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Automatic segmentation of bladder in CT images

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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.

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Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China

    Feng Shi & Jie Yang

  2. CREATIS, INSA of Lyon, University of Lyon 1, Villeurbanne, France

    Yue-min Zhu

Authors
  1. Feng Shi
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  2. Jie Yang
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  3. Yue-min Zhu
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Corresponding author

Correspondence to Feng Shi.

Additional information

Project (No. 60675023) supported by the National Natural Science Foundation of China

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Shi, F., Yang, J. & Zhu, Ym. Automatic segmentation of bladder in CT images. J. Zhejiang Univ. Sci. A 10, 239–246 (2009). https://doi.org/10.1631/jzus.A0820157

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  • DOI: https://doi.org/10.1631/jzus.A0820157

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