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A digital mammogram generally detects varying degrees of breast cancer such as clustered microcalcifications, speculated lesions, circumscribed masses, ill-defined masses, and architectural distortions. Many methods of analysing digital mammograms have been recently examined and yielded varied success. Common techniques from the field of image processing have been applied to digital mammograms in an effort to locate signs of cancer sooner and more precisely than previously possible. Research suggests that computerized techniques applied/utilized by radiologists will be highly successful in analysing digital mammograms. Computerized systems that draw attention to areas of suspicion, otherwise less noticeable to radiologists have the potential to greatly increase early detection. Previously, Algorithms that effectively segment mammogram images into major sub-components and also meets the goals of efficiency and generality has been lacking. This paper reviews some methods of mammogram segmentation process for detection of masses in breast.

Survey on Segmentation Methods for Locating Masses in a Mammogram Image