Algorithms for detecting clusters of microcalcifications in mammograms

Authors:
Claudio Marrocco;Mario Molinara;Francesco Tortorella
Affiliations:
Dipartimento di Automazione, Elettromagnetismo, Ingegneria dell'Informazione e Matematica Industriale, Università degli Studi di Cassino, Cassino, (FR), Italy;Dipartimento di Automazione, Elettromagnetismo, Ingegneria dell'Informazione e Matematica Industriale, Università degli Studi di Cassino, Cassino, (FR), Italy;Dipartimento di Automazione, Elettromagnetismo, Ingegneria dell'Informazione e Matematica Industriale, Università degli Studi di Cassino, Cassino, (FR), Italy
Venue:
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Year:
2005

Citing 3
Cited 3

Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Detection of Microcalcifications Clusters in Mammograms through TS-MRF Segmentation and SVM-based Classification

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
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Pattern Recognition, Third Edition

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Detecting clusters of microcalcifications with a cascade-based approach

IWDM'12 Proceedings of the 11th international conference on Breast Imaging

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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 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 method 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 different clustering algorithms which produce the final decision. The approach has been successfully tested on a standard database of 40 mammographic images, publicly available.