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Morphological-based microcalcification detection using adaptive thresholding and structural similarity indices

Abstract : In this paper, we propose a new morphological-based method for automatic detection of microcalcifications in digitized mammograms. It uses various structuring elements to deal with the diversity of microcalcification characteristics. The obtained morphological maps are converted to a continuous suspicion map (SM) based on the structural similarity index (SSIM). This new semantic representation map is then locally analyzed, using superpixels, to automatically estimate adaptive threshold values and finally identify potential microcalcification areas. The proposed method was evaluated using the publicly-available INBreast database. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to some state-of-the-art methods.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-02478203
Contributor : Pierre-Henri Conze <>
Submitted on : Thursday, February 13, 2020 - 5:50:06 PM
Last modification on : Thursday, April 15, 2021 - 12:06:01 PM

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Asma Touil, Karim Kalti, Pierre-Henri Conze, Basel Solaiman, Mohamed Ali Mahjoub. Morphological-based microcalcification detection using adaptive thresholding and structural similarity indices. ATSIP 2020 : 5th International Conference on Advanced Technologies for Signal & Image Processing, Mar 2020, Sousse, Tunisia. ⟨10.1109/ATSIP49331.2020.9231731⟩. ⟨hal-02478203⟩

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