Detection of Mammographic Microcalcifications Using a Statistical Model

Breast cancer is the leading cause of early mortality in women [1]. Reseach has shown that radiologists involved in screening mammograms for signs of early breast cancer can be aided by the provision of prompts to direct their attention towards potential abnormalities. In order for prompting to be successful in improving detection performance, the error rates of prompt generation algorithms must be strictly controlled [2]. Almost half of clinically occult breast cancers are due to the presence of microcalcifications [3]. In this paper, a new method is proposed to achieve the automatic detection of microcalcifications. A directional recursive median filtering (DRMF) technique at various scales and orientations is applied to the mammograms to obtain signatures at a pixel level which are characteristic of the local greylevel distribution [2], [4]. We have developed a Principal Component Analysis (PCA) statistical model based on the signatures [2], [4] which can be used for the detection of microcalcifications. A Receiver Operating Characteristic (ROC) study based on pixel classification is provided and the results are compared with approaches published in the literature [5], [6].