Skewness as feature for the diagnosis of Alzheimer's disease using SPECT images

• Main Authors: Salas-Gonzalez, D., Gorriz, J.M., Ramirez, J., Alvarez, I., Lopez, M., Segovia, F., Gomez-Rio, M.
• Format: Conference Proceeding
• Language: English
• Subjects: Alzheimer's disease; Brain; Classification; Computer aided diagnosis; Dementia; Image databases; Nuclear medicine; Pixel; Power system modeling; Single photon emission computed tomography; SPECT Brain Imaging; Support vector machines
• ISSN: 1522-4880; 2381-8549
• DOI: 10.1109/ICIP.2009.5414369

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.