Registration for Correlative Microscopy Using Image Analogies
Correlative microscopy is a methodology combining the functionality of light microscopy with the high resolution of electron microscopy and other microscopy technologies for the same biological specimen. In this paper, we propose an image registration method for correlative microscopy, which is challenging due to the distinct appearance of biological structures when imaged with different modalities. Our method is based on image analogies and allows to transform images of a given modality into the appearance-space of another modality. Hence, the registration between two different types of microscopy images can be transformed to a mono-modality image registration. We use a sparse representation model to obtain image analogies. The method makes use of representative corresponding image training patches of two different imaging modalities to learn a dictionary capturing appearance relations. We test our approach on backscattered electron (BSE) Scanning Electron Microscopy (SEM)/confocal and Transmission Electron Microscopy (TEM)/confocal images and show improvements over direct registration using a mutual-information similarity measure to account for differences in image appearance.
KeywordsMutual Information Image Registration Compress Sensing Confocal Image Image Analogy
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- 1.Caplan, J., Niethammer, M., Taylor II, R.M., Czymmek, K.J.: The power of correlative microscopy: multi-modal, multi-scale, multi-dimensional. Current Opinion in Structural Biology (2011)Google Scholar
- 2.Fronczek, D., Quammen, C., Wang, H., Kisker, C., Superfine, R., Taylor, R., Erie, D.A., Tessmer, I.: High accuracy FIONA-AFM hybrid imaging. Ultramicroscopy (2011)Google Scholar
- 4.Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: The 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 327–340. ACM (2001)Google Scholar
- 7.Elad, M.: Sparse and redundant representations: from theory to applications in signal and image processing. Springer (2010)Google Scholar
- 11.Combettes, P.L., Pesquet, J.C.: Proximal splitting methods in signal processing. In: Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp. 185–212. Springer (2011)Google Scholar