Compression of Digital Mammograms Using Wavelets and Fuzzy Algorithms for Learning Vector Quantization
Nicolaos B. Karayiannis, Ted C. Wang · Studies in fuzziness and soft computing · 2000
This chapter presents the development and evaluates the performance of an image compression system designed for digital mammograms using wavelet image decomposition and vector quantization. In digital mammograms, important diagnostic features such as the microcalcifications appear in small clusters of few pixels with relatively high intensity compared with their neighboring pixels. These image features can be preserved by a compression scheme employing a suitable image transform which can localize the signal characteristics in the original and the transform domain. Image compression is achieved by using wavelet filters to decompose digital mammograms into subbands carrying different frequencies. The resulting subbands are then encoded using vector quantization. Vector quantization is based on multiresolution codebooks designed by the Linde-Buzo-Gray (LBG) algorithm and a family of fuzzy algorithms for learning vector quantization (FALVQ). The experimental results confirm the potential of the compression system described in this chapter for use in digital mammography and also provide the basis for evaluating the effect of the main design components of such a system on image quality.