NDLI: Segmentation of small bowel tumor tissue in capsule endoscopy images by using the MAP algorithm

Content Provider	IEEE Xplore Digital Library
Author	Vieira, P. Ramos, J. Barbosa, D. Roupar, D. Silva, C. Correia, H. Lima, C.S.
Copyright Year	2012
Description	Author affiliation: Gastroenterology Dept., Hosp. dos Capuchos, Capuchos, Portugal (Ramos, J.) \|\| Ind. Electron. Dept., Univ. of Minho, Guimaraes, Portugal (Vieira, P.; Silva, C.; Correia, H.; Lima, C.S.) \|\| Catholic Univ. of Leuven, Leuven, Belgium (Barbosa, D.; Roupar, D.)
Abstract	State of the art algorithms for diagnosis of the small bowel by using capsule endoscopic images usually rely on the processing of the whole frame, hence no segmentation is usually required. However, some specific applications such as three-dimensional reconstruction of the digestive wall, detection of small substructures such as polyps and ulcers or training of young medical staff require robust segmentation. Current state of the art algorithms for robust segmentation are mainly based on Markov Random Fields (MRF) requiring prohibitive computational resources not compatible with applications that generate a great amount of data as is the case of capsule endoscopy. However context information given by MRF is not the only way to improve robustness. Alternatives could come from a more effective use of the color information. This paper proposes a Maximum A Posteriori (MAP) based approach for lesion segmentation based on pixel intensities read simultaneously in the three color channels. Usually tumor regions are characterized by higher intensity than normal regions, where the intensity can be measured as the vectorial sum of the 3 color channels. The exception occurs when the capsule is positioned perpendicularly and too close to the small bowel wall. In this case a hipper intense tissue region appears at the middle of the image, which in case of being normal tissue, will be segmented as tumor tissue. This paper also proposes a Maximum Likelihood (ML) based approach to deal with this situation. Experimental results show that tumor segmentation becomes more effective in the HSV than in the RGB color space where diagonal covariance matrices have similar effectiveness than full covariance matrices.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	4010
Ending Page	4013
File Size	652883
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6346846
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Image color analysis Robustness Lesions Covariance matrix Clustering algorithms
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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