Non-rigid Image Registration by Minimizing Weighted Residual Complexity

Background: Non-rigid registration of medical images with intensity distortions is a difficult problem due to the change in pixel intensity. It is caused by contrast agent or intensity bias field. Methods: In some cases, this problem can be solved using Residual Complexity (RC) method. However, relative modification of parameter in residual complexity would result in completely different experimental effect. Another drawback is sensitivity to noise. To handle this problem, a new intensity-based similarity measure, Weighted Residual Complexity (WRC) has been proposed for effective medical image registration in this paper. Specifically, the local entropy image of two images is computed to be aligned respectively. Then, a weighting function using a function of the local entropy difference is modeled. The weighting function is used to weight the residual image in residual complexity adaptively. The residual image is defined as the difference between reference image and warped floating image. Results: The weighting function assigns smaller weight to residual image if the corresponding pixel value is larger in local entropy difference. The proposed technique was applied to simulative and real medical images. The contrast experiments were made with mutual information, diffeomorphic demons and residual complexity. Conclusion: Also, the analysis of experimental results was made qualitatively and quantitatively, which indicates that this new approach gives a better performance than diffeomorphic demons, mutual information and residual complexity.