Adaptive Shape Prior Constrained Level Sets for Bladder MR Image Segmentation

Abstract

Three-dimensional bladder wall segmentation for thickness measuring can be very useful for bladder magnetic resonance (MR) image analysis, since thickening of the bladder wall can indicate abnormality. However, it is a challenging task due to the artifacts inside bladder lumen, weak boundaries in the apex and base areas, and complicated outside intensity distributions. To deal with these difficulties, in this paper, an adaptive shape prior constrained directional level set model is proposed to segment the inner and outer boundaries of the bladder wall. In addition, a coupled directional level set model is presented to refine the segmentation by exploiting the prior knowledge of region information and minimum thickness. With our proposed method, the influence of the artifacts in the bladder lumen and the complicated outside tissues surrounding the bladder can be appreciably reduced. Furthermore, the leakage on the weak boundaries can be avoided. Compared with other related methods, better results were obtained on 11 patients' 3-D bladder MR images by using the proposed method.

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