Electron Microscopic Sequential Images Stitching Based on Belief Propagation

The precise stitching of microscopic images of large-scale biological sequence slices is of great significance for the study of biological structure and function, but the slight scale changes of microscopic images and the blank areas in the images seriously affect the accuracy of mosaic. In this paper, we propose a electron microscope sequence image stitching based on belief propagation algorithm, which basically solves this problem. Firstly, the relative scale of adjacent images is calculated by extracting the sift feature points of the images. Then the global optimization model is used to obtain the absolute scale of each image, and the image is scaled to obtain the microscopic image with consistent scale. Secondly, obtain the relative displacement relationship of adjacent images by template matching method, and then the global positions of all images are optimized by Belief Propagation (BP) algorithm to eliminate the influence of blank regions and repetitive structures on the stitching results. In the case study, the proposed method demonstrates high quality.