Prostate boundary detection in transrectal ultrasound images

The paper aims to improve a prostate boundary detection system by modifying a set of preprocessing algorithms, including tree-structured nonlinear filter (TSF), directional wavelet transforms (DWT) and tree-structured wavelet transform (TSWT). A new advanced automatic edge delineation model for the detection and diagnosis of prostate cancer on transrectal ultrasound (TRUS) images is presented. The model consists of a preprocessing module and a segmentation module. The preprocessing module is implemented for noise suppression, image smoothing and boundary enhancement. The active contours model is used in the segmentation module for prostate boundary detection in two-dimensional (2D) TRUS images. Experimental results show that the preprocessing module improves the accuracy and sensitivity of the segmentation module greatly when the segmented images with and without preprocessing are compared. It is believed that the proposed automatic boundary detection module for the TRUS images is a promising approach, which provides an efficient and robust detection and diagnosis strategy and acts as a "second opinion" for the physician\´s interpretation of prostate cancer.