In this work, we present a fully automatic computer-aided diagnosis method for the early
diagnosis of the Alzheimer’s disease. We study the distance between classes (labelled as normal controls
and possible Alzheimer’s disease) calculated in 116 regions of the brain using the Welchs’s t-test.
We select the regions with highest Welchs’s t-test value as features to perform classification. Furthermore,
we also study the less discriminative region according to the t-test (regions with lowest t-test
absolute values) in order to use them as reference. We show that the mean and standard deviation of
the intensity values in these two regions, the less and most discriminative according to the Welch’s ttest,
can be combined as a vector. The modulus and phase of this vector reveal statistical differences
between groups which can be used to improve the classification task. We show how they can be used
as input for a support vector machine classifier. The proposed methodology is tested in a SPECT brain database of 70
SPECT brain images yielding an accuracy up to 91.5% for a wide range of selected voxels.
Keywords: Computer aided diagnosis, feature extraction and selection, SPECT brain imaging, support vector machines.
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