Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Efficient least squares fusion of MRI and CT images using a phase congruency model
Pattern Recognition Letters
Intensity gradient based registration and fusion of multi-modal images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Efficient global weighted least-squares translation registration in the frequency domain
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
IEEE Transactions on Signal Processing
Multi-sensor image registration based-on local phase coherence
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
High Performance Adaptive Fidelity Algorithms for Multi-Modality Optic Nerve Head Image Fusion
Journal of Signal Processing Systems
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Automatic registration of multimodal images has proven to be a difficult task. Most existing techniques have difficulty dealing with situations involving highly non-homogeneous image contrast and a small initial overlapping region between the images. This paper presents a robust multi-resolution method for regis tering multimodal images using local phase-coherence representations. The proposed method finds the transformation that minimizes the error residual between the local phase-coherence representations of the two multimodal images. The error residual can be minimized using a combination of efficient globally exhaustive optimization techniques and subpixel-level local optimization techniques to further improve robustness in situations with small initial overlap. The proposed method has been tested on various medical images acquired using different modalities and evaluated based on its registration accuracy. The results show that the proposed method is capable of achieving better accuracy than existing multimodal registration techniques when handling situations where image non-homogeneity and small overlapping regions exist.