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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013

Volume 8149 of the series Lecture Notes in Computer Science pp 243-250

Visual Phrase Learning and Its Application in Computed Tomographic Colonography

  • Shijun WangAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health
  • , Matthew McKennaAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health
  • , Zhuoshi WeiAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health
  • , Jiamin LiuAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health
  • , Peter LiuAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health
  • , Ronald M. SummersAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health

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Abstract

In this work, we propose a visual phrase learning scheme to learn an optimal visual composite of anatomical components/parts from CT colonography images for computer-aided detection. The key idea is to utilize the anatomical parts of human body from medical images and associate them with biological targets of interest (organs, cancers, lesions, etc.) for joint detection and recognition. These anatomical parts of the human body are not necessarily near each other regarding their physical locations, and they serve more like a human body navigation system for detection and recognition. To show the effectiveness of the proposed learning scheme, we applied it to two sub-problems in computed tomographic colonography: teniae detection and classification of colorectal polyp candidates. Experimental results showed its efficacy.