Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China; CREATIS, INSA of Lyon, University of Lyon 1, Villeurbanne, France
Abstract Segmentation of the bladder in computerized tomography (CT) images is an important step in radiation therapy planning of prostate cancer. We present a new segmentation scheme to automatically delineate the bladder contour in CT images with three major steps. First, we use the mean shift algorithm to obtain a clustered image containing the rough contour of the bladder, which is then extracted in the second step by applying a region-growing algorithm with the initial seed point selected from a line-by-line scanning process. The third step is to refine the bladder contour more accurately using the rolling-ball algorithm. These steps are then extended to segment the bladder volume in a slice-by-slice manner. The obtained results were compared to manual segmentation by radiation oncologists. The average values of sensitivity, specificity, positive predictive value, negative predictive value, and Hausdorff distance are 86.5%, 96.3%, 90.5%, 96.5%, and 2.8 pixels, respectively. The results show that the bladder can be accurately segmented.
Feng SHI, Jie YANG, Yue-min ZHU. Automatic segmentation of bladder in CT images. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2009, 10(2): 239-246.
