Regions-of-interest based automated diagnosis of Parkinson’s disease using T1-weighted MRI

•First automated ROI based computer aided diagnosis of PD using T1-weighted MRI.•Analysis of GM, WM and CSF from five ROIs individually, in pairs and triplets.•Evaluation on acquired age and gender matched dataset (30 PD and 30 healthy).•Forward feature selection based on mutual information outperforms ranking method.•Best classification accuracy of 86.67% is achieved with SN for GM and SN + HP for WM.

Parkinson’s disease (PD) is the second most common neurodegenerative disorder of the central nervous system. For its early management and accurate prognosis, there is a need to develop automated and non-invasive computer-aided diagnosis (CAD) technique(s). The present study proposes a novel region-of-interest (ROI) based CAD technique using T1-weighted magnetic resonance imaging (MRI) to discriminate PD patients from healthy subjects. A volumetric 3D T1-weighted (1 mm isovoxel) MRI of 30 PD patients and age & gender matched 30 healthy subjects is acquired on a 1.5 T MRI scanner. Five well-documented regions affected in PD, namely substantia nigra (SN), thalamus, hippocampus, frontal lobe (FL) and mid-brain are analyzed individually and in combinations of pairs and triplets. Features are constructed from gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) values of voxel from these regions. A small set of discriminating and non-redundant features is selected using mutual information based approach in conjunction with unpaired two-tailed two-sample t-test based ranking. A decision model is built with the help of support vector machine (SVM) as a classifier. The performance of the decision model is evaluated in terms of sensitivity, specificity and accuracy with leave-one-out cross-validation scheme. Experimental results demonstrate that the proposed method is able to differentiate PD from healthy subjects with a maximum accuracy of 86.67% with SN for GM and combination of SN & FL for WM; and outperforms the voxel-based morphometry method. Furthermore, loss in GM and WM volume and gain in CSF volume is observed in PD patients in comparison to healthy subjects. The excellent performance of the proposed method is beneficial for clinicians as it can be used as a decision support system which requires less time and efforts in diagnosing PD. In addition, it also encourages the application of CAD in medical domain.