Visual Phrase Learning and Its Application in Computed Tomographic Colonography

  • Shijun Wang
  • Matthew McKenna
  • Zhuoshi Wei
  • Jiamin Liu
  • Peter Liu
  • Ronald M. Summers
Conference paper

DOI: 10.1007/978-3-642-40811-3_31

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8149)
Cite this paper as:
Wang S., McKenna M., Wei Z., Liu J., Liu P., Summers R.M. (2013) Visual Phrase Learning and Its Application in Computed Tomographic Colonography. In: Mori K., Sakuma I., Sato Y., Barillot C., Navab N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8149. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shijun Wang
    • 1
  • Matthew McKenna
    • 1
  • Zhuoshi Wei
    • 1
  • Jiamin Liu
    • 1
  • Peter Liu
    • 1
  • Ronald M. Summers
    • 1
  1. 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical CenterNational Institutes of HealthBethesdaUSA

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