Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images

Author(s)

Publication date

2015-03-22

Series/Report no

Journal of Biomedical Imaging;2015

Publisher

Hindawi Publishing Corporation

Document type

Abstract

Dementia is an evolving challenge in society, and no disease-modifying treatment exists. Diagnosis can be demanding and MR imaging may aid as a noninvasive method to increase prediction accuracy. We explored the use of 2D local binary pattern (LBP) extracted from FLAIR and T1 MR images of the brain combined with a Random Forest classifier in an attempt to discern patients with Alzheimer’s disease (AD), Lewy body dementia (LBD), and normal controls (NC). Analysis was conducted in areas with white matter lesions (WML) and all of white matter (WM). Results from 10-fold nested cross validation are reported as mean accuracy, precision, and recall with standard deviation in brackets.The best result we achieved was in the two-class problem NC versus AD + LBD with total accuracy of 0.98 (0.04). In the three-class problem AD versus LBD versus NC and the two-class problem AD versus LBD, we achieved 0.87 (0.08) and 0.74 (0.16), respectively. The performance using 3DT1 images was notably better than when using FLAIR images.Theresults fromtheWMregion gave similar results as in theWMLregion.Our study demonstrates that LBP texture analysis in brain MR images can be successfully used for computer based dementia diagnosis.

Keywords

Version

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Permanent URL (for citation purposes)

  • http://hdl.handle.net/10642/3281