Computer-aided diagnosis of Parkinson's disease based on [123I]FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector machines. (24th February 2015)

• Record Type: Journal Article
• Authors: Oliveira, Francisco P M; Castelo-Branco, Miguel
• Is Part Of: Journal of neural engineering. Volume 12:Number 2(2015:Apr.)
• Publication Date: 2015-02-24
• Subjects: DaTSCAN -- binding potential -- classification -- automated image analysis; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2560/12/2/026008
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: 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. (ClinicalTrials.gov Identifier: NCT01141023)