AAAI Publications, The Twenty-Sixth International FLAIRS Conference

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Detection of Alzheimer’s Disease via Statistical Features from Brain Slices
Namita Aggarwal, Bharti Rana, R. K. Agrawal

Last modified: 2013-05-19


In this study, we propose a model which may assist in diagnosis of Alzheimer’s disease (AD) using T1 weighted MRI brain images. The proposed model involves construction of statistical features from multiple trans-axial slices from hippocampus and amygdala regions, which play a significant role in AD diagnosis. Features from multiple slices are then averaged, which resulted into a smaller set of relevant features. The reduced set of features enhances the performance of decision learning system, and takes less memory and computation time. Effectiveness of the proposed model is compared with recent voxel-based-morphometry work in terms of sensitivity, specificity and accuracy. Experimental results on a publicly available MRI dataset showed that the proposed method outperforms the recent voxel-based-morphometry model.


Alzheimer’s disease; Brain MRI; Feature Extraction; First and second order statistics

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