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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