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.
Electron microscope image stitching is highly desired to acquire microscopic resolution images of large target scenes in neuroscience. However, the result of multiple Mosaicked electron microscope images may exist severe gray scale inhomogeneity due to the instability of the electron microscope system and registration errors, which degrade the visual effect of the mosaicked EM images and aggravate the difficulty of follow-up treatment, such as automatic object recognition. Consequently, the grayscale correction method for multiple mosaicked electron microscope images is indispensable in these areas. Different from most previous grayscale correction methods, this paper designs a grayscale correction process for multiple EM images which tackles the difficulty of the multiple images monochrome correction and achieves the consistency of grayscale in the overlap regions. We adjust overall grayscale of the mosaicked images with the location and grayscale information of manual selected seed images, and then fuse local overlap regions between adjacent images using Poisson image editing. Experimental result demonstrates the effectiveness of our proposed method.