Wavelet-based 2D Multifractal Spectrum with Applications in Analysis of Digital Mammography Images
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Breast cancer is the second leading cause of death in women in the United States and at present, mammography is the only proven method that can detect minimal breast cancer. On the other hand, many medical images demonstrate a certain degree of self-similarity over a range of scales. The Multifractal spectrum (MFS) summarizes possibly variable degrees of scaling in one dimensional signals and has been widely used in fractal analysis. In this work, we develop a generalization to two dimensions of MFS and use dynamics of the scaling as discriminatory descriptors to do classification of mammographic images to benign and malignant. Methodology we propose was tested using images from the University of South Florida Digital Database for Screening Mammography (DDSM) (Heat et al. ).