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)


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.


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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Booij, J., Habraken, J., Bergmans, P., Tissingh, G., Winogrodzka, A., Wolters, E., Janssen, A., Stoof, J., Van Royen, E.: Imaging of dopamine transporters with Iodine-123-FP-CIT SPECT in healthy controls and patients with parkinson’s disease. Journal of Nuclear Medicine 39(11), 1879–1884 (1998)Google Scholar
  2. 2.
    Jankovic, J., Rajput, A., McDermott, M., Perl, D.: The evolution of diagnosis in early parkinson disease. Archives of Neurology 57(3), 369–372 (2000)CrossRefGoogle Scholar
  3. 3.
    Seifert, K.D., Wiener, J.I.: The impact of DaTscan on the diagnosis and management of movement disorders: A retrospective study. American Journal of Neurodegenerative Disease 2(1), 29–34 (2013)Google Scholar
  4. 4.
    Illán, I.A., Górriz, J.M., Ramírez, J., Segovia, F., Jimenez-Hoyuela, J.M., Lozano, S.J.O.: Automatic assistance to parkinson’s disease diagnosis in DaTSCAN SPECT imaging. Medical Physics 39(10), 5971–5980 (2012)CrossRefGoogle Scholar
  5. 5.
    Benamer, H.T.S., Patterson, J., Grosset, D.G.: Accurate Differentiation of Parkinsonism and Essential Tremor Using Visual Assessment of [123I]-FP-CIT SPECT Imaging: The [123I]-FP-CIT Study Group. Movement Disorders 15(3), 503–510 (2000)CrossRefGoogle Scholar
  6. 6.
    Brahim, A., Górriz, J., Ramírez, J., Khedher, L.: Linear intensity normalization of DaTSCAN images using Mean Square Error and a model-based clustering approach. Studies in Health Technology and Informatics 207, 251–260 (2014)Google Scholar
  7. 7.
    Padilla, P., Górriz, J., Ramírez, J., Salas-González, D.D., Illn, I.: Intensity normalization in the analysis of functional DaTSCAN SPECT images: The α-stable distribution-based normalization method vs other approaches. Neurocomputing 150, 4–15 (2015)CrossRefGoogle Scholar
  8. 8.
    Brahim, A., Górriz, J., Ramírez, J., Khedher, L.: Applications of gaussian mixture models and mean squared error within datscan spect imaging. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 3617–3621 (2014)Google Scholar
  9. 9.
    Scherfler, C., Seppi, K., Donnemiller, E., Goebel, G., Brenneis, C., Virgolini, I., Wenning, G., Poewe, W.: Voxel-wise analysis of [123 I] β-CIT SPECT differentiates the Parkinson variant of multiple system atrophy from idiopathic Parkinson’s disease. Brain 128(7), 1605–1612 (2005)CrossRefGoogle Scholar
  10. 10.
    Weisenfeld, N., Warfteld, S.: Normalization of joint image-intensity statistics in MRI using the Kullback-Leibler divergence. In: IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2004, vol. 1, pp. 101–104 (2004)Google Scholar
  11. 11.
    Kullback, S.: Information Theory and Statistics. Dover Books on Mathematics. John Wiley & Sons, New York (1959)zbMATHGoogle Scholar
  12. 12.
    Galimberti, G., Soffritti, G.: A multivariate linear regression analysis using finite mixtures of t distributions. Computational Statistics and Data Analysis 71, 138–150 (2014)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Jeffreys, H.: An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London Series A 186, 453–461 (1946)CrossRefzbMATHMathSciNetGoogle Scholar

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

Personalised recommendations