Anisotropic Regularization of Posterior Probability Maps Using Vector Space Projections


M. A. Rodriguez-Florido, R. Cardenes, C.-F. Westin, C. Alberola-Lopez, J. Ruiz-Alzola
Computer Aided Systems Theory (EUROCAST'03), Lecture Notes in Computer Science 2809
Pages 597-606
February 24-28, 2003

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Abstract

In this paper we address the problem of regularized data classification. To this extent we propose to regularize spatially the classposterior 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.

Figure 5.a shows a zoomed area of a slice from the original MRI volume, Fig. 5.b shows the MAP classification (the parameters are provided from a ground-truth segmentation) and Fig. 5.c shows the regularized MAP segmentation using the same probabilistic characterization. The labels for the segmentation are white for WM, gray for GM, dark gray for CSF, and black for the background. Notice how the regularized MAP provides much more spatially coherent classifications while preserving the borders.


Reference

Rodriguez-Florido MA, Cardenes R, Westin CF, Alberola-Lopez C, Ruiz-Alzola J. Anisotropic regularization of posterior probability maps using vector space projections. In RM Diaz, AQ Arencibia, eds., Computer Aided Systems Theory (EUROCAST'03), Lecture Notes in Computer Science 2809. Las Palmas de Gran Canaria, Spain: Springer Verlag, 2003;597-606.

Bibtex entry

@INPROCEEDINGS{rodriguez-floridoEUROCAST03,
  author         = {M. A. Rodriguez-Florido and R. Cardenes and C.-F. Westin   
                   and  C. Alberola-Lopez and J. Ruiz-Alzola},                 
  title          = {Anisotropic Regularization of Posterior Probability Maps   
                   Using Vector Space Projections},                            
  editor         = {Roberto Moreno Diaz and Alexis Quesada Arencibia},         
  booktitle      = {Computer Aided Systems Theory (EUROCAST'03), Lecture Notes 
                   in Computer Science 2809},                                  
  pages          = {597--606},                                                 
  month          = {February 24--28},                                          
  year           = {2003},                                                     
  address        = {Las Palmas de Gran Canaria, Spain},                        
  publisher      = {Springer Verlag},                                          
  note           = {}
}                                                         

Grants

NIH P41-RR13218 (NAC), CIMIT, TIC2001-3808-C02

Research areas

DTMRI, Tensor

Copyright Information

SpringerVerlag