Multi-sensor image registration based-on local phase coherence

The major challenges in automatic multi-sensor image registration are the inconsistency in intensity or contrast patterns, and the existence of partial or missing information between images. Here we propose a novel image registration method based on local phase coherence features, which are insensitive to changes in intensity or contrast. Furthermore, a new objective function based on weighted mutual information is proposed, where less weight is given to the objects that have no correspondence between images. The proposed method has been tested on both synthetic and medical images and evaluated based on registration accuracy. Our experiments demonstrate good performance of the proposed approach with missing or partial data, with significant changes in contrast, and with the presence of noise.

[1]  Alexander Wong,et al.  Robust Multimodal Registration Using Local Phase-Coherence Representations , 2009, J. Signal Process. Syst..

[2]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[3]  G. Marchal,et al.  Multi-modal volume registration by maximization of mutual information , 1997 .

[4]  David J. Fleet,et al.  Computation of component image velocity from local phase information , 1990, International Journal of Computer Vision.

[5]  T.,et al.  Shiftable Multi-scale TransformsEero , 1992 .

[6]  Max A. Viergever,et al.  Comparison of edge-based and ridge-based registration of CT and MR brain images , 1996, Medical Image Anal..

[7]  Eero P. Simoncelli,et al.  A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients , 2000, International Journal of Computer Vision.

[8]  David J. Fleet,et al.  Stability of phase information , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[9]  John Daugman,et al.  Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns , 2001, International Journal of Computer Vision.

[10]  M J Ackerman,et al.  The Visible Human Project , 1998, Proc. IEEE.

[11]  Zhou Wang,et al.  Local Phase Coherence and the Perception of Blur , 2003, NIPS.

[12]  Michael Brady,et al.  Phase mutual information as a similarity measure for registration , 2005, Medical Image Anal..

[13]  Guy Marchal,et al.  Multi-modality image registration by maximization of mutual information , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[14]  Michael John Ackerman,et al.  The Visible Human Project. , 1991 .

[15]  David J. Fleet,et al.  Phase-based disparity measurement , 1991, CVGIP Image Underst..

[16]  P Kovesi,et al.  Phase congruency: A low-level image invariant , 2000, Psychological research.

[17]  Edward H. Adelson,et al.  Shiftable multiscale transforms , 1992, IEEE Trans. Inf. Theory.

[18]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.