Computer-aided diagnosis of Parkinson's disease based on [(123)I]FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector machines

J Neural Eng. 2015 Apr;12(2):026008. doi: 10.1088/1741-2560/12/2/026008. Epub 2015 Feb 24.


Objective: The aim of the present study was to develop a fully-automated computational solution for computer-aided diagnosis in Parkinson syndrome based on [(123)I]FP-CIT single photon emission computed tomography (SPECT) images.

Approach: A dataset of 654 [(123)I]FP-CIT SPECT brain images from the Parkinson's Progression Markers Initiative were used. Of these, 445 images were of patients with Parkinson's disease at an early stage and the remainder formed a control group. The images were pre-processed using automated template-based registration followed by the computation of the binding potential at a voxel level. Then, the binding potential images were used for classification, based on the voxel-as-feature approach and using the support vector machines paradigm.

Main results: The obtained estimated classification accuracy was 97.86%, the sensitivity was 97.75% and the specificity 98.09%.

Significance: The achieved classification accuracy was very high and, in fact, higher than accuracies found in previous studies reported in the literature. In addition, results were obtained on a large dataset of early Parkinson's disease subjects. In summation, the information provided by the developed computational solution potentially supports clinical decision-making in nuclear medicine, using important additional information beyond the commonly used uptake ratios and respective statistical comparisons. ( Identifier: NCT01141023).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Dopamine Plasma Membrane Transport Proteins / metabolism*
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods*
  • Male
  • Middle Aged
  • Molecular Imaging / methods
  • Parkinson Disease / diagnostic imaging
  • Parkinson Disease / metabolism*
  • Pattern Recognition, Automated / methods
  • Radiopharmaceuticals / pharmacokinetics
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Support Vector Machine
  • Tomography, Emission-Computed, Single-Photon / methods*
  • Tropanes / pharmacokinetics*


  • Dopamine Plasma Membrane Transport Proteins
  • Radiopharmaceuticals
  • Tropanes
  • 2-carbomethoxy-8-(3-fluoropropyl)-3-(4-iodophenyl)tropane

Associated data