Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/47458
Title: Anisotropic regularization of posterior probability maps using vector space projections. application to MRI segmentation
Authors: Rodriguez-Florido, M. A.
Cárdenes, R.
Westin, C. F.
Alberola, C.
Ruiz-Alzola, J. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Anisotropic Regularization
Markov Random Field
Posterior Probability Model
Synthetic Aperture Radar
Issue Date: 2003
Journal: Lecture Notes in Computer Science 
Conference: 9th International Workshop on Computer Aided Systems Theory 
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.
URI: http://hdl.handle.net/10553/47458
ISBN: 3540202218
ISSN: 0302-9743
DOI: 10.1007/978-3-540-45210-2_54
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[ISSN 0302-9743],v. 2809, p. 597-606
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