A Prostate Segmentation of TRUS Image using Average Shape Model and SIFT Features


KIPS Transactions on Software and Data Engineering, Vol. 1, No. 3, pp. 187-194, Dec. 2012
10.3745/KTSDE.2012.1.3.187, Full Text:

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, transrectal ultrasound(TRUS) images are being used because the cost is low. But, accurate detection of prostate boundaries is a challenging and difficult task due to weak prostate boundaries, speckle noises and the short range of gray levels. This paper proposes a method for automatic prostate segmentation in TRUS images using its average shape model and invariant features. This approach consists of 4 steps. First, it detects the probe position and the two straight lines connected to the probe using edge distribution. Next, it acquires 3 prostate patches which are in the middle of average model. The patches will be used to compare the features of prostate and nonprostate. Next, it compares and classifies which blocks are similar to 3 representative patches. Last, the boundaries from prior classification and the rough boundaries from first step are used to determine the segmentation. A number of experiments are conducted to validate this method and results showed that this new approach extracted the prostate boundary with less than 7.78% relative to boundary provided manually by experts.


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Cite this article
[IEEE Style]
S. B. Kim and Y. G. Seo, "A Prostate Segmentation of TRUS Image using Average Shape Model and SIFT Features," KIPS Transactions on Software and Data Engineering, vol. 1, no. 3, pp. 187-194, 2012. DOI: 10.3745/KTSDE.2012.1.3.187.

[ACM Style]
Sang Bok Kim and Yeong Geon Seo. 2012. A Prostate Segmentation of TRUS Image using Average Shape Model and SIFT Features. KIPS Transactions on Software and Data Engineering, 1, 3, (2012), 187-194. DOI: 10.3745/KTSDE.2012.1.3.187.