Hybrid wavelet features for the classification of microcalcification clusters in digital mammograms

Abstract

Breast cancer is a major problem that accounts mortality rate among women, in developed and underdeveloped countries. Compared to the other developed countries, India has a lower incidence rate, but an awareness of breast cancer is imperative. Hormone-dependent cancers possibly are caused by organ chlorine. High-quality images and skilled mammographic interpretation are required to detect breast cancer at an early stage. High-quality images are mainly considered for detection of cancer in breast screening. Radiography combined with Computer Aided Diagnosis (CAD) is the most promising field for diagnosis not only of breast cancer but all types of diseases. Nevertheless, cancer is getting operated to remove the affected regions a few cancer cells remain to hide with the stem cells and starts growing after a particular period of time. Chemotherapy is advisable for avoiding excruciating pain but weakens the patient even with the first trail. A few screening programs show mammography as the best and effective way of decreasing breast cancer mortality by detecting and providing treatment at an early stage. Very little success rate has been achieved, but there are a few challenging and directions for future research in developing better enhancement and segmentation algorithms. Also, the Nanotechnologies which constitute Deoxyribonucleic acid (DNA) level treatment can eradicate cancer cells completely and reduce the pain resulting from surgery or chemotherapy. The Mammography Image Analysis Society (MIAS), an organization of United Kingdom research groups, has developed a mammography database in order to understand and analyze the images. The data for this research was taken from MIAS database which contains digitized images of normal as well as breast cancer affected mammograms. newline