This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The proposed methodology is based on the calculation of the skewness to each m-by-m sliding block of the transaxial slices of the SPECT brain images. We replace the center pixel in the m-by-m block by the skewness value and build a new 3-D brain image which will be used for classification purposes. After that, we select the voxels which present a Welch's t-statistic between both classes, Normal and Alzheimer images, higher (or lower) than a given threshold. The mean, standard deviation, skewness and kurtosis are calculated for selected voxels and they are chosen as feature vectors for three different classifiers: support vector machines with linear kernel, classification trees and multivariate normal model. The proposed methodology reaches an accuracy higher than 98% in the classification task.
['Diego Salas-Gonzalez', 'Juan Manuel Górriz', 'Javier Ramírez 0001', 'Ignacio Álvarez', 'Míriam López', 'Fermín Segovia', 'Manuel Gómez-Río']
Skewness as feature for the diagnosis of Alzheimer's disease using SPECT images