Parkinson's Classification and Feature Extraction from Diffusion Tensor Images
Rajeswari Sivakumar
Shannon Quinn
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.
tensor decomposition, brain imaging, diffusion tensor image, Parkinsons disease
DOI10.25080/Majora-7ddc1dd1-00f