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Proceedings Paper

A new partial volume segmentation approach to extract bladder wall for computer-aided detection in virtual cystoscopy
Author(s): Lihong Li; Zigang Wang; Xiang Li; Xinzhou Wei; Howard L. Adler; Wei Huang; Syed A. Rizvi; Hong Meng; Donald P. Harrington; Zhengrong Liang
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Paper Abstract

We propose a new partial volume (PV) segmentation scheme to extract bladder wall for computer aided detection (CAD) of bladder lesions using multispectral MR images. Compared with CT images, MR images provide not only a better tissue contrast between bladder wall and bladder lumen, but also the multispectral information. As multispectral images are spatially registered over three-dimensional space, information extracted from them is more valuable than that extracted from each image individually. Furthermore, the intrinsic T1 and T2 contrast of the urine against the bladder wall eliminates the invasive air insufflation procedure. Because the earliest stages of bladder lesion growth tend to develop gradually and migrate slowly from the mucosa into the bladder wall, our proposed PV algorithm quantifies images as percentages of tissues inside each voxel. It preserves both morphology and texture information and provides tissue growth tendency in addition to the anatomical structure. Our CAD system utilizes a multi-scan protocol on dual (full and empty of urine) states of the bladder to extract both geometrical and texture information. Moreover, multi-scan of transverse and coronal MR images eliminates motion artifacts. Experimental results indicate that the presented scheme is feasible towards mass screening and lesion detection for virtual cystoscopy (VC).

Paper Details

Date Published: 30 April 2004
PDF: 8 pages
Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004); doi: 10.1117/12.535913
Show Author Affiliations
Lihong Li, CUNY/College of Staten Island (United States)
SUNY/Stone Brook (United States)
Zigang Wang, SUNY/Stony Brook (United States)
Xiang Li, Columbia Univ. (United States)
Xinzhou Wei, CUNY/Brooklyn (United States)
Howard L. Adler, SUNY/Stony Brook (United States)
Wei Huang, SUNY/Stony Brook (United States)
Syed A. Rizvi, CUNY/College of Staten Island (United States)
Hong Meng, SUNY/Stony Brook (United States)
Donald P. Harrington, SUNY/Stony Brook (United States)
Zhengrong Liang, SUNY/Stony Brook (United States)


Published in SPIE Proceedings Vol. 5369:
Medical Imaging 2004: Physiology, Function, and Structure from Medical Images
Amir A. Amini; Armando Manduca, Editor(s)

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