LAUSR

Abstract Efficient and accurate segmentation of prostate is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this paper, a novel hierarchical level set clustering approach is proposed to segment prostate from MR image, which makes full use of statistics information of manual segmentation result and incorporates shape prior into the segmentation task. The medium slice of prostate MR data, which is segmented artificially, is used to offer prior information and guide the segmentation of other slices. The Bhattacharyya coefficient between manual segmentation result of medium slice and local block region of pending slice is calculated to estimate the likelihood of local prostate region in pending slice. An adaptive blurring process is implemented before the optimization of level set function to restrain the redundancy texture information and retain the edge information in the meantime. We can capture the contour of prostate with a level set evolution embedded shape prior which is derived from the segmented result of medium slice. A comparative performance evaluation is carried out over a large set of experiments using real prostate magnetic resonance images and synthetic magnetic resonance data to demonstrate the validity of our method, showing significant improvements on both segmentation accuracy and noise sensitivity comparing to the state-of-the-art approaches.