An Optimal Approach for Selecting Discriminant Regions for the Diagnosis of Alzheimer's Disease

Title:An Optimal Approach for Selecting Discriminant Regions for the Diagnosis of Alzheimer's Disease

Volume: 13 Issue: 7

Author(s): D. Salas-Gonzalez, F. Segovia, F. J. Martínez-Murcia, E. W. Lang, J. M. Gorriz and J. Ramırez

Affiliation:

Keywords: Computer aided diagnosis, feature extraction and selection, SPECT brain imaging, support vector machines.

Abstract: 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.

Cite this article as:

Salas-Gonzalez D., Segovia F., J. Martínez-Murcia F., W. Lang E., M. Gorriz J. and Ramırez J., An Optimal Approach for Selecting Discriminant Regions for the Diagnosis of Alzheimer's Disease, Current Alzheimer Research 2016; 13 (7) . https://dx.doi.org/10.2174/1567205013666160415154852