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 language | English |
|---|---|
| Title of host publication | Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 424-427 |
| Number of pages | 4 |
| ISBN (Electronic) | 9784901122160 |
| DOIs | |
| Publication status | Published - 2017 Jul 19 |
| Event | 15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan Duration: 2017 May 8 → 2017 May 12 |
Other
| Other | 15th IAPR International Conference on Machine Vision Applications, MVA 2017 |
|---|---|
| Country | Japan |
| City | Nagoya |
| Period | 17/5/8 → 17/5/12 |
Fingerprint
ASJC Scopus subject areas
- Computer Science Applications
- Computer Vision and Pattern Recognition
Cite this
Multiple-organ segmentation by graph cuts with supervoxel nodes. / Takaoka, Toshiya; Mochizuki, Yoshihiko; Ishikawa, Hiroshi.
Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 424-427 7986891.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multiple-organ segmentation by graph cuts with supervoxel nodes
AU - Takaoka, Toshiya
AU - Mochizuki, Yoshihiko
AU - Ishikawa, Hiroshi
PY - 2017/7/19
Y1 - 2017/7/19
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85027887026&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027887026&partnerID=8YFLogxK
U2 - 10.23919/MVA.2017.7986891
DO - 10.23919/MVA.2017.7986891
M3 - Conference contribution
AN - SCOPUS:85027887026
SP - 424
EP - 427
BT - Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -