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