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Wavelets for Extended Depth-of-Field in Light Microscopy: Image Fusion and 3D Visualization

B. Forster, D. Van De Ville, J. Berent, N. Quack, D. Sage, M. Unser

Sixth European Conference on Mathematical and Theoretical Biology (ECMTB'05), Dresden, Federal Republic of Germany, July 18-22, 2005.



Bright-field microscopy suffers from a relatively small depth-of-field. Typically, the specimen's profile covers a range larger than the depth-of-field, and parts of the specimen that lie outside the object plane appear blurred. The specimen can be ‘scanned’ by moving the object along the optical axis, and different images will contain different areas that are sharp. The purpose of image fusion is to combine those images into one single image with an extended depth-of-field. One promising method is based on the wavelet transform. To extend the depth-of-field, we have to define an in-focus criterion. Typically, an image that is in-focus has a maximal number of visible details, whereas defocused images are blurred by the point-spread-function of the microscope. Therefore, we assume that the areas of an image that are focused contain more high-frequency components than the out-of-focus areas. Classical frequency analysis, using the Fourier transform, does not provide any spatial localization. The discrete wavelet transform DWT, by contrast, seems to be the ideal high saliency detection tool, since it allows a local analysis of the image's frequency content. The wavelet approach computes the DWT of the image slices at various focal distances, and constitutes the wavelets coefficients of the composite image by a maximum-absolute-value selection rule. The final composite image is obtained after computing the inverse DWT. We show how the wavelet-based image fusion technique can be successfully applied to obtain a sharp composite color image. The selection of the proper type of wavelets can improve the results. We also introduce a way to apply this technique for color images without introducing false colors. Finally, we show how the profile of the specimen, which we obtain as a side product from our algorithm, can be used for 3D visualization. Our algorithm is freely available at http://bigwww.epfl.ch/demo/edf/ as a plug-in for ImageJ and is used in practice by biologists at the ISREC cancer research facility in Lausanne. The algorithm can be applied to visualize and present light microscopy stacks.


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