Clustering microcalcifications techniques in digital mammograms Available to Purchase

Breast cancer has become a serious public health problem around the world. However, this pathology can be treated if it is detected in early stages. This task is achieved by a radiologist, who should read a large amount of mammograms per day, either for a screening or diagnostic purpose in mammography. However human factors could affect the diagnosis. Computer Aided Detection is an automatic system, which can help to specialists in the detection of possible signs of malignancy in mammograms. Microcalcifications play an important role in early detection, so we focused on their study. The two mammographic features that indicate the microcalcifications could be probably malignant are small size and clustered distribution. We worked with density techniques for automatic clustering, and we applied them on a mammography CAD prototype developed at INFN‐Turin, Italy. An improvement of performance is achieved analyzing images from a Perugia‐Assisi Hospital, in Italy.