Selecting Regions of Interest in SPECT Images Using Wilcoxon Test for the Diagnosis of Alzheimer's Disease

This work presents a computer-aided diagnosis technique for improving the accuracy of the diagnosis of the Alzheimer’s disease (AD). Some regions of the SPECT image discriminate more between healthy and AD patients than others, thus, it is important to design an automatic tool for selecting these regions. This work shows the performance of the Mann-Whitney-Wilcoxon U-test, a non-parametric technique which allows to select voxels of interest. Those voxels with higher U values are selected and their intensity values are used as input for a Support Vector Machine classifier with linear kernel. The proposed methodology yields an accuracy greater than 90% in the diagnosis of the AD and outperforms existing techniques including the voxel-as-features approach.

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