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Visual Phrase Learning and Its Application in Computed Tomographic Colonography
Shijun Wang, Matthew McKenna, Zhuoshi Wei, Jiamin Liu, Peter Liu, and Ronald M. Summers
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bldg 10, Room 1C224, Bethesda, MD 20892-1182, USArms@nih.gov
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.
LNCS 8149, p. 243 ff.
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© Springer-Verlag Berlin Heidelberg 2013