A general framework for wireless capsule endoscopy study synopsis

Qian Zhao, Gerard E. Mullin, Max Q.H. Meng, Themistocles Dassopoulos, Rajesh Kumar

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)108-116
Number of pages9
JournalComputerized Medical Imaging and Graphics
Volume41
DOIs
StatePublished - Apr 1 2015

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Keywords

  • Computer-aided diagnosis
  • Hidden Markov models
  • Study synopsis
  • Supervised classification
  • Support vector machines
  • Wireless capsule endoscopy

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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