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A new conditional region growing approach for an accurate detection of microcalcifications from mammographic images

Abstract : In this paper, we propose a new Conditional Region Growing (CRG) approach with the ability of finding the accurate MC boundaries starting from selected seed points. The starting seed points are determined based on regional maxima detection and superpixel analysis. The region growing step is controlled by a set of criteria derived from prior knowledge to characterize MCs. The key feature is to highlight below each MC to estimate the appropriate criteria and not to use the same parameters for all of them. Defined criteria can be divided into two categories. The first one concerns the neighbourhood searching size. The second one deals with the gradient information and shape evolution within the growing process. Experimental results show the benefits of used criteria in terms of improving the MC delineation qualities.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-02915012
Contributor : Pierre-Henri Conze <>
Submitted on : Thursday, August 13, 2020 - 11:37:23 AM
Last modification on : Wednesday, July 21, 2021 - 7:42:02 AM

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Asma Touil, Pierre-Henri Conze, Karim Kalti, Basel Solaiman, Mohamed Ali Mahjoub. A new conditional region growing approach for an accurate detection of microcalcifications from mammographic images. BIBE 2020 : IEEE 20th International Conference on Bioinformatics And Bioengineering, Oct 2020, Cincinnati, United States. ⟨10.1109/BIBE50027.2020.00132⟩. ⟨hal-02915012⟩

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