Analysis of Spect Brain Images Using Wilcoxon and Relative Entropy Criteria and Quadratic Multivariate Classifiers for the Diagnosis of Alzheimer’s Disease

  • F. J. Martínez
  • D. Salas-González
  • J. M. Górriz
  • J. Ramírez
  • C. G. Puntonet
  • M. Gómez-Río
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)


This paper presents a computer aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer’s disease. 97 SPECT brain images from the “Virgen de las Nieves” Hospital in Granada are studied. The proposed method is based on two different classifiers that use two different separability criteria and a posterior reduction of the feature dimension using factor analysis. Factor loadings are used as features of two multivariate classifiers with quadratic discriminant functions. The result of these two different classifiers is used to figure out the final decision. An accuracy rate up to 92.78 % when NC and AD are considered is obtained using the proposed methodology.


Single Photon Emission Compute Tomography Gaussian Mixture Model Spect Image Relative Entropy Single Photon Emission Compute Tomography Image 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • F. J. Martínez
    • 1
  • D. Salas-González
    • 1
  • J. M. Górriz
    • 1
  • J. Ramírez
    • 1
  • C. G. Puntonet
    • 2
  • M. Gómez-Río
    • 3
  1. 1.Dept. of Signal Theory, Networking and CommunicationsUniversity of GranadaSpain
  2. 2.Dept. of Computers Architecture and TechnologySpain
  3. 3.Deptartment Nuclear MedicineVirgen Nieves HospitalGranadaSpain

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