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, Volume 77, Issue 3, pp 3963–3989 | Cite as

fMRI based computer aided diagnosis of schizophrenia using fuzzy kernel feature extraction and hybrid feature selection

Article

Abstract

Functional magnetic resonance imaging (fMRI) is a useful technique for capturing deformities in brain activity patterns of several disorders. Schizophrenia is one such serious psychiatric disorder that, in absence of any standard diagnostic tests, is detected from behavioural symptoms observed externally. Thus, fMRI can be used for building an effective decision model for computer aided diagnosis of schizophrenia. However, fMRI data has huge dimension compared with the number of subjects; therefore it is essential to reduce the data dimension to avoid poor generalisation performance of the decision model. In the present work, we propose a three-phase dimension reduction that comprises of segmentation of voxels of 3-D spatial maps (independent component score-maps or β-maps) into anatomical brain regions; feature extraction from each region using a novel fuzzy kernel principal component analysis; and a novel hybrid (filter-cum-wrapper) feature selection for determining a reduced subset of discriminative features. These features are used as input to support vector machine classifier for learning a decision model. The method is carried out within leave-one-out cross-validation. Classification accuracy, sensitivity, and specificity are utilised to estimate the performance on two different balanced datasets D1 and D2 (respectively acquired on 1.5 T and 3 T scanners). Both the datasets contain fMRI data of age-matched healthy subjects and schizophrenia patients for auditory oddball task, obtained from FBIRN multisite dataset. The proposed method attains best classification accuracy of 95.6% and 96.0% for D1 and D2 respectively. The proposed method shows enhanced performance over the state-of-the-art methods. Further, the discriminative brain regions identified are in accordance with the findings in related literature and may be used as potential biomarkers.

Keywords

Computer aided diagnosis fMRI Fuzzy kernel principal component analysis Hybrid forward feature selection Support vector machine Schizophrenia 

Notes

Acknowledgements

Data used for this study were downloaded from the publicly available Function BIRN Data Repository (http://fbirnbdr.birncommunity.org:8080/BDR/), supported by grants to the Function BIRN (U24-RR021992). Testbed funded by the National Centre for Research Resources at the National Institutes of Health, U.S.A. First author thanks University Grants Commission (INDIA) and second author thanks Department of Science and Technology (INDIA) for research fellowship. The authors are grateful to the reviewers for their valuable comments which helped in improving the overall quality of the manuscript.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Computer & Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

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