Detecting bladder abnormalities based on inter-layer intensity curve for virtual cystoscopy

  • Authors:

  • Fanghua Liu;Chaijie Duan;Kehong Yuan;Zhengrong Liang;Shanglian Bao

  • Affiliations:

  • Research Center for Biomedical Engineering of Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;Research Center for Biomedical Engineering of Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;Research Center for Biomedical Engineering of Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;Department of Radiology, State University of New York, Stony Brook, NY;Beijing Key Lab of Medical Physics and Engineering, Peking University, Beijing, China

  • Venue:
  • MICCAI'10 Proceedings of the Second international conference on Virtual Colonoscopy and Abdominal Imaging: computational challenges and clinical opportunities
  • Year:
  • 2010

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Abstract

This paper presents a level set based method for bladder abnormality detection on T1-weighted MR images. First, the bladder wall is segmented by using a coupled level set framework, in which the inner and outer borders of the bladder wall are extracted by two level set functions. Then, the middle layer of the bladder wall is founded and represented by a new level set function. Finally, the new level set function divides the bladder wall into several layers. The inter-layer intensity of all voxels in each layer is sorted in ascending order to generate the inter-layer intensity curve. The results prove the effectiveness of inter-layer intensity curve in indicating the emerging of the bladder abnormalities.