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Classification of SPECT Images Using Clustering Techniques Revisited

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

Abstract

We present a novel classification method of SPECT images based on clustering for the diagnosis of Alzheimer’s disease. The aims of the clustering approach which is based on Gaussian Mixture Model (GMM) for density estimation, is to automatically select Regions of Interest (ROIs) and to effectively reduce the dimensionality of the problem. The clusters represented by Gaussians are constructed according to a maximum likelihood criterion employing the expectation maximization (EM) algorithm. By considering only the intensity levels inside the clusters, the resulting feature space has a significantly reduced dimensionality with respect to former approaches using the voxel intensities directly as features. With this feature extraction method one avoids the so-called small sample size problem and nonlinear classifiers may be used to distinguish between the brain images of normal and Alzheimer patients. Our results show that for various classifiers the clustering method yields higher accuracy rates than the classification considering all voxel values.

Keywords

Support Vector Machine Feature Vector Radial Basis Function Gaussian Mixture Model SPECT 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • J. M. Górriz
    • 1
  • J. Ramírez
    • 1
  • A. Lassl
    • 1
  • I. Álvarez
    • 1
  • F. Segovia
    • 1
  • D. Salas
    • 1
  • M. López
    • 1
  1. 1.E.T.S.I.I., Universidad de GranadaGranadaSpain

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