Evaluation and Selection of Morphological Procedures for Automatic Detection of Micro-calcifications in Mammography Images
In this paper, we present an evaluation of four different algorithms, based on Mathematical Morphology, to detect the occurrence of micro-calcifications in digital mammogram images from the mini-MIAS database. Results provided by TMVA produced the ranking of features that allowed discrimination between real micro-calcifications and normal tissue. ROC area measures the performance of automatic classification, which produced its highest value 0.976 for Gaussian kernel, followed by polynomial kernel, which produced 0.972. An additional parameter, called Signal Efficiency*Purity (SE*P), is proposed as a measure of the number of micro-calcifications with the lowest quantity of noise.
KeywordsMammography image mage reconstruction digital mammography micro-calcification detection Mathematical Morphology
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