Independent Component Analysis of SPECT Images to Assist the Alzheimer’s Disease Diagnosis

  • Ignacio Álvarez
  • Juan M. Górriz
  • Javier Ramírez
  • Diego Salas-Gonzalez
  • Miriam López
  • Carlos García Puntonet
  • Fermin Segovia
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 56)


Finding sensitive and appropriate technologies for non-invasive observation and early detection of the Alzheimer’s Type Dementia (ATD) are of fundamental importance to develop early treatments. Single Photon Emission Computed Tomography (SPECT) images are commonly used by physicians to assist the diagnosis, rating them by visual evaluations. In this work, we present a computer aided diagnosis method in which a selection of relevant features was extracted from each patient image by means of Independent Component Analysis (ICA). An average image was computed within the normal or Alzheimer’s disease brain image class, to be later used to extract a set of independent sources that best symbolized each class characteristics. Each brain image was projected onto the space spanned by this independent sources basis, and the extracted information was used to train a SVM classifier which could classify new subjects in a unsupervised manner.


Single Photon Emission Compute Tomography Independent Component Analysis Single Photon Emission Compute Tomography Image Small Sample Size Problem Spect Brain Image 
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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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ignacio Álvarez
    • 1
  • Juan M. Górriz
    • 1
  • Javier Ramírez
    • 1
  • Diego Salas-Gonzalez
    • 1
  • Miriam López
    • 1
  • Carlos García Puntonet
    • 2
  • Fermin Segovia
    • 1
  1. 1.Dept. of Signal Theory, Networking and CommunicationsUniversity of GranadaSpain
  2. 2.Dept. of Architecture and Computer TechnologyUniversity of GranadaSpain

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