Multi-modal Image Registration for Correlative Microscopy
This addresses a challenging multi-modal registration problem in microscopy, but it appears incremental as it builds on existing techniques like fiducials and image analogies.
The paper tackles multi-modal image registration for correlative microscopy by introducing two methods: one using fiducials with least-squares refinement and another based on sparse representation to convert multi-modal to mono-modal registration, tested on TEM and confocal microscopy images.
Correlative microscopy is a methodology combining the functionality of light microscopy with the high resolution of electron microscopy and other microscopy technologies. Image registration for correlative microscopy is quite challenging because it is a multi-modal, multi-scale and multi-dimensional registration problem. In this report, I introduce two methods of image registration for correlative microscopy. The first method is based on fiducials (beads). I generate landmarks from the fiducials and compute the similarity transformation matrix based on three pairs of nearest corresponding landmarks. A least-squares matching process is applied afterwards to further refine the registration. The second method is inspired by the image analogies approach. I introduce the sparse representation model into image analogies. I first train representative image patches (dictionaries) for pre-registered datasets from two different modalities, and then I use the sparse coding technique to transfer a given image to a predicted image from one modality to another based on the learned dictionaries. The final image registration is between the predicted image and the original image corresponding to the given image in the different modality. The method transforms a multi-modal registration problem to a mono-modal one. I test my approaches on Transmission Electron Microscopy (TEM) and confocal microscopy images. Experimental results of the methods are also shown in this report.