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Proceedings Paper

Computer-aided detection of bladder tumors based on the thickness mapping of bladder wall in MR images
Author(s): Hongbin Zhu; Chaijie Duan; Ruirui Jiang; Lihong Li; Yi Fan; Xiaokang Yu; Wei Zeng; Xianfeng Gu; Zhengrong Liang
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Paper Abstract

Bladder cancer is reported to be the fifth leading cause of cancer deaths in the United States. Recent advances in medical imaging technologies, such as magnetic resonance (MR) imaging, make virtual cystoscopy a potential alternative with advantages as being a safe and non-invasive method for evaluation of the entire bladder and detection of abnormalities. To help reducing the interpretation time and reading fatigue of the readers or radiologists, we introduce a computer-aided detection scheme based on the thickness mapping of the bladder wall since locally-thickened bladder wall often appears around tumors. In the thickness mapping method, the path used to measure the thickness can be determined without any ambiguity by tracing the gradient direction of the potential field between the inner and outer borders of the bladder wall. The thickness mapping of the three-dimensional inner border surface of the bladder is then flattened to a twodimensional (2D) gray image with conformal mapping method. In the 2D flattened image, a blob detector is applied to detect the abnormalities, which are actually the thickened bladder wall indicating bladder lesions. Such scheme was tested on two MR datasets, one from a healthy volunteer and the other from a patient with a tumor. The result is preliminary, but very promising with 100% detection sensitivity at 7 FPs per case.

Paper Details

Date Published: 12 March 2010
PDF: 8 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76234H (12 March 2010); doi: 10.1117/12.844439
Show Author Affiliations
Hongbin Zhu, Stony Brook Univ. (United States)
Chaijie Duan, Stony Brook Univ. (United States)
Peking Univ. (China)
Ruirui Jiang, Stony Brook Univ. (United States)
Lihong Li, City Univ. of New York at College of Staten Island (United States)
Yi Fan, Stony Brook Univ. (United States)
Xiaokang Yu, Stony Brook Univ. (United States)
Wei Zeng, Stony Brook Univ. (United States)
Xianfeng Gu, Stony Brook Univ. (United States)
Zhengrong Liang, Stony Brook Univ. (United States)

Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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