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
N1 - Publisher Copyright:
© 2015 MVA organization.
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
T3 - Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015
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.
T2 - 14th IAPR International Conference on Machine Vision Applications, MVA 2015
Y2 - 18 May 2015 through 22 May 2015
ER -