TY - CONF AU - Laura Igual AU - Joan Carles Soliva AU - Antonio Hernandez AU - Sergio Escalera AU - Oscar Vilarroya AU - Petia Radeva A2 - HPCS PY - 2012// TI - Supervised Brain Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder BT - High Performance Computing and Simulation, International Conference on SP - 182 EP - 187 PB - IEEE Xplore N2 - 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. SN - 978-1-4673-2359-8 L1 - http://refbase.cvc.uab.es/files/ISH2012a.pdf UR - http://dx.doi.org/10.1109/HPCSim.2012.6266909 N1 - MILAB;HuPBA ID - Laura Igual2012 ER -