Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9421
Title: An adaptive fuzzy-inference-rule-based flexible model for automatic elastic image registration
Authors: Chung, FL 
Deng, Z
Wang, S
Keywords: Adaptive learning
Elastic image registration
Fuzzy inference rule
Motion estimation
Issue Date: 2009
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on fuzzy systems, 2009, v. 17, no. 5, p. 995-1010 How to cite?
Journal: IEEE transactions on fuzzy systems 
Abstract: In this study, a fuzzy-inference-rule-based flexible model (FIR-FM) for automatic elastic image registration is proposed. First, according to the characteristics of elastic image registration, an FIR-FM is proposed to model the complex geometric transformation and feature variation in elastic image registration. Then, by introducing the concept of motion estimation and the corresponding sum-of-squared-difference (SSD) objective function, the parameter learning rules of the proposed model are derived for general image registration. Based on the likelihood objective function, particular attention is also paid to the derivation of parameter learning rules for the case of partial image registration. Thus, an FIR-FM-based automatic elastic image registration algorithm is presented here. It is distinguished by its 1) strong ability in approximating complex nonlinear transformation inherited from fuzzy inference; 2) efficiency and adaptability in obtaining precise model parameters through effective parameter learning rules; and 3) completely automatic registration process that avoids the requirement of manual control, as in many traditional landmark-based algorithms. Our experiments show that the proposed method has an obvious advantage in speed and is comparable in registration accuracy as compared with a state-of-the-art algorithm.
URI: http://hdl.handle.net/10397/9421
ISSN: 1063-6706
EISSN: 1941-0034
DOI: 10.1109/TFUZZ.2009.2020154
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