title: Improved parkinsonism diagnosis using a partial least squares based approach.
abstract: OBJECTIVE
  An accurate and early diagnosis of Parkinsonian syndrome (PS) is nowadays a challenge. This syndrome includes several pathologies with similar symptoms (Parkinson's disease, multisystem atrophy, progressive supranuclear palsy, corticobasal degeneration and others) which make the diagnosis more difficult. (123)I-ioflupane allows to obtain in vivo images of the brain that can be used to assist the PS diagnosis and provides a way to improve its accuracy.
  
  
  METHODS
  In this paper, we show a novel method to automatically classify (123)I-ioflupane images into two groups: controls or PS. The proposed methodology analyzes separately each hemisphere of the brain by means of a novel approach based on partial least squares (PLS) and support vector machine.
  
  
  RESULTS
  A database with 189 (123)I-ioflupane images (94 controls and 95 pathological images) was used for evaluation purposes. The application of the proposed method based on PLS yields high accuracy rates up to 94.7% with sensitivity = 93.7% and specificity = 95.7%, outperforming previous approaches based on singular value decomposition, which are used as a reference.
  
  
  CONCLUSIONS
  The use of advanced techniques based on classical signal analysis and their application to each hemisphere of the brain separately improves the (assisted) diagnosis of PS.
authors: Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain. fsegovia@ugr.es
authors: F
authors: F
authors: Segovia
authors: J M
authors: JM
authors: Gorriz
authors: J
authors: J
authors: Ramirez
authors: I
authors: I
authors: Alvarez
authors: J M
authors: JM
authors: Jimenez-Hoyuela
authors: S J
authors: SJ
authors: Ortega
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D010300:Parkinson Disease / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*