Mammography is the most effective tool now available for an early diagnosis of breast cancer. However, the detection of cancer signs in mammograms is a difficult task owing to the great number of non pathological structures which are also present in the image. It has been shown that in current breast cancer screenings 10percent-25percent of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for Computer Aided Detection (CADe). Probably, some causes of the false-negative screening examinations are that tumoral masses have varying dimension and irregular shape, their borders are often ill-defined and their contrast is very low, thus making difficult the discrimination from parenchymal structures. Therefore, in a CADe system a preliminary segmentation procedure has to be implemented in order to separate the mass from background tissue. In this way, various characteristics of the segmented mass can be evaluated, which may be used in a classification step to discriminate pathological and negative cases. In this paper we describe an effective algorithm for massive lesions segmentation based on region-growing technique and we provide full details of the performance evaluation procedure used in this specific context.
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