2 May 2003 Robust colon residue detection using vector-quantization-based classification for virtual colonoscopy
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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.
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Sarang Lakare, Sarang Lakare, Dongqing Chen, Dongqing Chen, Lihong Li, Lihong Li, Arie E. Kaufman, Arie E. Kaufman, Mark R. Wax, Mark R. Wax, Zhengrong Liang, Zhengrong Liang, } "Robust colon residue detection using vector-quantization-based classification for virtual colonoscopy", Proc. SPIE 5031, Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications, (2 May 2003); doi: 10.1117/12.480410; https://doi.org/10.1117/12.480410
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