Microcalcifications are the first and smallest signs of breast cancer and some of the most predominant non-palpable breast lesions. We are developing an automated detection method to assist the clinician in the diagnosis process. Our method is based on processed normalised hi,,, images [2] and uses filters based on Gaussian derivative and anisotropic diffusion [4] to differentiate microcalcifications from breast tissue. Tests are performed on samples of real Standard Mammogram Format (SMF) images by a single-scale non-parametric algorithm with encouraging results. We detect 91.3% of the individual calcifications, whether isolated or clustered, which draws the attention of the radiologist to all the clusters.
