Title :
Tumor detecting in colonoscopic narrow-band imaging data
Author :
Hao Chun Wang ; Wei Ming Chen ; Yen Pin Lin ; Wei Chih Shen
Author_Institution :
Nat. Taipei Univ. of Technol., Taipei, Taiwan
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.
Keywords :
biomedical optical imaging; cancer; endoscopes; feature extraction; image classification; image segmentation; medical image processing; sparse matrices; tumours; vector quantisation; Kmean clustering algorithms; benign lesion; colon polyps; colonoscopic narrow-band imaging data; colorectal cancer; endoscopist; feature detection; feature segmentation; histological analysis; hyperplastic polyp; intrinsic block selection problem; manual segmentations; narrow band imaging; optical biopsy application; optical colonoscopy; polyp region segmentation-traditional clustering algorithm; precursor adenomatous polyp detection; precursor adenomatous polyp removal; regional feature classification; sparse matrix; training codebook; tumor detection; vector quantization algorithms; Algorithm design and analysis; Biomedical optical imaging; Clustering algorithms; Image segmentation; Optical imaging; Sparse matrices; Sparse Matrix; Vectory Quantization; polyps detection; polyps segmentation;
Conference_Titel :
Intelligent Signal Processing and Communications Systems (ISPACS), 2012 International Symposium on
Conference_Location :
New Taipei
Print_ISBN :
978-1-4673-5083-9
Electronic_ISBN :
978-1-4673-5081-5
DOI :
10.1109/ISPACS.2012.6473553