Fast Automatic Registration Algorithm for Large Microscopy Images

In this paper, a framework of fast registration algorithm for large microscopy images is presented. The rationale behind this approach is that the rigid transform gives the global mapping between the two images while the nonrigid components further refines the local matching of the pixels by taking care of local nonrigid distortion and variation. Therefore, to estimate rigid transform, the global features such as specific anatomical structures need to be used instead of point features which does not contain any global information. Then to estimate local nonrigid transform, the local features such as points are used. The algorithm is divided into two stages: the first stage is to find an accurate estimate of the rigid (Euclidean) transform between the two images. To achieve this goal, high level (global) features such as small regions with anatomical meanings such as clusters of cells or blood vessels are exploited for matching purposes. A voting scheme is used to confirm the matching and compute the rigid transformation between two consecutive images. This then transforms the foundation for the second stage of nonrigid registration. Using the accurate estimate of the rigid transform, a large number of point feature correspondence is established and used as control points for the nonrigid transform