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 language | English |
|---|---|
| Title of host publication | Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 158-161 |
| Number of pages | 4 |
| ISBN (Print) | 9784901122153 |
| DOIs | |
| Publication status | Published - 2015 Jul 8 |
| Event | 14th IAPR International Conference on Machine Vision Applications, MVA 2015 - Tokyo, Japan Duration: 2015 May 18 → 2015 May 22 |
Other
| Other | 14th IAPR International Conference on Machine Vision Applications, MVA 2015 |
|---|---|
| Country | Japan |
| City | Tokyo |
| Period | 15/5/18 → 15/5/22 |
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ASJC Scopus subject areas
- Computer Science Applications
- Computer Vision and Pattern Recognition
Cite this
Multiple-organ segmentation based on spatially-divided neighboring data energy. / Morita, Minato; Okagawa, Asuka; Oyamada, Yuji; Mochizuki, Yoshihiko; Ishikawa, Hiroshi.
Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 158-161 7153157.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multiple-organ segmentation based on spatially-divided neighboring data energy
AU - Morita, Minato
AU - Okagawa, Asuka
AU - Oyamada, Yuji
AU - Mochizuki, Yoshihiko
AU - Ishikawa, Hiroshi
PY - 2015/7/8
Y1 - 2015/7/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84941208334&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84941208334&partnerID=8YFLogxK
U2 - 10.1109/MVA.2015.7153157
DO - 10.1109/MVA.2015.7153157
M3 - Conference contribution
AN - SCOPUS:84941208334
SN - 9784901122153
SP - 158
EP - 161
BT - Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -