Improved Elastic Registration of Low-Contrast Fluorescent Microscopy Images Using the Behaviour of Local Similarity

We introduce a mechanism for improving the result of elastic registration for simultaneously acquired low-contrast fluorescent microscopy images. B-spline basis functions are commonly used for modelling nonrigid image deformations. We present a novel approach that improves the result of B-spline based registration process. This approach monitors the behaviour of local similarity measure values during optimization in order to recognize and exclude the pixels which drive the optimization into local optima. These are low intensity pixels, mostly in background regions but also in data areas, where there is no or little useful image structure to guide registration. Such pixels play a fallacious role in the registration process due to their high frequency content and pseudorandom contribution in the overall similarity measure and its derivatives. We show that excluding such pixels during the optimization considerably improves the registration result. This approach can reduce the average registration error up to several pixels and reduce the number of iterations down to one-third of the current implementations.