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A general framework for wireless capsule endoscopy study synopsis.

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Title: A general framework for wireless capsule endoscopy study synopsis.
Authors: Zhao, Qian1,2 kate.qzhao@gmail.com, Mullin, Gerard E.2, Meng, Max Q.-H.1, Dassopoulos, Themistocles3, Kumar, Rajesh2
Source: Computerized Medical Imaging & Graphics. Apr2015, Vol. 41, p108-116. 9p.
Subjects: Capsule endoscopy, Computer diagnostic software, Hidden Markov models, Support vector machines, Information theory
Abstract: We present a general framework for analysis of wireless capsule endoscopy (CE) studies. The current available workstations provide a time-consuming and labor-intense work-flow for clinicians which requires the inspection of the full-length video. The development of a computer-aided diagnosis (CAD) CE workstation will have a great potential to reduce the diagnostic time and improve the accuracy of assessment. We propose a general framework based on hidden Markov models (HMMs) for study synopsis that forms the computational engine of our CAD workstation. Color, edge and texture features are first extracted and analyzed by a Support Vector Machine classifier, and then encoded as the observations for the HMM, uniquely combining the temporal information during the assessment. Experiments were performed on 13 full-length CE studies, instead of selected images previously reported. The results (e.g. 0.933 accuracy with 0.933 recall for detection of polyps) show that our framework achieved promising performance for multiple classification. We also report the patient-level CAD assessment of complete CE studies for multiple abnormalities, and the patient-level validation demonstrates the effectiveness and robustness of our methods. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:We present a general framework for analysis of wireless capsule endoscopy (CE) studies. The current available workstations provide a time-consuming and labor-intense work-flow for clinicians which requires the inspection of the full-length video. The development of a computer-aided diagnosis (CAD) CE workstation will have a great potential to reduce the diagnostic time and improve the accuracy of assessment. We propose a general framework based on hidden Markov models (HMMs) for study synopsis that forms the computational engine of our CAD workstation. Color, edge and texture features are first extracted and analyzed by a Support Vector Machine classifier, and then encoded as the observations for the HMM, uniquely combining the temporal information during the assessment. Experiments were performed on 13 full-length CE studies, instead of selected images previously reported. The results (e.g. 0.933 accuracy with 0.933 recall for detection of polyps) show that our framework achieved promising performance for multiple classification. We also report the patient-level CAD assessment of complete CE studies for multiple abnormalities, and the patient-level validation demonstrates the effectiveness and robustness of our methods. [ABSTRACT FROM AUTHOR]
ISSN:08956111
DOI:10.1016/j.compmedimag.2014.05.011