title: Automatic brain caudate nuclei segmentation and classification in diagnostic of Attention-Deficit/Hyperactivity Disorder.
abstract: 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.
authors.affiliation: Dept. Applied Mathematics and Analysis, Universitat de Barcelona, Gran Via Corts Catalanes 585, 08007 Barcelona, Spain. ligual@ub.edu
authors.forename: Laura
authors.initials: L
authors.lastname: Igual
authors.forename: Joan Carles
authors.initials: JC
authors.lastname: Soliva
authors.forename: Sergio
authors.initials: S
authors.lastname: Escalera
authors.forename: Roger
authors.initials: R
authors.lastname: Gimeno
authors.forename: Oscar
authors.initials: O
authors.lastname: Vilarroya
authors.forename: Petia
authors.initials: P
authors.lastname: Radeva
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001289:Attention Deficit Disorder with Hyperactivity / Q000473:pathology*; D002421:Caudate Nucleus / Q000473:pathology*; D002648:Child; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity