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

  • Authors:

  • D. Salas-Gonzalez;J. M. Górriz;J. Ramírez;I. Álvarez;M. López;F. Segovia;M. Gómez-Río

  • Affiliations:

  • Dept. Signal Theory, Networking and Communications, University of Granada, Spain;Dept. Signal Theory, Networking and Communications, University of Granada, Spain;Dept. Signal Theory, Networking and Communications, University of Granada, Spain;Dept. Signal Theory, Networking and Communications, University of Granada, Spain;Dept. Signal Theory, Networking and Communications, University of Granada, Spain;Dept. Signal Theory, Networking and Communications, University of Granada, Spain;Department of Nuclear Medicine, Virgen de las Nieves University Hospital, Granada, Spain and Dept. Signal Theory, Networking and Communications, University of Granada, Spain

  • Venue:
  • ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.