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Unsupervised segmentation of colonic polyps in narrow-band imaging data based on manifold representation of images and Wasserstein distance
Univ Coimbra, CMUC, Dept Math, Fac Sci & Technol, Coimbra, Portugal..
Univ Coimbra, CMUC, Dept Math, Fac Sci & Technol, Coimbra, Portugal..ORCID iD: 0000-0003-1121-1738
Univ Coimbra, Fac Med, Coimbra, Portugal.;CHUC, Dept Gastroenterol, Coimbra, Portugal.;Ctr Cirurg Coimbra, Coimbra, Portugal..
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Numerical Analysis, NA. Univ Texas Austin, Dept Math, Austin, TX 78712 USA.;Univ Texas Austin, Oden Inst Computat Engn & Sci, Austin, TX 78712 USA..ORCID iD: 0000-0001-8441-3678
2019 (English)In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 53, article id UNSP 101577Article in journal (Refereed) Published
Abstract [en]

Colorectal cancer (CRC) is one of the most common cancers worldwide and after a certain age (>= 50) regular colonoscopy examination for CRC screening is highly recommended. One of the most prominent precursors of CRC are abnormal growths known as polyps. If a polyp is detected during colonoscopy examination the endoscopist needs to decide whether the polyp should be discarded, removed, or biopsied for further examination. However, the last two options involve some risks for the patient, while not all the polyps are precancerous. On the other hand, discarding a polyp has the risk of failing to detect CRC. We propose an automatic and unsupervised method for the segmentation of colonic polyps for in vivo Narrow-Band-Imaging (NBI) data. Polyp segmentation is a crucial step towards an automatic real-time polyp classification system, that could help the endoscopist in the diagnosis of CRC. The proposed method is a histogram based two-phase segmentation model, involving the Wasserstein distance. These histograms incorporate fused information about suitable image descriptors, namely semi-local texture, geometry and color. To test the proposed segmentation methodology we use a dataset consisting of 86 NBI polyp frames: the 83% sensitivity, 95% specificity, and 93% accuracy suggest a better performance compared to the results obtained with other methods.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD , 2019. Vol. 53, article id UNSP 101577
Keywords [en]
NBI, Polyp, Segmentation, Texture, Wasserstein distance
National Category
Computational Mathematics Biological Sciences
Identifiers
URN: urn:nbn:se:kth:diva-261338DOI: 10.1016/j.bspc.2019.101577ISI: 000485334600027OAI: oai:DiVA.org:kth-261338DiVA, id: diva2:1358195
Note

QC 20191007

Available from: 2019-10-07 Created: 2019-10-07 Last updated: 2019-10-07Bibliographically approved

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Tsai, Yen-Hsi Richard

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