Wavelet Analysis and Orthogonal Moments based Classification of Microcalcification in Digital Mammograms
Problem statement: An important early sign of breast cancer is the clusters of micro calcifications in digital mammograms. To assist radiologists in the diagnosis of mammographic clusters a novel hybrid algorithm has been developed. Approach: A method, for detecting micro calcification in mammograms based on combined feature set with Ant Colony Optimization (ACO) was proposed. The diagonal matrix ‘S’ obtained from the Singular Value Decomposition (SVD) of LL band of wavelet transform was used as one of the feature set for classification of mammogram. A new approach for detecting micro calcifications in mammograms employing Jacobi Moments was proposed. The set of Jacobi polynomials were orthogonal and this ensured minimal information redundancy between the moments. Results and Conclusion: Jacobi moments include the properties of well-known Zernike, Legendre and Tchebichef moments. Ant Colony Optimization (ACO) was used for reducing the Jacobi feature set dimensionality through selecting a subset of features that performed well in the classification phase. The selected Jacobi feature set were combined with ‘S’ matrix to achieve the better classification rate of over 90%.
Copyright: © 2011 N. V.S. Sree Rathna Lakshmi and C. Manoharan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Jacobi polynomials
- Ant Colony Optimization (ACO)
- Singular Value Decomposition (SVD)
- pixel groupings