Computer Aided Detection of prostate cancer based on GDA and predictive deconvolution

Abstract

A Computer-Aided Detection (CAD) scheme to support prostate cancer diagnosis based on ultrasound images is presented. The approach described in this work employs a multifeature classification model. To indentify features highly correlated to the pathologic state of the tissue we use a Feature Selection algorithm based on mutual information. System-dependent effects are removed through predictive deconvolution and this operation results in increasing quality of images and discriminating power of features. A comparison of the classification model applied before and after deconvolution shows a gain in accuracy and area under the ROC curve. The use of deconvolution as preprocessing step in CAD schemes can improve prostate cancer detection.