Artificial life feature selection techniques for prostrate cancer diagnosis using TRUS images

This paper presents two novel feature selection techniques for the purpose of prostate tissue characterization based on Trans-rectal Ultrasound (TRUS) images. First, suspected cancerous regions of interest (ROIs) are identified from the segmented TRUS images using Gabor fillers. Next, second and higher order statistical texture features are constructed for these ROIs. Furthermore, a representative feature subset with the best discriminatory power among the constructed features is selected using two artificial life techniques: the Particle Swarm Optimization (PSO) and the Ant Colony Optimization (ACO). Both the PSO and ACO are tailored to fit the binary nature of the feature selection problem. The results are compared to the results obtained using the Genetic Algorithm (GA) feature selection approach. When Support Vector Machine (SVM) classifier is applied for the purpose of tissue characterization, the features obtained using the PSO and ACO outperforms the features obtained using the GA, i.e., they are capable of discriminating between suspicious cancerous and non-cancerous in a better accuracy. The obtained results demonstrate excellent tissue characterization with 83.3% sensitivity, 100% specificity and 94% overall accuracy.