Image segmentation using local probabilistic atlases coupled with topological information
Résumé
Atlas-based segmentation is a widely used method for Magnetic Resonance Imaging (MRI) segmentation. It
is also a very efficient method for the automatic segmentation of brain structures. In this paper, we propose
a more adaptive and interactive atlas-based method. The proposed model allows to combine several local
probabilistic atlases with a topological graph. Local atlases can provide more precise information about the
structure’s shape and the spatial relationships between each of these atlases are learned and stored inside a
graph representation. In this way, local registrations need less computational time and image segmentation
can be guided by the user in an incremental way. Pixel classification is achieved with the help of a hidden
Markov random field that is able to integrate the a priori information with the intensities coming from different
modalities. The proposed method was tested on the OASIS dataset, used in the MICCAI’12 challenge for
multi-atlas labeling.