An Energy-Based Segmentation of Prostate from Ultrasouind Images using Dot-Pattern Select Cells

Automatic segmentation of prostate boundaries from transrectal ultrasound (TRUS) images still poses significant challenge in minimally-invasive surgical procedures. The presence of strong speckle noise and shadow artifacts limits the effectiveness of classical segmentation schemes. Several model-based and feature-approaches have been proposed for segmentation of the prostate. In this paper, we propose a new energy-based method for segmentation of ultrasound prostate images using active contour modeling guided by dot-pattern textural energy map. First, impulsive noise and speckles are reduced with median filtering and top-hat transform. Prostate features are then extracted from the filtered images using non-linear dot-pattern select operator. An elastic template shape model that incorporates a priori knowledge of the average geometric shape of the prostate boundaries as well as the energy derived from the dot-pattern feature image are utilized to search for the optimal prostate contour. A number of experiments comparing the extracted contours with manually-delineated contours validated the performance of our method.