Computer aided diagnosis of CT focal liver lesions by an ensemble of neural network and statistical classifiers

A computer aided diagnosis (CAD) system for the characterization of hepatic tissue from computed tomography (CT) images is presented. Regions of interest (ROI's) corresponding to four types of hepatic tissue are drawn by an experienced radiologist on abdominal non-enhanced CT images. For each ROI, five sets of texture features are extracted and combined to provide input to the CAD system. If the dimensionality of a feature set is greater than a predefined threshold, appropriate feature selection based on a genetic algorithm (GA) is applied. Classification of the ROI is then carried out using an ensemble of classifiers consisting of two neural network (NN) and three statistical classifiers. The final decision of the CAD system is based on the application of a voting scheme across the outputs of the primary classifiers of the ensemble. A classification performance of the order of 90.63% was finally achieved.