Analysis of framelets for the microcalcification

Abstract

Mammography is used commonly to detect the cancer in breast at the early stage. The early stage of breast cancer detection helps in avoiding the removal of breast in women and even the death caused due to breast cancer. There are many computer aided softwares that are designed to detect breast cancer but still only biopsy method is effective in predicting the exact scenario. This biopsy technique is a painful one. To avoid this, a novel classification approach for classifying microcalcification clusters based on framelet transform is proposed. The real -time mammography images were collected from Sri Ramachandra Medical Centre, Chennai, India in order to evaluate the performance of the proposed system. The GLCM features (contrast, energy and homogeneity) are extracted from the framelet decomposed mammograms with different resolution levels and support vector machine classifier is used to classify the unknown mammograms into normal or abnormal initially and then further classifies it as benign or malignant if detected as abnormal. The result shows that framelet transform-based classification provides considerable classification accuracy.