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

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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

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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

Content Provider	ACM Digital Library
Author	Gudwani, Sunita Juneja, Akanksha Agrawal, R K Saxena, Mohit Kumaran, S Senthil Rana, Bharti Behari, Madhuri
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.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450347532
DOI	10.1145/3009977.3009998
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-12-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Minimum redundancy maximum relevance Parkinson's disease Magnetic resonance imaging Voxel-based morphometry Computer-aided diagnosis
Content Type	Text
Resource Type	Article

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