Image Registration of Medical Images Using Ripplet Transform

For image fusion of geometrically distorted images, registration is the prerequisite step. Intensity-based image registration methods are preferred due to higher accuracy than that of feature-based methods. But, perfect registered image using intensity based method leads towards improvements in computational complexity. Conventional transform like wavelet transform based image registration reduces the computational complexity, but suffers from discontinuities such as curved edges in the medical images. In this paper, a new registration algorithm is proposed that uses the approximate-level coefficients of the ripplet transform, which allows arbitrary support and degree as compared to curvelet transform. The entropy-based objective function is developed for registration using ripplet coefficients of the images. The computations are carried out with 6 sets of CT and MRI brain images to validate the performance of the proposed registration technique. The quantitative approach such as standard deviation, mutual information, peak signal to noise ratio and root mean square error are used as performance measure.