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Automatic Detection of Clustered Microcalcifications Using Morphological Reconstruction

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Digital Mammography

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

Abstract

This paper presents a method for automatic detection of clusters of microcalcifications (MCCs) in mammograms. As MCCs are small regions made up by groups of pixels brighter than their neighbours, in order to detect them the operators have to be based on the size and contrast. In mammography linear operators have been used like Laplacian-of-a-Gaussian (LoG), among others, approximated for the discrete integer approximation with the Difference-of-Gaussian (DoG) filter, as proposed in [1]. In this paper we explore operators based on non-linear theory, specifically based on mathematical morphology. This discipline has already been used in previous work [2] by means of the watershed, but in that paper the process needs user-introduced markers to reduce the oversegmentation produced by the watershed, so it is not a fully automatic detection method. In a previous work [3] we proposed to use the residue of the supremum of a number of openings each of them with a linear structuring element jointly with the Markov random field model (MRF) proposed in [4], which can eliminate the major part of the connective tissue reducing the false positive rate. In some cases this morphological operator is not enough. In this paper we studied the contribution of morphological reconstruction to improve the elimination of the connective tissue and so to reduce the false positive rate.

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References

  1. C. S. Carman and Dr. G. Eliot (1996) Detecting calcifications and calcifications clusters in digitized mammograms. In: Proceedings of International Workshop on Digital Mammography 96, pp 253–255.

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  2. U. de Mendonca Braga et al (1996) Mammographic calcification detection by mathematical morphology methods. In: Proceedings of International Workshop on Digital Mammography 96, pp 263–266.

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  3. J. M. Mossi and A. Albiol (1997) Digital Mammography: mathematical morphology applied to detection of clusters of microcalcifications. In: Proceedings of International Conference on Signal and Image Processing 97, pp 150–153.

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  4. N. Karssemeijer (1993) Adaptive noise equalization and recognition of microcalcification clusters in mammograms. International Journal of Pattern Recognition and Artificial Intelligence, 7(6), pp 1357–1376.

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  5. J. Serra (1982) Image Analysis and Mathematical Morphology. Academic Press.

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  6. L. Vincent (1993) Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms. IEEE Transactions on Image Processing, 2(2) pp 176–201.

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© 1998 Springer Science+Business Media Dordrecht

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Mossi, J.M., Albiol, A. (1998). Automatic Detection of Clustered Microcalcifications Using Morphological Reconstruction. 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_78

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  • DOI: https://doi.org/10.1007/978-94-011-5318-8_78

  • 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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