Abstract
In this paper we address the problem of regularized data classification. To this extent we propose to regularize spatially the class-posterior probability maps, to be used by a MAP classification rule, by applying a non-iterative anisotropic filter to each of the class-posterior maps. Since the filter cannot guarantee that the smoothed maps preserve their probabilities meaning (i.e., probabilities must be in the range [0,1] and the class-probabilities must sum up to one), we project the smoothed maps onto a probability subspace. Promising results are presented for synthetic and real MRI datasets.
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Rodriguez-Florido, M.A., Cárdenes, R., Westin, C.F., Alberola, C., Ruiz-Alzola, J. (2003). Anisotropic Regularization of Posterior Probability Maps Using Vector Space Projections. Application to MRI Segmentation. In: Moreno-Díaz, R., Pichler, F. (eds) Computer Aided Systems Theory - EUROCAST 2003. EUROCAST 2003. Lecture Notes in Computer Science, vol 2809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45210-2_54
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DOI: https://doi.org/10.1007/978-3-540-45210-2_54
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