Clustered Microcalcification Detection Using Optimized Difference of Gaussians.

The objective of this thesis is to design an automated microcalcification detection system to be used as an aid in radiologic mammogram interpretation. This research proposes the following methodology for clustered microcalcification detection. First, preprocess the digitized film mammogram to reduce digitization noise. Second, spatially filter the image with a difference of Gaussians DoG kernel. To detect potential microcalcifications, segment the filtered image using global and local thresholding. Next, cluster and index these detections into regions of interest ROIs. Identify ROIs on the digitized image or hardcopy printout for final radiologic diagnosis.