AN ENHANCED MAMMOGRAM DIAGNOSIS USING SHIFT-INVARIANT TRANSFORM

Breast cancer is a common disease for women and various techniques have been used to detect the breast cancer. The mammogram images are noise, low contrast and blur due to limitations of the X-ray hardware system. So, we should enhance the mammogram images for radiologist observation. To attain this, we strongly recognize that the digital mammography is a truthful technique with a new method and also it can easily identify the breast cancer at the very early stage before any symptoms are shown. In this paper, we propose NonSubsampled Contourlet Transform (NSCT) method for enhancing the mammogram images and the comparison between 2-D HAAR Discrete Wavelet Transform and Contourlet Transform. The NSCT extracts the shift-invariant multi-scale, multi-direction and the geometric information of mammogram images which is used to distinguish noise from weak edges than existing transformations.