Image segmentation using local probabilistic atlases coupled with topological information

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.