Abstract
Early detection of breast cancer is carried out by using mammographic images. Due to low contrast nature of these images, it is difficult to detect signs such as micro calcifications and masses. This paper describes novel algorithms for early detection of breast cancer using image processing techniques. Novel algorithms are implemented for 1) Mass region extraction to get exact shape of the mass 2) Superposition of boundary of mass on mammogram helps doctors to view the boundary easily as mass region overlaps with breast parenchyma 3) Extraction of texture features like mean, standard deviation, entropy, kurtosis etc, geometric features like area perimeter L:S, ENC, (Elliptical normalized circumference) wavelet based features, so that signatures can be assigned for identification and classification of benign and malignant masses. Fourteen patients' mammograms have been processed. Features of six patients have been extracted that have masses.
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