Identification of abnormility from digital mammogram to detect breast cancer

The breast cancer is diagnosed using many ways for past two decades. The Studies have proved that the early detection of cancer will increase the life span of the patients. The breast cancer detection requires double reading of mammogram by radiologist, hence the radiologist need to have support from CAD which includes different image processing techniques. We are in urge to improve the CAD systems that detects the abnormalities such as micro calcification, mass, etc. than existing. Firstly, This paper focus on the preprocessing which removes noise from the mammogram and it is followed by segmentation of the image which helps to partition the image and to identify the abnormalities which could cause cancer. The segmentation is made by OTSU´s method which helps us further to classify the abnormalities from the normal.