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