A TRUS prostate segmentation using Gabor texture features and snake-like contour

Abstract

Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound(TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation in TRUS imagesusing Gabor feature extraction and snake-like contour is presented. This method involves preprocessing, extracting Gaborfeature, training, and prostate segmentation. The speckle reduction for preprocessing step has been achieved by using stick filter and top-hat transform has been implemented for smoothing the contour. A Gabor filter bank for extraction of rotationinvariant texture features has been implemented. A support vector machine(SVM) for training step has been used toget each feature of prostate and nonprostate. Finally, the boundary of prostate is extracted by the snake-like contouralgorithm. A number of experiments are conducted to validate this method and results showed that this new algorithm extracted the prostate boundary with less than 10.2% of the accuracy which is relative to boundary provided manually by experts. ? 2013 KIPS.