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Towards the Identification of Parkinson’s Disease Using only T1 MR Images

  • Sara Soltaninejad
  • Irene Cheng
  • Anup Basu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11010)

Abstract

Parkinson’s Disease (PD) is one of the most common types of neurological diseases caused by progressive degeneration of dopaminergic neurons in the brain. Even though there is no fixed cure for this neurodegenerative disease, earlier diagnosis followed by earlier treatment can help patients have a better quality of life. Magnetic Resonance Imaging (MRI) has been one of the most popular diagnostic tool in recent years because it avoids harmful radiations. In this paper, we investigate the plausibility of using MRIs for automatically diagnosing PD. Our proposed method has three main steps: (1) Preprocessing, (2) Feature Extraction, and (3) Classification. The FreeSurfer library is used for the first and the second steps. For classification, three main types of classifiers, including Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM), are applied and their classification ability is compared. The Parkinson’s Progression Markers Initiative (PPMI) data set is used to evaluate the proposed method. The proposed system prove to be promising in assisting the diagnosis of PD.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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