Multimodal image registration using floating regressors in the joint intensity scatter plot

This paper presents a new approach for multimodal medical image registration and compares it to normalized mutual information (NMI) and the correlation ratio (CR). Like NMI and CR, the new method’s measure of registration quality is based on the distribution of points in the joint intensity scatter plot (JISP); compact clusters indicate good registration. This method iteratively fits the JISP clusters with regressors (in the form of points and line segments), and uses those regressors to efficiently compute the next motion increment. The result is a striking, dynamic process in which the regressors float around the JISP, tracking groups of points as they contract into tight clusters. One of the method’s strengths is that it is intuitive and customizable, offering a multitude of ways to incorporate prior knowledge to guide the registration process. Moreover, the method is adaptive, and can adjust itself to fit data that does not quite match the prior model. Finally, the method is efficiently expandable to higher-dimensional scatter plots, avoiding the “curse of dimensionality” inherent in histogram-based registration methods such as MI and NMI. In two sets of experiments, a simple implementation of the new registration framework is shown to be comparable to (if not superior to) state-of-the-art implementations of NMI and CR in both accuracy and convergence robustness.