Contrast enhancement in dense breast images to aid clustered microcalcifications detection.

This paper presents a method to provide contrast enhancement in dense breast digitized images, which are difficult cases in testing of computer-aided diagnosis (CAD) schemes. Three techniques were developed, and data from each method were combined to provide a better result in relation to detection of clustered microcalcifications. Results obtained during the tests indicated that, by combining all the developed techniques, it is possible to improve the performance of a processing scheme designed to detect microcalcification clusters. It also allows operators to distinguish some of these structures in low-contrast images, which were not detected via conventional processing before the contrast enhancement. This investigation shows the possibility of improving CAD schemes for better detection of microcalcifications in dense breast images.

A segmentation technique to detect masses in dense breast digitized mammograms.

This article describes a segmentation technique that uses the watershed transformation to provide mass detection in dense breast digitized images. The technique consists of four steps: preprocessing, which isolates the breast from the image background; histogram equalization to enhance the contrast; watershed transformation, which calculates the gradient for the whole image; and a topography technique that organizes the segmented parts. We processed 109 regions of interest extracted from mammograms. Preliminary results showed 20% false positive and 85.4% true positive detection regarding the equalized images containing masses. Good results were obtained with this technique. However, additional techniques should be developed to eliminate noise in some images. This technique could be an important tool in mammography.