A fractal approach to the segmentation of microcalcifications in digital mammograms.

This paper presents a computerized method for the automated segmentation of individual microcalcifications in a region of interest (ROI) known to contain a cluster in digital mammograms. Mammographic parenchyma caj be accurately modeled with the fractal approach, but not areas with microcalcifications. The digitized image is divided into 16 x 16-pixel overlapping windows and those accurately modeled by the fractal model are eliminated. The next steps include local thresholding of the ROIs using an iterative method, the elimination of some of the artifacts and identification of the clustered microcalcifications using a clustering algorithm. The evaluation was performed on 81 simulated clusters superimposed on normal mammographic backgrounds and on a representative database of 408 real mammograms. Microcalcification locations were identified by two radiologists independently. These locations were compared to those found by the computer algorithm. An average of 59% of the simulated microcalcifications and 69% of the microcalcifications common to both radiologists were detected. The algorithm described provides a fully automated method for the segmentation of individual microcalcifications in an area of the mammogram known to contain a cluster.