Abstract

Breast cancer is the second leading cause of cancer deaths in women today. Early detection of the cancer can reduce mortality rate. Studies have shown that radiologists can miss the detection of a significant proportion of abnormalities in addition to having high rates of false positives. Pattern recognition in image processing requires the extraction of features from regions of the image and the processing of these features with a pattern recognition algorithm. We consider the feature extraction part of this processing; with a focus on the problem of micro calcification detection in digital mammography. For every pattern classification problem, the most important stage is feature extraction. The accuracy of the classification depends on the feature extraction stage. We have extracted textural, statistical and structural features which show promising results than most of the existing technology.

Keyword

Mammograms, features extraction, GLCM, Textural, Statistical features.

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