Automatic brain caudate nuclei segmentation and classification in diagnostic of Attention-Deficit/Hyperactivity Disorder

Laura Igual, Joan Carles Soliva, Sergio Escalera, Roger Gimeno, Oscar Vilarroya, Petia Radeva

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)

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. © 2012 Elsevier Ltd.
Original languageEnglish
Pages (from-to)591-600
JournalComputerized Medical Imaging and Graphics
Volume36
Issue number8
DOIs
Publication statusPublished - 1 Dec 2012

Keywords

  • Attention-Deficit/Hyperactivity Disorder
  • Automatic caudate segmentation
  • Decision stumps
  • Diagnostic test
  • Dissociated dipoles
  • Machine learning

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