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.
|Original language||English (US)|
|Number of pages||8|
|Journal||Lecture Notes in Computer Science|
|Issue number||PART 1|
|State||Published - 2004|
|Event||Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings - Saint-Malo, France|
Duration: Sep 26 2004 → Sep 29 2004