title: Automatic assistance to Parkinson's disease diagnosis in DaTSCAN SPECT imaging.
abstract: OBJECTIVE
  In this work, an approach to computer aided diagnosis (CAD) system is proposed as a decision-making aid in Parkinsonian syndrome (PS) detection. This tool, intended for physicians, entails fully automatic preprocessing, normalization, and classification procedures for brain single-photon emission computed tomography images.
  
  
  METHODS
  Ioflupane[(123)I]FP-CIT images are used to provide in vivo information of the dopamine transporter density. These images are preprocessed using an automated template-based registration followed by two proposed approaches for intensity normalization. A support vector machine (SVM) is used and compared to other statistical classifiers in order to achieve an effective diagnosis using whole brain images in combination with voxel selection masks.
  
  
  RESULTS
  The CAD system is evaluated using a database consisting of 208 DaTSCAN images (100 controls, 108 PS). SVM-based classification is the most efficient choice when masked brain images are used. The generalization performance is estimated to be 89.02 (90.41-87.62)% sensitivity and 93.21 (92.24-94.18)% specificity. The area under the curve can take values of 0.9681 (0.9641-0.9722) when the image intensity is normalized to a maximum value, as derived from the receiver operating characteristics curves.
  
  
  CONCLUSIONS
  The present analysis allows to evaluate the impact of the design elements for the development of a CAD-system when all the information encoded in the scans is considered. In this way, the proposed CAD-system shows interesting properties for clinical use, such as being fast, automatic, and robust.
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain. illan@ugr.es
authors.forename: I A
authors.initials: IA
authors.lastname: Illan
authors.forename: J M
authors.initials: JM
authors.lastname: Gorrz
authors.forename: J
authors.initials: J
authors.lastname: Ramirez
authors.forename: F
authors.initials: F
authors.lastname: Segovia
authors.forename: J M
authors.initials: JM
authors.lastname: Jimenez-Hoyuela
authors.forename: S J
authors.initials: SJ
authors.lastname: Ortega Lozano
mesh_terms: D019540:Area Under Curve; D001331:Automation; D003936:Diagnosis, Computer-Assisted; D050483:Dopamine Plasma Membrane Transport Proteins / Q000378:metabolism; D006801:Humans; D010300:Parkinson Disease / Q000000981:diagnostic imaging* / Q000378:metabolism; D012372:ROC Curve; D060388:Support Vector Machine; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*
chemical_list: D050483:Dopamine Plasma Membrane Transport Proteins