A Hybrid Technique For Detection Of Microcalcification Clusters In Mammograms

The introduction of wavelets in signal and image processing has provided a new tool to create innovative and novel methods for solving problems in the areas of data compression, signal analysis, and noise removal, to name a few. Although wavelets 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 wavelet image processing 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 wavelet multiresolution analysis and synthesis is developed to assist radiologists looking for clusters of microcalcifications in digitized mammograms. Microcalcification regions may not be detectable by visual inspection or other detection techniques because of their inherent complexity. The algorithm presented in this paper successfully unmasks the complexity and limits the false positives. A thorough analysis, algorithm description and examples are shown in this paper.