An Efficient Algorithm for Point Set Registration Using Analytic Differential Approach

Image registration is an important topic in medical image analysis. It is usually used in 2D mosaics to construct the whole image of a biological specimen or in 3D reconstruction to build up the structure of an examined specimen from a series of microscopic images. Nevertheless, owing to a variety of factors, including microscopic optics, mechanisms, sensors, and manipulation, there may be great differences between the acquired image slices even if they are adjacent. The common differences include the chromatic aberration as well as the geometry discrepancy that is caused by cuts, tears, folds, and deformation. They usually make the registration problem a difficult challenge to achieve. In this paper, we propose an efficient registration method, which consists of a feature-based registration approach based on analytic robust point matching (ARPM) and a refinement procedure of the feature-based Levenberg-Marquardt algorithm (FLM), to automatically reconstruct 3D vessels of the rat brains from a series of microscopic images. The registration algorithm could speedily evaluate the spatial correspondence and geometric transformation between two point sets with different sizes. In addition, to achieve subpixel accuracy, an FLM method is used to refine the registered results. Due to the nonlinear characteristic of FLM method, it converges much faster than most other methods. We evaluate the performance of proposed method by comparing it with well-known thin-plate spline robust point matching (TPS-RPM) algorithm. The results indicate that the ARPM algorithm together with the FLMmethod is not only a robust but efficient method in image registration. key words: brain mapping, medical image registration, analytic robust point matching (ARPM), Levenberg-Marquardt algorithm, spatial correspondence, neuro-informatics