Please use this identifier to cite or link to this item:
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.
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
Appears in Collections:Artículos
Adobe PDF (358,08 kB)
Show full item record

Page view(s)

checked on Jan 8, 2022


checked on Jan 8, 2022

Google ScholarTM




Export metadata

Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.