Statistical shape modeling of unfolded retinotopic maps for a visual areas probabilistic atlas

Isabelle Corouge 1 Christian Barillot 1 Michel Dojat 2
1 VISTA - Vision spatio-temporelle et active
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 Neuroimagerie Fonctionnelle et Metabolique
Abstract : This paper proposes a statistical model of functional landmarks delimiting low level visual areas which are highly variable across individuals. Low level visual areas are first precisely delineated by fMRI retinotopic mapping which provides detailed information about the correspondence between the visual field and its cortical representation. The model is then built by learning the variability within a given training set. It relies on an appropriate data representation and on the definition of an intrinsic coordinate system to a visual map enabling to build a consistent training set on which a principal components analysis (PCA) is eventually applied. Our approach constitutes a first step toward a functional landmark-based probabilistic atlas of low level visual areas.
keyword : PCA neuroimaging probabilistic atlas visual areas retinotopy statistical modeling PCA.
Type de document :
Communication dans un congrès
Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2003, Canada. pp.705-713, 2003, Lecture Notes in Computer Science. 〈10.1007/978-3-540-39899-8_86〉
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http://www.hal.inserm.fr/inserm-00772625
Contributeur : Isabelle Corouge <>
Soumis le : jeudi 10 janvier 2013 - 18:46:38
Dernière modification le : mercredi 16 mai 2018 - 11:23:06

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Isabelle Corouge, Christian Barillot, Michel Dojat. Statistical shape modeling of unfolded retinotopic maps for a visual areas probabilistic atlas. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2003, Canada. pp.705-713, 2003, Lecture Notes in Computer Science. 〈10.1007/978-3-540-39899-8_86〉. 〈inserm-00772625〉

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