TY - GEN
T1 - Adaptive-focus statistical shape model for segmentation of 3D MR structures
AU - Shen, Dinggang
AU - Davatzikos, Christos
PY - 2000/1/1
Y1 - 2000/1/1
N2 - This paper presents a deformable model for automatically segmenting objects from volumetric MR images and obtaining point correspondences, using geometric and statistical information in a hierarchical scheme. Geometric information is embedded into the model via an affine-invariant attribute vector, which characterizes the geometric structure around each model point from a local to a global level. Accordingly, the model deforms seeking boundary points with similar attribute vectors. This is in contrast to most deformable surface models, which adapt to nearby edges without considering the geometric structure. The proposed model is adaptive in that it initially focuses on the most reliable structures of interest, and subsequently switches focus to other structures as those become closer to their respective targets and therefore more reliable. The proposed techniques have been used to segment boundaries of the ventricles, the caudate nucleus, and the lenticular nucleus from volumetric MR images.
AB - This paper presents a deformable model for automatically segmenting objects from volumetric MR images and obtaining point correspondences, using geometric and statistical information in a hierarchical scheme. Geometric information is embedded into the model via an affine-invariant attribute vector, which characterizes the geometric structure around each model point from a local to a global level. Accordingly, the model deforms seeking boundary points with similar attribute vectors. This is in contrast to most deformable surface models, which adapt to nearby edges without considering the geometric structure. The proposed model is adaptive in that it initially focuses on the most reliable structures of interest, and subsequently switches focus to other structures as those become closer to their respective targets and therefore more reliable. The proposed techniques have been used to segment boundaries of the ventricles, the caudate nucleus, and the lenticular nucleus from volumetric MR images.
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M3 - Conference contribution
AN - SCOPUS:84945535942
SN - 3540411895
SN - 9783540411895
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 206
EP - 215
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2000 - 3rd International Conference, Proceedings
A2 - DiGoia, Anthony M.
A2 - Jaramaz, Branislav
A2 - Delp, Scott L.
PB - Springer Verlag
T2 - 3rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2000
Y2 - 11 October 2000 through 14 October 2000
ER -