Fast BlockMatching Registration with Entropy-based Similarity

Eduardo Suarez-Santana, Rafael Nebot, C.-F. Westin, Juan Ruiz-Alzola
Tenth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '07)
Pages 178-185
2007

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Abstract

This paper describes the implementation of a multidimensional block-matching nonrigid registration algorithm. The main features of the algorithm are its simplicity, its free form nature, the modularity of the similarity measure, which makes it possible using local entropy-based similarity measures and the avoidance of the optimization module. The algorithm implementation described in this paper is based on the method by Suarez et al. [5, 3]. This paper, which has already been submitted to the Insight Journal, is accompanied with the source code, input data, parameters and output data used for validating the algorithm described in it. surgically relevant white matter tracts involved in the tumor region, and the corresponding healthy tracts on the contralateral side, were identified (tracts represented in yellow and tumor region represented in green).

Images in experiment.

Reference

Suarez-Santana E, Nebot R, Westin CF, Ruiz-Alzola J. Fast blockmatching registration with entropy-based similarity. In Tenth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '07), Lecture Notes in Computer Science. Brisbane, Australia, 2007;178-185.

Bibtex entry

@InProceedings{suarez-santanaMICCAI07,
  author         = {Eduardo Suarez-Santana and Rafael Nebot and Carl-Fredrik   
                   Westin and Juan Ruiz-Alzola},                               
  title          = {Fast BlockMatching Registration with Entropy-based         
                   Similarity},                                                
  booktitle      = {Tenth International Conference on Medical Image Computing  
                   and  Computer-Assisted Intervention (MICCAI '07)},          
  pages          = {178--185},                                                 
  year           = {2007},                                                     
  series         = {Lecture Notes in Computer Science},                        
  address        = {Brisbane, Australia}, }

Grants

NIH U54-EB005149 (NAMIC), NIH P41-RR13218 (NAC)