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Proceedings Paper

Robust colon residue detection using vector-quantization-based classification for virtual colonoscopy
Author(s): Sarang Lakare; Dongqing Chen; Lihong Li; Arie E. Kaufman; Mark R. Wax; Zhengrong Liang
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Paper Abstract

We present an automatic and robust tagged-residue detection technique using vector quantization based classification. This technique enables electronic cleansing even on poorly tagged datasets, leading to more effective virtual colonoscopy. In order to reduce the sensitivity towards intensity variation among the tagged residual material, we use a multi-step technique. First, we apply classification using an unsupervised and self-adapting vector quantization algorithm. Then, we sort the resultant classes by their average intensities. We apply thresholding on these classes based on a conservative threshold. This helps us in differentiating soft tissue inside tagged material from poorly tagged region or noise.

Paper Details

Date Published: 2 May 2003
PDF: 6 pages
Proc. SPIE 5031, Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications, (2 May 2003); doi: 10.1117/12.480410
Show Author Affiliations
Sarang Lakare, Stony Brook Univ. (United States)
Dongqing Chen, Stony Brook Univ. (United States)
Lihong Li, Stony Brook Univ. (United States)
Arie E. Kaufman, Stony Brook Univ. (United States)
Mark R. Wax, Stony Brook Univ. (United States)
Zhengrong Liang, Stony Brook Univ. (United States)


Published in SPIE Proceedings Vol. 5031:
Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications
Anne V. Clough; Amir A. Amini, Editor(s)

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