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.
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© 1995 Springer-Verlag Berlin Heidelberg
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Mangin, JF., Regis, J., Bloch, I., Frouin, V., Samson, Y., Lopez-Krahe, J. (1995). A MRF Based Random Graph Modelling the Human Cortical Topography. In: Ayache, N. (eds) Computer Vision, Virtual Reality and Robotics in Medicine. CVRMed 1995. Lecture Notes in Computer Science, vol 905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49197-2_20
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DOI: https://doi.org/10.1007/978-3-540-49197-2_20
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