  Image segmentation is a crucial step in a wide range of method image
processing systems. It is useful in visualization of the different objects
present in the image. In spite of the several methods available in the
literature, image segmentation still a challenging problem in most of image
processing applications. The challenge comes from the fuzziness of image
objects and the overlapping of the different regions. Detection of edges in an
image is a very important step towards understanding image features. There are
large numbers of edge detection operators available, each designed to be
sensitive to certain types of edges. The Quality of edge detection can be
measured from several criteria objectively. Some criteria are proposed in terms
of mathematical measurement, some of them are based on application and
implementation requirements. Since edges often occur at image locations
representing object boundaries, edge detection is extensively used in image
segmentation when images are divided into areas corresponding to different
objects. This can be used specifically for enhancing the tumor area in
mammographic images. Different methods are available for edge detection like
Roberts, Sobel, Prewitt, Canny, Log edge operators. In this paper a novel
algorithms for edge detection has been proposed for mammographic images. Breast
boundary, pectoral region and tumor location can be seen clearly by using this
method. For comparison purpose Roberts, Sobel, Prewitt, Canny, Log edge
operators are used and their results are displayed. Experimental results
demonstrate the effectiveness of the proposed approach.
