A graph-based method for detecting and classifying clusters in mammographic images

Authors:
P. Foggia;M. Guerriero;G. Percannella;C. Sansone;F. Tufano;M. Vento
Affiliations:
Dipartimento di Informatica e Sistemistica, Università di Napoli “Federico II”, Napoli, Italy;Dipartimento di Ingegneria dell’Informazione e di Ingegneria Elettrica, Università di Salerno, Fisciano (SA), Italy;Dipartimento di Ingegneria dell’Informazione e di Ingegneria Elettrica, Università di Salerno, Fisciano (SA), Italy;Dipartimento di Informatica e Sistemistica, Università di Napoli “Federico II”, Napoli, Italy;Dipartimento di Ingegneria dell’Informazione e di Ingegneria Elettrica, Università di Salerno, Fisciano (SA), Italy;Dipartimento di Ingegneria dell’Informazione e di Ingegneria Elettrica, Università di Salerno, Fisciano (SA), Italy
Venue:
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2006

Citing 5
Cited 0

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Abstract

In this paper we propose a method based on a graph-theoretical cluster analysis for automatically finding and classifying clusters of microcalcifications in mammographic images, starting from the output of a microcalcification detection phase. This method does not require the user to provide either the expected number of clusters or any threshold values, often with no clear physical meaning, as other algorithms do. The proposed approach has been tested on a standard database of 40 mammographic images and has demonstrated to be very effective, even when the detection phase gives rise to several false positives.