A MRF based random graph modelling the human cortical topography

Abstract

This paper presents a project aiming at the automatic detection and recognition of the human cortical sulci in a 3D magnetic resonance image. The two first steps of this project (automatic extraction of an attributed relational graph (ARG) representing the individual cortical topography, constitution of a database of labelled ARGs) are briefly described. Then, a probabilistic structural model of the cortical topography is inferred from the database. This model, which is a structural prototype whose nodes can split into pieces according to syntactic constraints, relies on several original interpretations of the inter-individual structural variability of the cortical topography. This prototype is endowed with a random graph structure taking into account this anatomical variability. The recognition process is formalized as a labelling problem whose solution, defined as the maximum a posteriori estimate of a Markovian random field (MRF), is obtained using simulated annealing.