Parkinson's Classification and Feature Extraction from Diffusion Tensor Images

Abstract

Parkinson's disease (PD) affects over 6.2 million people around the world. Despite its prevalence, there is still no cure, and diagnostic methods are extremely subjective, relying on observation of physical motor symptoms and response to treatment protocols. Other neurodegenerative diseases can manifest similar motor symptoms and often too much neuronal damage has occurred before motor symptoms can be observed. The goal of our study is to examine diffusion tensor images (DTI) from Parkinson's and control patients through linear dynamical systems and tensor decomposition methods to generate features for training classification models. Diffusion tensor imaging emphasizes the spread and density of white matter in the brain. We will reduce the dimensionality of these images to allow us to focus on the key features that differentiate PD and control patients. We show through our experiments that these approaches can result in good classification accuracy (90%), and indicate this avenue of research has a promising future.