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Automatic construction of an attributed relational graph representing the cortex topography using homotopic transformations
Author(s): Jean-Francois Mangin; Vincent Frouin; Isabelle Bloch; Jean Regis; Jaime Lopez-Krahe
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Paper Abstract

We propose an algorithm allowing the construction of a high level 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 sulcus parts). The ARG relations are of two types: first, the SSs pairs connected in the skeleton; second, the SSs 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 the human brain functional mapping.

Paper Details

Date Published: 8 July 1994
PDF: 12 pages
Proc. SPIE 2299, Mathematical Methods in Medical Imaging III, (8 July 1994); doi: 10.1117/12.179275
Show Author Affiliations
Jean-Francois Mangin, Telecom Paris and CEA Service Hospitalier Frederic Joliot (France)
Vincent Frouin, CEA Service Hospitalier Frederic Joliot (France)
Isabelle Bloch, Telecom Paris (France)
Jean Regis, CEA Service de Neurologie Fonctionnelle et Stereotaxique (France)
Jaime Lopez-Krahe, Telecom Paris (France)

Published in SPIE Proceedings Vol. 2299:
Mathematical Methods in Medical Imaging III
Fred L. Bookstein; James S. Duncan; Nicholas Lange; David C. Wilson, Editor(s)

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