Fuzzy sectorization in knowledge discovery of digital mammograms

Medical images contain a vast amount of information that, if effectively analyzed, could lead to the early detection and diagnosis of abnormalities. Often medical images of high resolution are stored on computers and therefore require a large amount of disk space. In recent years, there have been extensive interest and research in the development of effective and efficient methods of extracting patterns and features from medical images. Knowledge discovery tools can be used to efficiently analyze large data, but often a data reduction technique is required to obtain a manageable size data. In this research we used wavelet lifting schemes for data reduction and several shape histogram-inspired data sectorization techniques for knowledge discovery and analyses.