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Multi-modal image registration using fuzzy kernel regression

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4 Author(s)
Edoardo Ardizzone ; Università degli studi di Palermo, DINFO - Dipartimento di Ingegneria Informatica, Viale delle Scienze - Ed.6 - 3° piano - 90128 (ITALY) ; Roberto Gallea ; Orazio Gambino ; Roberto Pirrone

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 method 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.

Published in:

2009 16th IEEE International Conference on Image Processing (ICIP)

Date of Conference:

7-10 Nov. 2009