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Voxel-based morphometry and minimum redundancy maximum relevance method for classification of Parkinson's disease and controls from T1-weighted MRI

Published: 18 December 2016 Publication History

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder, which needs to be accurately diagnosed in early stage. Voxel-based morphometry (VBM) has been extensively utilized to determine focal changes between PD patients and controls. However, it is not much utilized in differential diagnosis of an individual subject. Thus, in this study, VBM findings in conjunction with minimum redundancy maximum relevance (mRMR) method are utilized to obtain a set of relevant and non-redundant features for computer-aided diagnosis (CAD) of PD using T1-weighted MRI. In the proposed method, firstly, statistical features are extracted from the clusters obtained from statistical maps, generated using VBM, of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) independently and their different combinations. Then mRMR, a multivariate feature selection method, is utilized to find a minimal set of relevant and non-redundant features. Finally, support vector machine is utilized to learn a decision model using the selected features. Experiments are performed on newly acquired T1-weighted MRI of 30 PD patients and 30 age & gender matched controls. The performance is evaluated using leave-one out cross-validation scheme in terms of sensitivity, specificity and classification accuracy. The maximum accuracy of 88.33% is achieved for GM+WM and GM+WM+CSF. In addition, the proposed method outperforms the existing methods. It is also observed that the selected clusters belong to regions namely middle and superior frontal gyrus for GM, inferior, middle frontal gyrus and insula for WM and lateral ventricle for CSF. Further, correlation of UPDRS/H&Y staging scale with GM/WM/CSF volume is observed to be not significant. Appreciable classification performance of the proposed method highlights the potential of the proposed method in CAD support system for the clinicians in PD diagnosis.

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  • (2022)Machine Learning Models for Diagnosis of Parkinson’s Disease Using Multiple Structural Magnetic Resonance Imaging FeaturesFrontiers in Aging Neuroscience10.3389/fnagi.2022.80852014Online publication date: 13-Apr-2022

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cover image ACM Other conferences
ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing
December 2016
743 pages
ISBN:9781450347532
DOI:10.1145/3009977
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 18 December 2016

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Author Tags

  1. Parkinson's disease
  2. computer-aided diagnosis
  3. magnetic resonance imaging
  4. minimum redundancy maximum relevance
  5. voxel-based morphometry

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ICVGIP '16
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  • MathWorks
  • Microsoft Research

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ICVGIP '16 Paper Acceptance Rate 95 of 286 submissions, 33%;
Overall Acceptance Rate 95 of 286 submissions, 33%

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  • (2022)Machine Learning Models for Diagnosis of Parkinson’s Disease Using Multiple Structural Magnetic Resonance Imaging FeaturesFrontiers in Aging Neuroscience10.3389/fnagi.2022.80852014Online publication date: 13-Apr-2022

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