Bilateral asymmetry identification for the early detection of breast cancer

Breast cancer is the second most common cancer overall and the leading cause of cancer deaths in women. Mammography is, at present, the only viable method for detecting most of tumors early enough for effective treatment. The secret of setting up the accurate diagnosis is to detect and understand the most subtle signs of breast lesions. Analysis of asymmetry between the left and right mammograms can provide clues about the presence of early signs of tumors. In this work we present an automated procedure for bilateral asymmetry detection composed of the following steps: (1) mammography density analysis and fibro-glandular disc detection through adaptive clustering techniques, (2) analysis and implementation of bilateral asymmetries detection algorithms based on Gabor filters analysis, (3) use of a linear Bayes classifier with the leave-one-out method to asses the asymmetry degree of the two breasts, (4) metrological evaluation of the whole system through random and systematic measurement uncertainty contributions modeling.