Automatic Separation of Parkinsonian Patients and Control Subjects Based on the Striatal Morphology

  • Fermín SegoviaEmail author
  • Juan M. Górriz
  • Javier Ramírez
  • Francisco J. Martínez-Murcia
  • Diego Castillo-Barnes
  • Ignacio A. Illán
  • Andres Ortiz
  • Diego Salas-Gonzalez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10337)


Parkinsonism is the second more common neurological disease and affects around 1%–2% of people over 65 years, being around 20%–24% of them incorrectly diagnosed. The disorder is associated to a progressive loss of dopaminergic neurons of the striatum. Thus, its diagnosis is usually corroborated by analyzing neuroimaging data of this region. In this work, we propose a novel computer system to automatically distinguish between parkinsonian patients and neurologically healthy subjects using \(^{123}\)I-FP-CIT SPECT data, a neuroimaging modality widely used to assist the diagnosis of Parkinsonism. First, the voxels of the striatum were selected using an intensity threshold. These voxels were then projected over the axial plane, resulting in a two-dimensional image with the striatum shape. Subsequently, the size and shape of the left and right sides of the striatum were characterized by 5 features: area, eccentricity, orientation and length of the major and minor axes. Finally, the extracted features were used along with a Support Vector Machine classifier to separate patients and controls. An accuracy rate of 91.53% (\(p<0.001\)) was estimated using a k-fold cross-validation scheme and a database with 189 \(^{123}\)I-FP-CIT SPECT neuroimages. This rate outperformed the ones achieved by previous approaches when using the same data.


Morphological features DaTSCAN \(^{123}\)I-FP-CIT SPECT Striatum Machine learning Parkinson’s disease Support Vector Machine 



The authors are grateful to MD. J.M. Jiménez-Hoyuela and MD. S.J. Ortega from “Virgen de la Victoria” hospital (Málaga, Spain) for their help in data management. This work was supported by the MINECO/FEDER under the TEC2015-64718-R project and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía under the Excellence Project P11-TIC-7103 and a Talent Hub project approved by the Andalucía Talent Hub Program launched by the Andalusian Knowledge Agency, co-funded by the European Union’s Seventh Framework Program, Marie Sklodowska-Curie actions (COFUND Grant Agreement no. 291780) and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fermín Segovia
    • 1
    Email author
  • Juan M. Górriz
    • 1
  • Javier Ramírez
    • 1
  • Francisco J. Martínez-Murcia
    • 1
  • Diego Castillo-Barnes
    • 1
  • Ignacio A. Illán
    • 2
  • Andres Ortiz
    • 3
  • Diego Salas-Gonzalez
    • 1
  1. 1.Department of Signal Theory, Networking and CommunicationsUniversity of GranadaGranadaSpain
  2. 2.The Florida State UniversityFloridaUSA
  3. 3.Department of Communications EngineeringUniversity of MálagaMálagaSpain

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