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Breast cancer is one of the foremost relentless malignant growth found in women and it has also become a major reason of death ratio. However, the recognition of breast cancer at the premature stage is reliant on both the ability of the radiologist’s and the superiority of the images. Treatment for the breast cancer is more successful only when the detection is done at the premature stage. Screening of breast cancer is done in two conditions, first one is when diagnosed at an early stage and the second one is at the later stage with some symptoms and in each stage requires a different type of treatments. Although there are different types of techniques, among which mammography is frequently used technique for discovery of tumors in breast. Mammography is the most effective method to trace the abnormal cancer cells. Screening is one of the key factors to diminish the death rates. It is achieved through the classification algorithm which in turn identifies the severity of lymph's in the breast. During the process of classification, the similar patterns are recognized and extraction of features from the region of the image. Digital mammography is the frequently handled technique for premature detection but these images are very complex to reduce false positives. Feature performs the considerable function in the area of digital image processing. There are enormous image preprocessing techniques adopt various progressions like resizing, thresholding, binarization, normalization etc which are applied on the retrieved images. These techniques are applied to massive image processing applications like character recognition. The feature rules the activities of the image, competence in classification, the storage place and also the time utilization. The different kinds of feature extraction methods based on image processing were studied. It is also suggested for the best features extraction technique that would be applicable for application development.