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;
2000-01-01
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 imagesI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
