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Statistical shape modeling of low level visual area borders.

Isabelle Corouge 1 Michel Dojat 2, * Christian Barillot 1 
* Corresponding author
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : This paper proposes a statistical modeling 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 common to all visual maps. This allows to build a consistent training set on which a principal component analysis is eventually applied. Our approach constitutes a first step toward a functional landmark-based probabilistic atlas of low level visual areas.
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Isabelle Corouge, Michel Dojat, Christian Barillot. Statistical shape modeling of low level visual area borders.. Medical Image Analysis, Elsevier, 2004, 8 (3), pp.353-60. ⟨10.1016/⟩. ⟨inserm-00402298⟩



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