Texture Recognization and Image Smoothing for Microcalcification and Mass Detection in Abnormal Region

The second most important cause of death is breast cancer in the country. In the early stages of the disease, primary treatment is difficult as its mechanisms are virtually unknown. Nonetheless, some common signatures of this disease can be used to improve early diagnostics approaches that are important for female Life quality. Mammograms of X-ray are the primary diagnostic and early diagnosis method and are the key to improving the prognosis of breast cancer examination and recovery. Good contrast and sometimes very fluidity of mass and healthy glandular tissue have been described to assist in their treatment, radiologists and internists. Many computerized diagnostics programs have been developed. The method presented in this paper is an important study of visual texture-based mammography for early-stage tumor detection. A few pictures from the digital data base were taken to screen and diagnose cancer mammograms. The suggested algorithm could be used to differentiate mass and micro calcifications by morphological operators from the context fabric and then to separate them using machine learning.