Multiple-organ segmentation based on spatially-divided neighboring data energy

Minato Morita, Asuka Okagawa, Yuji Oyamada, Yoshihiko Mochizuki, Hiroshi Ishikawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Medical image segmentation, e.g., Computed Tomography (CT) volume segmentation, is necessary for further medical image analysis and computer aided intervention. In the standard energy minimization scheme for medical image segmentation, three terms exist in the energy: the data term, the Potts smoothing term, and the probabilistic atlas term. In this paper, we propose a novel potential function that extends the data term. The discriminability of the existing data term, which fully depends on how distinctive the objects of interest appear on CT volume, has problem when some of the objects have similar or same CT values. We overcome this limitation by considering the CT values of a pair of neighboring voxels. Increasing the voxel of interest to be evaluated, the data term become more discriminable even if some objects of interest have similar CT values. We also propose to learn the probability of the neighboring data term for each sub-region, not for each voxel. The proposed neighboring data term can be regarded as to combine the standard data term and the probabilistic atlas.

Original languageEnglish
Title of host publicationProceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages158-161
Number of pages4
ISBN (Print)9784901122153
DOIs
Publication statusPublished - 2015 Jul 8
Event14th IAPR International Conference on Machine Vision Applications, MVA 2015 - Tokyo, Japan
Duration: 2015 May 182015 May 22

Other

Other14th IAPR International Conference on Machine Vision Applications, MVA 2015
CountryJapan
CityTokyo
Period15/5/1815/5/22

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Morita, M., Okagawa, A., Oyamada, Y., Mochizuki, Y., & Ishikawa, H. (2015). Multiple-organ segmentation based on spatially-divided neighboring data energy. In Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015 (pp. 158-161). [7153157] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MVA.2015.7153157