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Proceedings Paper

GISentinel: a software platform for automatic ulcer detection on capsule endoscopy videos
Author(s): Steven Yi; Heng Jiao; Fan Meng; Jonathon A. Leighton; Pasha Shabana; Lauri Rentz
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Paper Abstract

In this paper, we present a novel and clinically valuable software platform for automatic ulcer detection on gastrointestinal (GI) tract from Capsule Endoscopy (CE) videos. Typical CE videos take about 8 hours. They have to be reviewed manually by physicians to detect and locate diseases such as ulcers and bleedings. The process is time consuming. Moreover, because of the long-time manual review, it is easy to lead to miss-finding. Working with our collaborators, we were focusing on developing a software platform called GISentinel, which can fully automated GI tract ulcer detection and classification. This software includes 3 parts: the frequency based Log-Gabor filter regions of interest (ROI) extraction, the unique feature selection and validation method (e.g. illumination invariant feature, color independent features, and symmetrical texture features), and the cascade SVM classification for handling "ulcer vs. non-ulcer" cases. After the experiments, this SW gave descent results. In frame-wise, the ulcer detection rate is 69.65% (319/458). In instance-wise, the ulcer detection rate is 82.35%(28/34).The false alarm rate is 16.43% (34/207). This work is a part of our innovative 2D/3D based GI tract disease detection software platform. The final goal of this SW is to find and classification of major GI tract diseases intelligently, such as bleeding, ulcer, and polyp from the CE videos. This paper will mainly describe the automatic ulcer detection functional module.

Paper Details

Date Published: 24 March 2014
PDF: 6 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352Y (24 March 2014); doi: 10.1117/12.2042369
Show Author Affiliations
Steven Yi, Xyken, LLC (United States)
Heng Jiao, Xyken, LLC (United States)
Fan Meng, Xyken, LLC (United States)
Jonathon A. Leighton, Mayo Clinic (United States)
Pasha Shabana, Mayo Clinic (United States)
Lauri Rentz, Mayo Clinic (United States)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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