An intelligent system to detect Crohn's disease inflammation in wireless capsule endoscopy videos

Authors:
H. Z. Girgis;B. R. Mitchell;T. Dassopouios;G. Mullin;G. Haga
Affiliations:
Johns Hopkins University, Department of Computer Science, Baltimore, MD and National Institutes of Health, NLM/NCBI, Bethesda, MD;Johns Hopkins University, Department of Computer Science, Baltimore, MD;Washington University, School of Medicine, St. Louis, MO;Johns Hopkins Hospital, Baltimore, MD;Johns Hopkins University, Department of Computer Science, Baltimore, MD
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

A Wireless Capsule Endoscope (WCE) is a small device that is capable of acquiring thousands of images as it travels through the gastrointestinal track. WCE is becoming a widely accepted method which physicians use in the diagnosis of Crohn's disease, an inflammatory disease that occurs mainly in the small intestine. In this article we present a novel method to detect those images showing inflammation among the thousands of images acquired by the WCE. Further, our method is capable of delineating the inflammation region(s) in each detected frame. Our system utilizes the mean-shift algorithm to find centers of candidate regions that may show Crohn's disease inflammation. Then the system classifies these regions by a trained Support Vector Machine. We have trained, validated and tested our method on three mutually exclusive sets. Our system's testing accuracy, specificity and sensitivity are 87%, 93% and 80% respectively.