%0 Journal Article
%T Efficient Image Registration Using Discrete Orthogonal
Stockwell Transform and SIFT
%A Hossam-E M. Shamardan
%J New name: ISSN: Past name: Past ISSN: Frequency: Journal DOI: Prof. Tian-Xiao He Dr. Paul Bracken Prof. Jia Li
%D 2018
%R 10.9734/JAMCS/2018/40026
%X Image registration is a vital step for most of recent image processing applications. In this paper, a novel approach for magnetic resonance images (MRI) registration based on artificial neural network (ANN) is proposed. The ANN achieves the state-of-the-art performance for estimation problems, hence it has been adopted for estimating the registration parameters. The ANN is fed by joined features extracted from both of spatial and frequency domains. The Scale Invariant Feature Transform (SIFT) is used for extracting the spatial domain features while The Discrete Orthogonal Stockwell Transform (DOST) coefficients are used as frequency domain features. The combined features provide a robust foundation for the registration process. Many experiments were performed to test the success of the new approach. The simulation results demonstrate that the proposed approach yields a better registration performance with regard to both the accuracy, and the robustness versus noise conditions.
%U http://www.sciencedomain.org/abstract/23885