Detection of Microcalcification Clusters in Mammograms Using a Difference of Optimized Gaussian Filters

Abstract

Since microcalcification clusters are primary indicators of malignant types of breast cancer, its detection is important to prevent and treat the disease. This paper proposes a method for detection of microcalcification clusters in mammograms using sequential Difference of Gaussian filters (DoG). In a first stage, fifteen DoG filters are applied sequentially to extract the potential regions, and later, these regions are classified using the following features: absolute contrast, standard deviation of the gray level of the microcalcification and a moment of contour sequence (asymmetry coefficient). Once the microcalcifications are detected, two approaches for clustering are compared. In the first one, several microcalcification clusters are detected in each mammogram. In the other, all microcalcifications are considered in a single cluster. We demonstrate that the diagnosis based on the detection of several microcalcification clusters in a mammogram is more efficient than considering a single cluster including all the microcalcifications in the image.