Graph-theory-based spectral feature selection for computer aided diagnosis of Parkinson's disease using T1-weighted MRI

Abstract

Introduction: Parkinson's disease (PD) is a neurological disorder, which is diagnosed on the basis of clinical history and examination alone as there are no diagnostic tests available. However, the current diagnosis highly depends on the knowledge and experience of clinicians and hence subjective in nature. Thus, the focus of this study is to develop a computer-aided diagnosis (CAD) method using T1-weighted magnetic resonance imaging (MRI) to differentiate PD from controls. Method: The proposed method utilizes graph-theory-based spectral feature selection method to select a set of discriminating features from whole brain volume. A decision model is built using support vector machine as a classifier with leave-one-out cross-validation scheme. The performance measures, namely, sensitivity, specificity, and classification accuracy, are utilized to evaluate the performance of the decision model. The efficacy of the proposed method is checked on volumetric 3D T1-weighted (1 mm iso-voxel) MRI dataset of 30 PD patients and 30 age and gender matched controls acquired with 1.5T MRI scanner. Results: Experimental results demonstrate that the proposed method is able to differentiate PD from controls with an accuracy of 86.67%, which encourages the use of CAD. The performance of the proposed method outperforms the existing methods except one. In addition, it is observed that the maximum number of selected features belong to caudate region followed by cuneus region. Thus, these regions may be considered as potential biomarkers in diagnosis of PD. Conclusion: The proposed method may be utilized by the clinicians for diagnosis of PD.

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“…Since Equation (6) maps drugs and diseases into a lowdimensional space, a natural idea occurs that the lowdimensional representations should preserve the underlying interconnection information of drugs and diseases. Studies on manifold learning (Belkin et al, 2006;Ma and Fu, 2012;Zhang et al, 2018a), spectral graph theory (Chung, 1997;Rana et al, 2015) and their applications (Zhang et al, 2016a(Zhang et al, , 2017a(Zhang et al, ,b,c, 2018bRuan et al, 2017) have shown that the learning performance can be signally enhanced, if the local topological invariant properties are preserved. Cai et al (2011) proposed Laplacian regularizations to achieve this goal.…”