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Automatic Colorectal Polyp Detection in Colonoscopy Video Frames

Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. His...

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Detalles Bibliográficos
Autores principales: K, Geetha, C, Rajan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: West Asia Organization for Cancer Prevention 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5454689/
https://www.ncbi.nlm.nih.gov/pubmed/28030914
http://dx.doi.org/10.22034/APJCP.2016.17.11.4869
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author K, Geetha
C, Rajan
author_facet K, Geetha
C, Rajan
author_sort K, Geetha
collection PubMed
description Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods.
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spelling pubmed-54546892017-08-28 Automatic Colorectal Polyp Detection in Colonoscopy Video Frames K, Geetha C, Rajan Asian Pac J Cancer Prev Research Article Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods. West Asia Organization for Cancer Prevention 2016 /pmc/articles/PMC5454689/ /pubmed/28030914 http://dx.doi.org/10.22034/APJCP.2016.17.11.4869 Text en Copyright: © Asian Pacific Journal of Cancer Prevention http://creativecommons.org/licenses/BY-SA/4.0 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
spellingShingle Research Article
K, Geetha
C, Rajan
Automatic Colorectal Polyp Detection in Colonoscopy Video Frames
title Automatic Colorectal Polyp Detection in Colonoscopy Video Frames
title_full Automatic Colorectal Polyp Detection in Colonoscopy Video Frames
title_fullStr Automatic Colorectal Polyp Detection in Colonoscopy Video Frames
title_full_unstemmed Automatic Colorectal Polyp Detection in Colonoscopy Video Frames
title_short Automatic Colorectal Polyp Detection in Colonoscopy Video Frames
title_sort automatic colorectal polyp detection in colonoscopy video frames
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5454689/
https://www.ncbi.nlm.nih.gov/pubmed/28030914
http://dx.doi.org/10.22034/APJCP.2016.17.11.4869
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