Feature based automatic stitching of microscopic images

Mosaicing of microscopic images is often necessary when the observed specimen cannot be captured into a single image. Automatic method is preferred because it will greatly reduce the work involved. In the paper, we present a feature based automatic mosaicing method based on the related research on panorama reconstruction for photography. Scale invariant feature transform (SIFT) is first applied to extract robust features from the images, and by careful implementation of Best-Bin-First (BBF) algorithm, we construct the global kd-Tree from all the features and search for the possible overlapping image pairs efficiently. Random sample consensus (RANSAC) is chosen to further verify the matches. And once the image pairs are all validated, minimum spanning tree (MST) is used to obtain the best connected-component of the image set to recover the transformation between images and project them into the mosaic frame. Our experiment results show that the approach is robust to background noises and illumination change in the images and can give reliable and accurate results even for images of low overlapping or with relatively few features.