A TRUS prostate segmentation using gabor texture features and snake-like contour
A TRUS prostate segmentation using gabor texture features and snake-like contour / Sung Gyun Kim, Yeong Geon Seo
p. 103-116 ; 26 cm
수록자료: Journal of information processing systems. Korea information processing society. Volume9 , Number 1(2013 March), p. 103-116 9:1<103 ISSN 1976-913X 저자: Sung Gyun Kim, Computer Institute, Gyeongsang National University E-MAIL: email@example.com 저자: Yeong Geon Seo, Dept. of Computer Science, Gyeongsang National University E-MAIL: firstname.lastname@example.org
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 images using Gabor feature extraction and snake-like contour is presented. This method involves preprocessing, extracting Gabor feature, 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 to get each feature of prostate and nonprostate. Finally, the boundary of prostate is extracted by the snake-like contour algorithm. 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.