Analysis of Brain SPECT Images for the Diagnosis of Alzheimer Disease Using First and Second Order Moments

  • D. Salas-Gonzalez
  • J. M. Górriz
  • J. Ramírez
  • M. López
  • I. Álvarez
  • F. Segovia
  • C. G. Puntonet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)


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 selection of the voxels which present greater overall difference between both modalities (normal and Alzheimer) and also lower dispersion. We measure the dispersion of the intensity values for normals and Alzheimer images by mean of the standard deviation images. The mean value of the intensities of selected voxels is used as feature for different classifiers, including support vector machines with linear kernels, fitting a multivariate normal density to each group and the k-nearest neighbors algorithm. The proposed methodology reaches an accuracy of 92 % in the classification task.


Single Photon Emission Compute Tomography Alzheimer Disease Spect Image Single Photon Emission Compute Tomography Image Linear Discriminant Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • D. Salas-Gonzalez
    • 1
  • J. M. Górriz
    • 1
  • J. Ramírez
    • 1
  • M. López
    • 1
  • I. Álvarez
    • 1
  • F. Segovia
    • 1
  • C. G. Puntonet
    • 2
  1. 1.Dept. of Signal Theory, Networking and CommunicationsUniversity of GranadaGranadaSpain
  2. 2.Dept. of Computer Architecture and Computer TechnologyUniversity of GranadaGranadaSpain

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