%0 Conference Proceedings
%T Supervised Brain Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder
%A Laura Igual
%A Joan Carles Soliva
%A Antonio Hernandez
%A Sergio Escalera
%A Oscar Vilarroya
%A Petia Radeva
%B High Performance Computing and Simulation, International Conference on
%D 2012
%I IEEE Xplore
%@ 978-1-4673-2359-8
%F Laura Igual2012
%O MILAB;HuPBA
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2038), last updated on Tue, 18 Oct 2016 13:19:36 +0200
%X This paper presents an automatic method for external and internal segmentation of the caudate nucleus in Magnetic Resonance Images (MRI) based on statistical and structural machine learning approaches. This method is applied in Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis. The external segmentation method adapts the Graph Cut energy-minimization model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus. In particular, new energy function data and boundary potentials are defined and a supervised energy term based on contextual brain structures is added. Furthermore, the internal segmentation method learns a classifier based on shape features of the Region of Interest (ROI) in MRI slices. The results show accurate external and internal caudate segmentation in a real data set and similar performance of ADHD diagnostic test to manual annotation.
%U http://refbase.cvc.uab.es/files/ISH2012a.pdf
%U http://dx.doi.org/10.1109/HPCSim.2012.6266909
%P 182-187