Abstract
In this work, we are dealing with the detection of microcalcifications. In order to do so, we propose to model the grey levels of digital mammograms by the sum of a surface trend and an additive noise or texture. The grey level intensities image is then considered as a surface which is approximated into a class of smooth functions (the trend) and the noise represents the fluctuations around the grey levels average values. Following [1], the trend estimation is done with bicubic spline functions, meaning that the considered class of smooth functions is spanned by piecewise polynomials of degree three and of class C 2 [2], [3].
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Maitournam, A., Graffigne, C., Strauss, A. (1998). Modeling of Digital Mammograms Using Bicubic Spline Functions and Additive Noise. 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_28
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DOI: https://doi.org/10.1007/978-94-011-5318-8_28
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