Learning disease severity for capsule endoscopy images

R. Kumar; P. Rajan; S. Bejakovic; S. Seshamani; G. Mullin; T. Dassopoulos; G. Hager

doi:10.1109/ISBI.2009.5193306

Learning disease severity for capsule endoscopy images

R. Kumar, P. Rajan, S. Bejakovic, S. Seshamani, G. Mullin, T. Dassopoulos, G. Hager

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Scopus citations

Abstract

Wireless capsule endoscopy (CE) is increasing being used to assess several gastrointestinal(GI) diseases and disorders. Current clinical methods are based on subjective evaluation of images. In this paper, we develop a method for ranking lesions appearing in CE images. This ranking is based on pairwise comparisons among representative images supplied by an expert. With such sparse pairwise rank information for a small number of images, we investigate methods for creating and evaluating global ranking functions. In experiments with CE images, we train statistical classifiers using color and edge feature descriptors extracted frommanually annotated regions of interest. Experiments on a data set using Crohn's disease lesions for lesion severity are presented with the developed ranking functions achieve high accuracy rates.

Original language	English (US)
Title of host publication	Proceedings - 2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI 2009
Pages	1314-1317
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2009.5193306
State	Published - 2009
Event	2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States Duration: Jun 28 2009 → Jul 1 2009

Publication series

Name	Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Other

Other	2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
Country/Territory	United States
City	Boston, MA
Period	6/28/09 → 7/1/09

Keywords

Capsule endoscopy
Disease severity
Ordinal regression
Statistical classification

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2009.5193306

Cite this

Kumar, R., Rajan, P., Bejakovic, S., Seshamani, S., Mullin, G., Dassopoulos, T., & Hager, G. (2009). Learning disease severity for capsule endoscopy images. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 (pp. 1314-1317). Article 5193306 (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009). https://doi.org/10.1109/ISBI.2009.5193306

Learning disease severity for capsule endoscopy images. / Kumar, R.; Rajan, P.; Bejakovic, S. et al.
Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. p. 1314-1317 5193306 (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kumar, R, Rajan, P, Bejakovic, S, Seshamani, S, Mullin, G, Dassopoulos, T & Hager, G 2009, Learning disease severity for capsule endoscopy images. in Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009., 5193306, Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 1314-1317, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, Boston, MA, United States, 6/28/09. https://doi.org/10.1109/ISBI.2009.5193306

Kumar R, Rajan P, Bejakovic S, Seshamani S, Mullin G, Dassopoulos T et al. Learning disease severity for capsule endoscopy images. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. p. 1314-1317. 5193306. (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009). doi: 10.1109/ISBI.2009.5193306

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abstract = "Wireless capsule endoscopy (CE) is increasing being used to assess several gastrointestinal(GI) diseases and disorders. Current clinical methods are based on subjective evaluation of images. In this paper, we develop a method for ranking lesions appearing in CE images. This ranking is based on pairwise comparisons among representative images supplied by an expert. With such sparse pairwise rank information for a small number of images, we investigate methods for creating and evaluating global ranking functions. In experiments with CE images, we train statistical classifiers using color and edge feature descriptors extracted frommanually annotated regions of interest. Experiments on a data set using Crohn's disease lesions for lesion severity are presented with the developed ranking functions achieve high accuracy rates.",

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AB - Wireless capsule endoscopy (CE) is increasing being used to assess several gastrointestinal(GI) diseases and disorders. Current clinical methods are based on subjective evaluation of images. In this paper, we develop a method for ranking lesions appearing in CE images. This ranking is based on pairwise comparisons among representative images supplied by an expert. With such sparse pairwise rank information for a small number of images, we investigate methods for creating and evaluating global ranking functions. In experiments with CE images, we train statistical classifiers using color and edge feature descriptors extracted frommanually annotated regions of interest. Experiments on a data set using Crohn's disease lesions for lesion severity are presented with the developed ranking functions achieve high accuracy rates.

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Learning disease severity for capsule endoscopy images

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Keywords

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