Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of Parkinson's disease based on [123I]FP-CIT SPECT images

• Type: Article in International Scientific Journal
• Date: 2018-06

Authors

Francisco P. M. Oliveira (Author) Other; Diogo Borges Faria (Author) Other; Durval C. Costa (Author) Other; Miguel Castelo Branco (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: European Journal of Nuclear Medicine and Molecular Imaging

Vol. 45 No. 6

Pages: 1052-1062

ISSN: 1619-7070

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 5 Citations

ISI Web of Science

Scopus - 23 Citations

Scientific classification

CORDIS: Technological sciences

FOS: Medical and Health sciences

Other information

Authenticus ID: P-00N-BQ9

DOI: 10.1007/s00259-017-3918-7

Abstract (EN): Purpose This work aimed to assess the potential of a set of features extracted from [I-123] FP-CIT SPECT brain images to be used in the computer-aided "in vivo" confirmation of dopaminergic degeneration and therefore to assist clinical decision to diagnose Parkinson's disease. Methods Seven features were computed from each brain hemisphere: five standard features related to uptake ratios on the striatum and two features related to the estimated volume and length of the striatal region with normal uptake. The features were tested on a dataset of 652 [I-123] FP-CIT SPECT brain images from the Parkinson's Progression Markers Initiative. The discrimination capacities of each feature individually and groups of features were assessed using three different machine learning techniques: support vector machines (SVM), k-nearest neighbors and logistic regression. Results Cross-validation results based on SVM have shown that, individually, the features that generated the highest accuracies were the length of the striatal region (96.5%), the putaminal binding potential (95.4%) and the striatal binding potential (93.9%) with no statistically significant differences among them. The highest classification accuracy was obtained using all features simultaneously (accuracy 97.9%, sensitivity 98% and specificity 97.6%). Generally, slightly better results were obtained using the SVM with no statistically significant difference to the other classifiers for most of the features. Conclusions The length of the striatal region uptake is clinically useful and highly valuable to confirm dopaminergic degeneration "in vivo" as an aid to the diagnosis of Parkinson's disease. It compares fairly well to the standard uptake ratio-based features, reaching, at least, similar accuracies and is easier to obtain automatically. Thus, we propose its day to day clinical use, jointly with the uptake ratio-based features, in the computer-aided diagnosis of dopaminergic degeneration in Parkinson's disease.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11