Mammography is a not invasive diagnostic technique widely used for early cancer detection in women breast. A particularly significant clue of such disease is the presence of clusters of microcalcifications. The automatic detection and classification of such clusters is a very difficult task because of the small size of the microcalcifications and of the poor quality of the digital mammograms. In literature, all the proposed methods for the automatic detection focus on the single microcalcification. In this paper, an approach that moves the final decision on the regions identified by the segmentation in the phase of clustering is proposed. To this aim, the output of a classifier on the single microcalcifications is used as input data in a clustering algorithms which produce the detected clusters. As final output the system highlights the suspicious clusters, leaving to the specialist the diagnosis responsibility. The approach has been successfully tested on a standard database of 40 mammographic images, publicly available.

Detection of Clusters of Microcalcifications in Mammograms: A Multi Classifier Approach

D'ELIA, Ciro;MARROCCO, Claudio;MOLINARA, Mario;TORTORELLA, Francesco
2008

Abstract

Mammography is a not invasive diagnostic technique widely used for early cancer detection in women breast. A particularly significant clue of such disease is the presence of clusters of microcalcifications. The automatic detection and classification of such clusters is a very difficult task because of the small size of the microcalcifications and of the poor quality of the digital mammograms. In literature, all the proposed methods for the automatic detection focus on the single microcalcification. In this paper, an approach that moves the final decision on the regions identified by the segmentation in the phase of clustering is proposed. To this aim, the output of a classifier on the single microcalcifications is used as input data in a clustering algorithms which produce the detected clusters. As final output the system highlights the suspicious clusters, leaving to the specialist the diagnosis responsibility. The approach has been successfully tested on a standard database of 40 mammographic images, publicly available.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11580/10784
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