This paper presents the results of a pilot study whose primary objective was to further substantiate the efficacy of front- end data reduction in computer-aided diagnosis (CAD) of mammograms. This concept is realized by a preprocessing module that can be utilized at the front-end of most mammographic CAD systems. Based on fractal encoding, this module takes a mammographic image as its input and generates, as its output, a collection of subregions called focus-of-attention regions (FARs). These FARs contain all structures in the input image that appear to be different from the normal background tissue. Subsequently, the CAD systems need only to process the presented FARs, rather than the entire input image. This accomplishes two objectives simultaneously: (1) an increase in throughput via a reduction in the input data, and (2) a reduction in false detections by limiting the scope of the detection algorithms to FARs only. The pilot study consisted of using the preprocessing module to analyze 80 mammographic images. The results were an average data reduction of 83% over all 80 images and an average false detection reduction of 86%. Furthermore, out of a total of 507 marked microcalcifications, 467 fell within FARs, representing a coverage rate of 92%.