A Biologically Inspired Algorithm for Microcalcification Cluster Detection

Marius George Linguraru 1, 2, * Kostas Marias 1, 3 Ruth English 4 Michael Brady 1
* Auteur correspondant
1 MVL - Wolfson Medical Vision Laboratory
2 Division of Engineering and Applied Sciences [Cambridge]
3 ICS-FORTH - Institute of Computer Science [Crete]
4 Churchill Hospital [Breast Care Unit]
Abstract : The early detection of breast cancer greatly improves prognosis. One of the earliest signs of cancer is the formation of clusters of microcalcifications. We introduce a novel method for microcalcification detection based on a biologically inspired adaptive model of contrast detection. This model is used in conjunction with image filtering based on anisotropic diffusion and curvilinear structure removal using local energy and phase congruency. An important practical issue in automatic detection methods is the selection of parameters: we show that the parameter values for our algorithm can be estimated automatically from the image. This way, the method is made robust and essentially free of parameter tuning. We report results on mammograms from two databases and show that the detection performance can be improved by first including a normalisation scheme.
Keywords : X-ray mammography microcalcification clusters image normalization anisotropic diffusion human visual system
Type de document :
Article dans une revue
Medical Image Analysis, Elsevier, 2006, 10 (6), pp.850-862. 〈10.1016/j.media.2006.07.004〉
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Marius George Linguraru, Kostas Marias, Ruth English, Michael Brady. A Biologically Inspired Algorithm for Microcalcification Cluster Detection. Medical Image Analysis, Elsevier, 2006, 10 (6), pp.850-862. 〈10.1016/j.media.2006.07.004〉. 〈inria-00614995〉

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