CLASSIFICATION OF MICROCALCIFICATION BASED ON WAVE ATOM TRANSFORM

Abstract

Breast cancer is a serious problem for women in the world. It is the most common form of cancer diagnosed in woman, with one in nine women expected to be diagnosed with some form of cancer in their life-time. It is second only to lung cancer in cancer related deaths. Statistics shows that the case of breast cancer in the world is increasing every year. By analyzing a mammogram, pathologists could detect the presence of microcalcification in ones breast. However, microcalcification can 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 microcalcification in a mammogram. Usually, it's consisting of two steps, feature extraction and classification. In our methodology, we proposed the use of Wave Atom Transform (WAT) 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.