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

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015
出版社Institute of Electrical and Electronics Engineers Inc.
ページ158-161
ページ数4
ISBN(印刷版)9784901122153
DOI
出版ステータスPublished - 2015 7 8
イベント14th IAPR International Conference on Machine Vision Applications, MVA 2015 - Tokyo, Japan
継続期間: 2015 5 182015 5 22

Other

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

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識

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