PT Journal
AU Laura Igual
   Joan Carles Soliva
   Sergio Escalera
   Roger Gimeno
   Oscar Vilarroya
   Petia Radeva
TI Automatic Brain Caudate Nuclei Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder
SO Computerized Medical Imaging and Graphics
JI CMIG
PY 2012
BP 591
EP 600
VL 36
IS 8
DI 10.1016/j.compmedimag.2012.08.002
DE Automatic caudate segmentation; Attention-Deficit/Hyperactivity Disorder; Diagnostic test; Machine learning; Decision stumps; Dissociated dipoles
AB 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.
ER