Texture Features Based Detection of Parkinson’s Disease on DaTSCAN Images

  • Francisco Jesús Martínez-Murcia
  • Juan Manuel Górriz
  • Javier Ramírez
  • I. Alvarez Illán
  • C. G. Puntonet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7931)


In this work, a novel approach to Computer Aided Diagnosis (CAD) system for the Parkinson’s Disease (PD) is proposed. This tool is intended for physicians, and is based on fully automated methods that lead to the classification of Ioflupane/FP-CIT-I-123 (DaTSCAN) SPECT images. DaTSCAN images from the Parkinson Progression Markers Initiative (PPMI) are used to have in vivo information of the dopamine transporter density. These images are normalized, reduced (using a mask), and then a GLC matrix is computed over the whole image, extracting several Haralick texture features which will be used as a feature vector in the classification task. Using the leave-one-out cross-validation technique over the whole PPMI database, the system achieves results up to a 95.9% of accuracy, and 97.3% of sensitivity, with positive likelihood ratios over 19, demonstrating our system’s ability on the detection of the Parkinson’s Disease by providing robust and accurate results for clinical practical use, as well as being fast and automatic.


Parkinson’s Disease DaTSCAN images Computer Aided Diagnosis Haralick Texture Features Support Vector Machines Supervised Learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francisco Jesús Martínez-Murcia
    • 1
  • Juan Manuel Górriz
    • 1
  • Javier Ramírez
    • 1
  • I. Alvarez Illán
    • 1
  • C. G. Puntonet
    • 2
  1. 1.Department of Signal Theory, Networking and CommunicationsUniversidad of GranadaSpain
  2. 2.Department of Computer Architecture and TechnologyUniversidad de GranadaSpain

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