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Multi-modal Image Registration using Fuzzy Kernel Regression

9 years 5 months ago
 Multi-modal Image Registration using Fuzzy Kernel Regression
This paper presents a study aimed to the realization of a novel multiresolution registration framework. The transformation function is computed iteratively as a composition of local deformations determined by the maximization of mutual information. At each iteration, local transformations are joint together using fuzzy kernel regression. This technique, represents the core of the mothod and it’s formally described from a probabilistic perspective. It avoids blocking artifacts and allows to keep the final deformation spatially congruent and smooth. Both qualitative and quantitative experimental results show that this approach is equally effective for registering datasets acquired from both single and multiple diagnostic modalities.
R. Gallea, E. Ardizzone, R. Pirrone, O. Gambino
Added 15 Jan 2010
Updated 15 Jan 2010
Type Conference
Year 2009
Where ICIP
Authors R. Gallea, E. Ardizzone, R. Pirrone, O. Gambino
 @inproceedings{iccv09interpolation,
author = {Gallea, Roberto and Ardizzone, Edoardo and Pirrone, Roberto and Gambino, Orazio},
title = {Multi-modal Image Registration using Fuzzy Kernel Regression},
booktitle = {ICIP 2009 - 2009 IEEE International Conference on Image Processing},
year = {2009},
publisher = {IEEE Computer Society},
address = {Washington, DC, USA}
}
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