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.
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Brahim, A., Górriz, J.M., Ramírez, J., Khedher, L. (2015). Intensity Normalization of 123 I-ioflupane-SPECT Brain Images Using a Model-Based Multivariate Linear Regression Approach. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_8
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DOI: https://doi.org/10.1007/978-3-319-18914-7_8
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