Research studies indicate that careful application of breast ultrasound is capable of reducing the number of unnecessary biopsies by 40% with potential cost savings of as much as $1 billion per year in the U.S. A well-defined rule-based system has been developed for scoring the Level of Suspicion (LOS) based on parameters describing the ultrasound appearance of breast lesion. Acceptance and utilization of LOS is increasing but it has proven difficult to teach the method and many radiologists have felt uncomfortable with the number of benign and malignant masses that overlap in appearance. In practice, the quality of breast ultrasound is highly operator dependent, it is often difficult to reproduce a finding and there is high variability of lesion description and assessment between radiologists. The goal of this research is to improve the uniformity and accuracy of applying the LOS scheme by automatically detecting, analyzing and comparing breast masses using sophisticated software developed for satellite imagery applications. The aim is to reduce biopsies on the masses with lower levels of suspicion, rather that increasing the accuracy of diagnosis of cancers, which will require biopsy anyway. In this paper we present our approach to develop a system to process, segment, analyze and classify medical images based on information content. A feasibility study was completed in a digital database of biopsy-proven image files from 46 women retrieved chronologically from our image library. Segmentation and classification were sufficiently accurate to correctly group all benign cystic masses, all benign solid masses and all solid malignant masses. The image analysis, computer-aided detection and image classification software system Image Companion developed by Almen Laboratories, Inc. was used to achieve the presented results.