Mass detection in mammograms using gabor filters and fuzzy clustering

Authors:
M. Santoro;R. Prevete;L. Cavallo;E. Catanzariti
Affiliations:
Department of Physical Sciences, University of Naples Federico II, INFN, Section of Naples;Department of Physical Sciences, University of Naples Federico II, INFN, Section of Naples;Department of Physical Sciences, University of Naples Federico II, INFN, Section of Naples;Department of Physical Sciences, University of Naples Federico II, INFN, Section of Naples
Venue:
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Year:
2005

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Abstract

In this paper we describe a new segmentation scheme to detect masses in breast radiographs. Our segmentation method relies on the well known fuzzy c-means unsupervised clustering technique using an image representation scheme based on the local power spectrum obtained by a bank of Gabor filters. We tested our method on 200 mammograms from the CALMA database. The detected regions have been validated by comparing them with the radiologist's hand-sketched boundaries of real masses. The results, evaluated using ROC curve methodology, show that the greater flexibility and effectiveness provided by the fuzzy clustering approach benefit from an image representation that combine both intensity and local frequency information.