title: Parametrization of textural patterns in 123I-ioflupane imaging for the automatic detection of Parkinsonism.
abstract: OBJECTIVE
  A novel approach to a computer aided diagnosis system for the Parkinson's disease is proposed. This tool is intended as a supporting tool for physicians, based on fully automated methods that lead to the classification of (123)I-ioflupane SPECT images.
  
  
  METHODS
  (123)I-ioflupane images from three different databases are used to train the system. The images are intensity and spatially normalized, then subimages are extracted and a 3D gray-level co-occurrence matrix is computed over these subimages, allowing the characterization of the texture using Haralick texture features. Finally, different discrimination estimation methods are used to select a feature vector that can be used to train and test the classifier.
  
  
  RESULTS
  Using the leave-one-out cross-validation technique over these three databases, the system achieves results up to a 97.4% of accuracy, and 99.1% of sensitivity, with positive likelihood ratios over 27.
  
  
  CONCLUSIONS
  The system presents a robust feature extraction method that helps physicians in the diagnosis task by providing objective, operator-independent textural information about (123)I-ioflupane images, commonly used in the diagnosis of the Parkinson's disease. Textural features computation has been optimized by using a subimage selection algorithm, and the discrimination estimation methods used here makes the system feature-independent, allowing us to extend it to other databases and diseases.
authors.affiliation: Signal Processing and Biomedical Applications Research Group, Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain.
authors.forename: F J
authors.initials: FJ
authors.lastname: Martinez-Murcia
authors.affiliation: Signal Processing and Biomedical Applications Research Group, Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain.
authors.forename: J M
authors.initials: JM
authors.lastname: Górriz
authors.affiliation: Signal Processing and Biomedical Applications Research Group, Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain.
authors.forename: J
authors.initials: J
authors.lastname: Ramírez
authors.affiliation: Department of Nuclear Medicine, Virgen de las Nieves Hospital, Granada 18071, Spain.
authors.forename: M
authors.initials: M
authors.lastname: Moreno-Caballero
authors.affiliation: Department of Nuclear Medicine, Virgen de las Nieves Hospital, Granada 18071, Spain.
authors.forename: M
authors.initials: M
authors.lastname: Gómez-Río
mesh_terms: D001331:Automation; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009662:Nortropanes; D020734:Parkinsonian Disorders / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*
chemical_list: D009662:Nortropanes; C519528:ioflupane