At present, mammography is the only non-invasive diagnostic technique of breast cancer at a very early stage. A visual clue of such disease particularly significant is the presence of clusters of microcalcifications. Reliable methods for an automatic recognition of malignant clusters are very difficult to accomplish because of the small size of the microcalcifications and the poor quality of the mammographic images. In this paper we propose a novel approach for automating the recognition of malignant clusters, based on the adoption of a multiple expert system. The approach has been successfully tested on a standard database of 40 mammographic images

Combining experts with different features for classifying clustered microcalcifications in mammograms

TORTORELLA, Francesco;
2000

Abstract

At present, mammography is the only non-invasive diagnostic technique of breast cancer at a very early stage. A visual clue of such disease particularly significant is the presence of clusters of microcalcifications. Reliable methods for an automatic recognition of malignant clusters are very difficult to accomplish because of the small size of the microcalcifications and the poor quality of the mammographic images. In this paper we propose a novel approach for automating the recognition of malignant clusters, based on the adoption of a multiple expert system. The approach has been successfully tested on a standard database of 40 mammographic images
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11580/21246
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