PT Unknown
AU Laura Igual
   Joan Carles Soliva
   Antonio Hernandez
   Sergio Escalera
   Oscar Vilarroya
   Petia Radeva
TI Supervised Brain Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder
BT High Performance Computing and Simulation, International Conference on
PY 2012
BP 182
EP 187
DI 10.1109/HPCSim.2012.6266909
AB 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.
ER