Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines. (February 2018)

• Record Type: Journal Article
• Authors: Morisi, Rita; Manners, David Neil; Gnecco, Giorgio; Lanconelli, Nico; Testa, Claudia; Evangelisti, Stefania; Talozzi, Lia; Gramegna, Laura Ludovica; Bianchini, Claudio; Calandra-Buonaura, Giovanna; Sambati, Luisa; Giannini, Giulia; Cortelli, Pietro; Tonon, Caterina; Lodi, Raffaele
• Is Part Of: Parkinsonism & related disorders. Volume 47(2018)
• Page Start: 64
• Page End: 70
• Publication Date: 2018-02
• Subjects: Parkinsonian disorders -- MR markers -- Feature selection -- Support vector machines -- Graph-based features; Parkinson's disease -- Periodicals; Movement disorders -- Periodicals; Movement Disorders -- Periodicals; Nerve Degeneration -- Periodicals; Nervous System Diseases -- Periodicals; Parkinson Disease -- Periodicals; Tremor -- Periodicals; Parkinson, Maladie de -- Périodiques; Parkinson's disease; 616.833
• DOI: 10.1016/j.parkreldis.2017.11.343
• Languages: English
• ISSNs: 1353-8020

Abstract

Abstract: Background and purpose: In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. Methods: We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. Results: When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. Conclusions: The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis. Highlights: Support vector machines (SVM) are applicable to multimodal brain MRI and 1 H-MRS data. SVMs with graph-derived features improve diagnostic accuracy, and require fewer features. PD, PSP, MSA-C and MSA–P patients are automatically classified with good accuracy.