Classification of Microcalcification Using Dual-Tree Complex Wavelet Transform and Support Vector Machine

Breast cancer is reported to be the second deadliest cancer among cancerous woman. Statistics show that the case of breast cancer in the world is increasing every year. By analyzing a mammogram, pathologists could detect the presence of micro calcification in ones breast. However, micro calcification could be classified into benign and malignant. The later indicates the presence of cancer. Computer-Aided Diagnosis (CADx) designed to help phatologists determine the type of micro calcification in a mammogram. Usually, it's consist of two steps, feature extraction and classification. In our methodology, we proposed the use of dual-tree complex wavelet transform (DT CWT) as feature extraction technique and support vector machine (SVM) as classifier. Using this methodology, our experimental result achieved good classification accuracy. However, some of the previous researches have shown better results than ours.