Relevant 3D local binary pattern based features from fused feature descriptor for differential diagnosis of Parkinson’s disease using structural MRI

• Published in: Biomedical signal processing and control Vol. 34; pp. 134 - 143
• Main Authors: Rana, Bharti, Juneja, Akanksha, Saxena, Mohit, Gudwani, Sunita, Kumaran, S. Senthil, Behari, Madhuri, Agrawal, R.K.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2017
• Subjects: 3D local binary pattern; Computer-aided diagnosis; Fused feature descriptor; Magnetic resonance imaging; Minimum redundancy maximum relevance; Parkinson's disease; Minimum redundancy maximum relevance; Parkinson's disease; Fused feature descriptor; Magnetic resonance imaging; 3D local binary pattern; Computer-aided diagnosis
• ISSN: 1746-8094
• DOI: 10.1016/j.bspc.2017.01.007

•Proposed fused feature descriptor captures better interrelation among GM, WM & CSF.•Analyzed 118 regions covering 116 regions according to AAL & 2 regions covering SN.•Captured changes in structural and statistical information, due to PD, using 3D LBP.•Obtained a minimal set of salient and uncorrelated features using t-test & mRMR.•Obtained 95% classification accuracy & identified potential imaging biomarkers. Computer-aided diagnosis (CAD) of Parkinson’s disease (PD) using structural magnetic resonance imaging is an emerging research field for the pattern recognition community. The existing research works have utilized gray matter, white matter and cerebrospinal fluid tissues individually for diagnosis of PD and have ignored the intercorrelation among the three tissues. Thus, there is a need to define a fused feature descriptor (FFD) which can capture information and intercorrelation of all the three tissues simultaneously, and to further enhance the performance of CAD. The present study proposes a simple and efficient FFD, in terms of all the three tissues, for CAD of PD. Each brain volume is represented in terms of the FFD. Then each fused volume is segmented into 118 brain regions. Thereafter, features extraction is carried out from each brain region using 3D local binary pattern. Then, a set of discriminating and uncorrelated features are identified using t-test in conjunction with minimum redundancy maximum relevance feature selection method. Finally, support vector machine is utilized to build a decision model. Volumetric 3D T1-weighted magnetic resonance imaging dataset (age & gender matched 30 PD and 30 healthy subjects) is acquired using 1.5T machine and is utilized to investigate the efficacy of the proposed method. The classification accuracy of 95% is achieved using leave-one-out cross-validation scheme which is superior to the existing methods. Regions namely Hippocampus_R, Cingulum_Mid_L, Frontal_Inf_Tri_L, Precentral_R, Precentral_L, Frontal_Mid_L, Frontal_Mid_Orb_L, Cingulum_Ant_L and Hippocampus_L, are observed to be the most discriminative regions for diagnosis of PD. The notable performance of the proposed method suggests that instead of studying the three tissues independently, their intercorrelation should also be considered. Further, the proposed method may be employed as a diagnostic tool for diagnosis of PD.