NDLI: Tumor detecting in colonoscopic narrow-band imaging data

Content Provider	IEEE Xplore Digital Library
Author	Hao Chun Wang Wei Ming Chen Yen Pin Lin Wei Chih Shen
Copyright Year	2012
Description	Author affiliation: Asia University, Taichung, Taiwan (Wei Chih Shen) \|\| National ILan University, ILan, Taiwan (Wei Ming Chen; Yen Pin Lin) \|\| National Taipei University of Technology, Taipei, Taiwan (Hao Chun Wang)
Abstract	In recent years, colorectal cancer is the most common type of cancer worldwide. However, this disease can be prevented by detection and removal of precursor adenomatous polyps during an optical colonoscopy course. In an optical colonoscopy course, the endoscopist looks for colon polyps. Hyperplastic polyp is benign lesion; adenomatous polyp is likely to become cancerous. Hence, it is common practice to remove all of the identified polyps and send them to subsequent histological analysis. But, removing hyperplastic polyps will pose unnecessary risk and incur unnecessary costs for histological analysis. In this paper, we pose a novel optical biopsy application based on narrow band imaging (NBI) in the first part. A common barrier to an automatic system is that require manual segmentations of the polyps in NBI data, therefore, we automatically segment polyps in NBI data. We propose an algorithm, Classification of Regional Feature (CoRF), that is an extension of the sparse matrix and vector quantization algorithms, a state of the art algorithm for feature detection and segmentation. CoRF solves the intrinsic block selection problem of vector quantization by including training codebook about the shape of the regional feature. CoRF outperforms previous methods with a better polyp region segmentation — traditional clustering algorithm, LBG or Kmean clustering algorithms will disperse the energy to other similar block regions, reducing accuracy of analysis in polyp detection.
Starting Page	564
Ending Page	568
File Size	1401330
Page Count	5
File Format	PDF
ISBN	9781467350839
e-ISBN	9781467350822
e-ISBN	9781467350815
DOI	10.1109/ISPACS.2012.6473553
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-04
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sparse matrices Image segmentation Biomedical optical imaging Optical imaging Algorithm design and analysis Clustering algorithms Vectory Quantization polyps detection polyps segmentation Sparse Matrix
Content Type	Text
Resource Type	Article

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