TY - JOUR
T1 - Segmentation of 3D probability density fields by surface evolution
T2 - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings
AU - Lenglet, Christophe
AU - Rousson, Mikaël
AU - Deriche, Rachid
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2004
Y1 - 2004
N2 - We propose an original approach for the segmentation of three-dimensional fields of probability density functions. This presents a wide range of applications in medical images processing, in particular for diffusion magnetic resonance imaging where each voxel is assigned with a function describing the average motion of water molecules. Being able to automatically extract relevant anatomical structures of the white matter, such as the corpus callosum, would dramatically improve our current knowledge of the cerebral connectivity as well as allow for their statistical analysis. Our approach relies on the use of the symmetrized Kullback-Leibler distance and on the modelization of its distribution over the subsets of interest in the volume. The variational formulation of the problem yields a level-set evolution converging toward the optimal segmentation.
AB - We propose an original approach for the segmentation of three-dimensional fields of probability density functions. This presents a wide range of applications in medical images processing, in particular for diffusion magnetic resonance imaging where each voxel is assigned with a function describing the average motion of water molecules. Being able to automatically extract relevant anatomical structures of the white matter, such as the corpus callosum, would dramatically improve our current knowledge of the cerebral connectivity as well as allow for their statistical analysis. Our approach relies on the use of the symmetrized Kullback-Leibler distance and on the modelization of its distribution over the subsets of interest in the volume. The variational formulation of the problem yields a level-set evolution converging toward the optimal segmentation.
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U2 - 10.1007/978-3-540-30135-6_3
DO - 10.1007/978-3-540-30135-6_3
M3 - Conference article
AN - SCOPUS:20344365068
SN - 0302-9743
VL - 3216
SP - 18
EP - 25
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
IS - PART 1
Y2 - 26 September 2004 through 29 September 2004
ER -