Multiple-organ segmentation by graph cuts with supervoxel nodes

Toshiya Takaoka, Yoshihiko Mochizuki, Hiroshi Ishikawa

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

Abstract

Improvement in medical imaging technologies has made it possible for doctors to directly look into patients' bodies in ever finer details. However, since only the cross-sectional image can be directly seen, it is essential to segment the volume into organs so that their shape can be seen as 3D graphics of the organ boundary surfaces. Segmentation is also important for quantitative measurement for diagnosis. Here, we introduce a novel higher-precision method to segment multiple organs using graph cuts within medical images such as CT-scanned images. We utilize super voxels instead of voxels as the units of segmentation, i.e., the nodes in the graphical model, and design the energy function to minimize accordingly. We utilize SLIC super voxel algorithm and verify the performance of our segmentation algorithm by energy minimization comparing to the ground truth.

Original languageEnglish
Title of host publicationProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages424-427
Number of pages4
ISBN (Electronic)9784901122160
DOIs
Publication statusPublished - 2017 Jul 19
Event15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan
Duration: 2017 May 82017 May 12

Other

Other15th IAPR International Conference on Machine Vision Applications, MVA 2017
CountryJapan
CityNagoya
Period17/5/817/5/12

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Takaoka, T., Mochizuki, Y., & Ishikawa, H. (2017). Multiple-organ segmentation by graph cuts with supervoxel nodes. In Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017 (pp. 424-427). [7986891] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/MVA.2017.7986891