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Digitization Regime as a Cause for Variation in Algorithm Performance Across Two Mammogram Databases
Digitization Regime as a Cause for Variation in Algorithm Performance Across Two Mammogram Databases
Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Computer assisted diagnosis of microcalcifications in mammograms: a scale-space approach
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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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.