Researchers at the University of South Florida have devel-oped a computer aided detection and diagnosis algorithm for mammographic calcification clusters.
Mammography is the process of using low level X-rays to examine the human breast and is used as a diagnostic and screening tool. To date, mammography, along with physical breast examination, is the modality of choice for screening for early breast cancer. It is the most efficient screening method to detect early breast cancer. Cal-cium deposits that form in the breast are one of the most im-portant characteristics in the identification of cancer on mammo-grams. It is not foolproof. Many of the calcium deposits are associ-ated with benign disease. Great uncertainty in the type of the de-tected calcifications leads to unnecessary biopsies. Therefore, there is a need for an accurate method of differentiating between benign and cancerous micro-calcifications.
Our researchers have developed a computer-based system that improves diagnostic accuracy by more than fourfold compared to the current method. The program has image processing and pattern recognition techniques to classify microcalcifications as be-nign or malignant. Automatic detection, segmentation of breast calcifications, analysis of the shape and distribution of the calcifica-tions and estimation of 14 parameters using image and non -image data from the patient’s life are used.
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