Article

Journal of Mathematical Imaging and Vision

, Volume 5, Issue 4, pp 297-318

From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations

  • Jean-François ManginAffiliated withService Hospitalier Frédéric Joliot, Commissariat à l'Énergie AtomiqueDépartement Images, École Nationale Supérieure des Télécommunications
  • , Vincent FrouinAffiliated withService Hospitalier Frédéric Joliot, Commissariat à l'Énergie Atomique
  • , Isabelle BlochAffiliated withDépartement Images, École Nationale Supérieure des Télécommunications
  • , Jean RégisAffiliated withService de Neurochirurgie Fonctionnelle et Stéréotaxique, C.H.U. La Timone
  • , Jaime López-KraheAffiliated withDépartement Images, École Nationale Supérieure des Télécommunications

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Abstract

We propose an algorithm allowing the construction of a structural representation of the cortical topography from a T1-weighted 3D MR image. This representation is an attributed relational graph (ARG) inferred from the 3D skeleton of the object made up of the union of gray matter and cerebro-spinal fluid enclosed in the brain hull. In order to increase the robustness of the skeletonization, topological and regularization constraints are included in the segmentation process using an original method: the homotopically deformable regions. This method is halfway between deformable contour and Markovian segmentation approaches. The 3D skeleton is segmented in simple surfaces (SSs) constituting the ARG nodes (mainly cortical folds). The ARG relations are of two types: first, theSS pairs connected in the skeleton; second, theSS pairs delimiting a gyrus. The described algorithm has been developed in the frame of a project aiming at the automatic detection and recognition of the main cortical sulci. Indeed, the ARG is a synthetic representation of all the information required by the sulcus identification. This project will contribute to the development of new methodologies for human brain functional mapping and neurosurgery operation planning.

Keywords

medical imaging mathematical morphology deformable contour Markovian random fields topology preserving deformation structural pattern recognition functional brain mapping