Abstract
Our approach to X-ray digital image enhancement was based on entropy maximization, which allows distributions to be estimated in cases when incomplete or corrupt information is only available. In data analysis, maximum entropy (ME) techniques are generally used to reconstruct positive distributions, such as images and spectra, from blurred or noisy data. Within this framework, positive distributions ought to be assigned probabilities which are based on the entropy of these distributions. If we consider a complete collection of images corresponding to all possible intensity distributions, then measurements act as a filter over the collection by restricting our attention to the images that satisfy the data with noise. Among these, a natural choice may be the one that could have arisen in the maximum number of ways, depending on our counting rule.
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© 1998 Springer Science+Business Media Dordrecht
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Mutihac, R., Colavita, A.A., Cicuttin, A., Cerdeira, A.E. (1998). Maximum Entropy Improvement of X-Ray Digital Mammograms. In: Karssemeijer, N., Thijssen, M., Hendriks, J., van Erning, L. (eds) Digital Mammography. Computational Imaging and Vision, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5318-8_54
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DOI: https://doi.org/10.1007/978-94-011-5318-8_54
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