ABSTRACT
Some processing techniques cited in the literature used for microcalcifications detection in digitized mammograms are evaluated here with regard to dense breast images. Three techniques were investigated: Nappi et al.'s, Nishikawa et al.'s and Wallet et al.'s. The methods were tested with low-contrast phantom images, simulating dense breast images. The ability of each technique to detect microcalcifications in dense breast images was evaluated. The following detection rates were obtained: Nappi et al's technique, 78.3%; Wallet et al.'s, 86.6%; and Nishikawa et al.'s, 94.4%. Dense breast images affect the performance of CAD schemes, as confirmed by our results. Therefore, data from those segmentation techniques applied to dense breast images could be improved by developing a hybrid method using the best characteristics of each technique.
