A clinically viable capsule endoscopy video analysis platform for automatic bleeding detection

Steven Yi; Heng Jiao; Jean Xie; Peter Mui; Jonathan A. Leighton; Shabana Pasha; Lauri Rentz; Mahmood Abedi

doi:10.1117/12.2001881

28 February 2013 A clinically viable capsule endoscopy video analysis platform for automatic bleeding detection

Steven Yi, Heng Jiao, Jean Xie, Peter Mui, Jonathan A. Leighton, Shabana Pasha, Lauri Rentz, Mahmood Abedi

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867028 (2013) https://doi.org/10.1117/12.2001881
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

In this paper, we present a novel and clinically valuable software platform for automatic bleeding detection on gastrointestinal (GI) tract from Capsule Endoscopy (CE) videos. Typical CE videos for GI tract run about 8 hours and are manually reviewed by physicians to locate diseases such as bleedings and polyps. As a result, the process is time consuming and is prone to disease miss-finding. While researchers have made efforts to automate this process, however, no clinically acceptable software is available on the marketplace today. Working with our collaborators, we have developed a clinically viable software platform called GISentinel for fully automated GI tract bleeding detection and classification. Major functional modules of the SW include: the innovative graph based NCut segmentation algorithm, the unique feature selection and validation method (e.g. illumination invariant features, color independent features, and symmetrical texture features), and the cascade SVM classification for handling various GI tract scenes (e.g. normal tissue, food particles, bubbles, fluid, and specular reflection). Initial evaluation results on the SW have shown zero bleeding instance miss-finding rate and 4.03% false alarm rate. This work is part of our innovative 2D/3D based GI tract disease detection software platform. While the overall SW framework is designed for intelligent finding and classification of major GI tract diseases such as bleeding, ulcer, and polyp from the CE videos, this paper will focus on the automatic bleeding detection functional module.

Citation Download Citation

Steven Yi, Heng Jiao, Jean Xie, Peter Mui, Jonathan A. Leighton, Shabana Pasha, Lauri Rentz, and Mahmood Abedi "A clinically viable capsule endoscopy video analysis platform for automatic bleeding detection", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867028 (28 February 2013); https://doi.org/10.1117/12.2001881

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