%0 Journal Article
%T Automatic Brain Caudate Nuclei Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder
%A Laura Igual
%A Joan Carles Soliva
%A Sergio Escalera
%A Roger Gimeno
%A Oscar Vilarroya
%A Petia Radeva
%J Computerized Medical Imaging and Graphics
%D 2012
%V 36
%N 8
%F Laura Igual2012
%O OR; HuPBA; MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2143), last updated on Fri, 19 Sep 2014 10:26:55 +0200
%X We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods.
%K Automatic caudate segmentation
%K Attention-Deficit/Hyperactivity Disorder
%K Diagnostic test
%K Machine learning
%K Decision stumps
%K Dissociated dipoles
%U http://dx.doi.org/10.1016/j.compmedimag.2012.08.002
%U http://refbase.cvc.uab.es/files/ISE2012.pdf
%P 591-600