Intensity Normalization of 123 I-ioflupane-SPECT Brain Images Using a Model-Based Multivariate Linear Regression Approach

  • A. Brahim
  • J. M. Górriz
  • J. Ramírez
  • L. Khedher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9107)

Abstract

The intensity normalization step is essential, as it corresponds to the initial step in any subsequent computer-based analysis. In this work, a proposed intensity normalization approach based on a predictive modeling using multivariate linear regression (MLR) is presented. Different intensity normalization parameters derived from this model will be used in a linear procedure to perform the intensity normalization of 123 I-ioflupane-SPECT brain images. This proposed approach is compared to conventional intensity normalization methods, such as specific-to-non-specific binding ratio, integral-based intensity normalization and intensity normalization by minimizing the Kullback-Leibler divergence. For the performance evaluation, a statistical analysis is used by applying the Euclidean distance and the Jeffreys divergence. In addition, a classification task using support vector machine to evaluate the impact of the proposed methodology for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome detection.

Keywords

Intensity normalization DaTSCAN SPECT images Multivariate Linear Regression Parkinsonian syndrome Computer-aided diagnosis system 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • A. Brahim
    • 1
  • J. M. Górriz
    • 1
  • J. Ramírez
    • 1
  • L. Khedher
    • 1
  1. 1.Department of Signal Theory, Networking and CommunicationsUniversity of GranadaGranadaSpain

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