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Multi-modal image registration by minimizing Kullback-Leibler distance between expected and observed joint class histograms

11 years 8 months ago
Multi-modal image registration by minimizing Kullback-Leibler distance between expected and observed joint class histograms
In this paper, we present a new multimodal image registration method based on the a priori knowledge of the class label mappings between two segmented input images. A joint class histogram between the image pairs is estimated by assigning each bin value equal to the total number of occurrences of the corresponding class label pairs. The discrepancy between the observed and expected joint class histograms should be minimized when the transformation is optimal. Kullback-Leibler distance (KLD) is used to measure the difference between these two histograms. Based on the probing experimental results on a synthetic dataset as well as a pair of precisely registered 3D clinical volumes, we showed that, with the knowledge of the expected joint class histogram, our method obtained longer capture range and fewer local optimal points as compared with the conventional Mutual Information (MI) based registration method. We also applied the proposed method to 2D-3D rigid registration problems between...
Ho-Ming Chan, Albert C. S. Chung, Simon C. H. Yu,
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2003
Where CVPR
Authors Ho-Ming Chan, Albert C. S. Chung, Simon C. H. Yu, Alexander Norbash, William M. Wells III
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