Microcalcification cluster detection in digitized mammograms using multiscale techniques

The introduction of multiscale techniques to signal and image processing has provided a new tool to create innovative methods for solving problems in the areas of data compression, signal analysis, and noise removal. Although these techniques are popular and used extensively in research and in engineering applications, their use in signature detection and classification is still an area open to extensive investigation. This paper discusses multiscale techniques working in synergy with other processing techniques to detect and recognize abnormal and cueing signatures that are important to diagnostic medicine-detection and recognition of microcalcification clusters in mammograms. In this application, an innovative detection algorithm that takes advantage of multiresolution analysis and synthesis is developed to assist radiologists looking for clusters of microcalcifications in digitized mammograms. The algorithm presented in this paper successfully limits the false positives. An algorithm description and examples are shown in this paper.