Dementia is an evolving challenge in society, and no disease-modifying treatment exists. Diagnosis can be demanding and MRimaging 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 patientswith Alzheimer’s disease (AD), Lewy body dementia (LBD), and normal controls (NC). Analysis was conducted in areas with whitematter 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 versusLBD, we achieved 0.87 (0.08) and 0.74 (0.16), respectively. The performance using 3DT1 images was notably better than when usingFLAIR images.Theresults fromtheWMregion gave similar results as in theWMLregion.Our study demonstrates that LBP textureanalysis in brain MR images can be successfully used for computer based dementia diagnosis.
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