proposed method QR-ACO produces significant results. Number of features selected using QR-ACO algorithm is minimal, and is successful and has high detection accuracy.