The early detection of breast cancer is essential for increasing the survival rate of the disease. Today, mammography is the only breast screening technique capable of detecting breast cancer at a very early stage. The presence of a breast tumor is indicated by some features on the mammogram. One sign of malignancy is the presence of clusters of fine, granular microcalcifications. We present here a three-step method for detecting and characterizing these microcalcifications. We begin with the detection of potential candidates. The aim of this first step is to detect all the pixels that could be a microcalcification. Then we focus on our specific region growing technique which provides an accurate extraction of the shape of the region corresponding to each detected growing technique which provides an accurate extraction of the shape of the region corresponding to each detected seed. This second step is essential because microcalcifications shape is a very important feature for the diagnosis. It is then possible to determine precise parameters to characterize these microcalcifications. This three-step method has been evaluated on a set of images form the mammographic image analysis society database.

Microcalcifications are an important sign for breast cancer diagnosis. Here the authors propose a three steps approach for microcalcifications detection and characterization. Firstly, a new and efficient non-linear filter, based on the global prior of the microcalcifications' shape, is presented. This filter limits the false detections due to noise while providing seeds representative of suspicious regions. In a second step a precise segmentation of the suspicious regions is provided by a region growing technique initialized from the previously detected seeds. In a last step, the individual potential microcalcifications are characterized by a set of features and grouped in clusters. This step separates the false detections from the true ones, on the basis of the high level prior of the microcalcifications, and provides quantitative information useful for the physicians' diagnosis. This global approach has been tested and evaluated on mammograms from the MIAS database, representative of different pathologies and breast tissue structures.