Abstract
In this work, we normalize the intensity of 40 FP-CIT SPECT images from the Parkinson’s Progression Markers Initiative assuming that the histogram of intensity values follows an \(\alpha \)-stable distribution. Then, we study the normalized images. The interclass separation of the Parkinson’s disease (PD) brain images and the healthy control (HC) are calculated by means of the Mann-Whitney-Wilcoxon U-test. The intensity transformed images present higher inter-class separation according to the estimation of the U-test.
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Acknowledgements
This work was supported by MINECO/FEDER under the TEC2015-64718-R project and Junta de Andalucía under the P11-TIC-7103 Excellence Project.
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Castillo-Barnes, D. et al. (2017). On a Heavy-Tailed Intensity Normalization of the Parkinson’s Progression Markers Initiative Brain Database. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_29
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DOI: https://doi.org/10.1007/978-3-319-59740-9_29
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