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Integration of Low-Level Processing to Facilitate Microcalcification Detection

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Digital Mammography

Part of the book series: Computational Imaging and Vision ((CIVI,volume 13))

Abstract

The performance of microcalcification detection algorithms is currently not good enough for them to be used in a clinical setting. Most attempts to improve their performance consist of devising increasingly smarter high-level detection schemes. In contrast, we believe that application of low-level model-based image processing can reduce the number of false positives generated by existing detection algorithms. In this paper, we show how to identify those pixels which tend to be systematically labeled falsely as microcalcifications because of their similarity in radiological appearance to microcalcifications, namely screen-film ‘shot’ noise. In one of the most successful algorithms, Karssemeijer [4] treats such noise at the segmentation step, not in his preprocessing step which aims at making noise spectrally flat by rescaling pixel values, by defining an extra label class in his Markov random field (MRF) model.

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References

  1. Andrew Blake and Andrew Zisserman. Visual Reconstruction. M.I.T. Press, 1987.

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  2. Ralph P. Highnam and Michael Brady. Mammographic image processing (in preparation). Kluwer International, 1998.

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  3. Ralph P. Highnam, Michael Brady, and Basil J. Shepstone. The h int representation and calcifications. In Proceedings of MUIA 1997, pages 121–124, 1997.

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  4. Nico Karssemeijer. Adaptive noise equalisation and recognition of microcalcification clusters in mammograms. International Journal of Pattern Recognition and Artificial Intelligence, 7(6):1357–1375, 1993.

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  5. K.S. Woods, J.L. Solka, C.E. Priebe, W.P. Kegelmeyer, C.C. Doss, and K.W. Bowyer. Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography. International Journal of Pattern Recognition and Artificial Intelligence, December, 1993.

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© 1998 Springer Science+Business Media Dordrecht

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Poissonnier, M., Highnam, R., Brady, M., Shepstone, B., English, R. (1998). Integration of Low-Level Processing to Facilitate Microcalcification Detection. 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_29

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  • DOI: https://doi.org/10.1007/978-94-011-5318-8_29

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-6234-3

  • Online ISBN: 978-94-011-5318-8

  • eBook Packages: Springer Book Archive

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