Abstract
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%.
Highlights
Mammography is the only efficient and feasible technique to detect breast cancer especially in the case of minimal tumors
1st stage classifier: In the first stage, classifier is tested for normal or abnormal images based on combined feature set of “S” matrix and the Jacobi moments features
The calculated combined feature set of training images is first trained with the SVM classifier and tested for all the images including training images for the classification
Summary
Mammography is the only efficient and feasible technique to detect breast cancer especially in the case of minimal tumors. About 30-50% of breast cancers exhibit deposits of calcium called micro calcifications. Many studies have been made on the problem of breast cancer diagnosing based on digital mammograms. The Least Square with Regularization (LSR) method to reconstruct the image and the electrodynamics sensor was used to capture the data that installed around the pipe is described Sayeed et al (2009). Development of automated image analysis methods to assist radiologists in the identification of abnormalities. ACO algorithm for load balancing in distributed systems described Ali et al (2010). It is fully distributed in which information is dynamically
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