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Improvement of SIMS image classification by means of wavelet de-noising

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Abstract

Classification is a powerful tool for the extraction of chemical information from analytical images. However, classification may sometimes not be applicable, when the clusters corresponding to single phases overlap due to high noise levels, such that they are not significantly distinguishable. We investigate the effect of the wavelet shrinkage de-noising algorithm on the subsequent classification of analytical images. By application of wavelet de-noising the distinction of phases in classification can be significantly improved.

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Authors and Affiliations

  1. Institute of Analytical Chemistry, Vienna University of Technology, Getreidemarkt 9/151, Vienna 1060, Austria, , , , , , AT

    M. Wolkenstein, H. Hutter, S. G. Nikolov & M. Grasserbauer

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  1. M. Wolkenstein
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  2. H. Hutter
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  3. S. G. Nikolov
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  4. M. Grasserbauer
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Received: 16 April 1996 / Accepted: 14 August 1996

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Wolkenstein, M., Hutter, H., Nikolov, S. et al. Improvement of SIMS image classification by means of wavelet de-noising. Fresenius J Anal Chem 357, 783–788 (1997). https://doi.org/10.1007/s002160050249

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  • DOI: https://doi.org/10.1007/s002160050249

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