Abstract
Mammography is one of the renowned techniques for detection of breast cancer in medical domain. The detection rate and accuracy of breast cancer in mammogram depend on the accuracy of image segmentation and the quality of mammogram images. Most of existing mammogram detection techniques suffer from exact continuous boundary detection and estimate amount of affected area. We propose an algorithm for the detection of deformities in mammographic images that using Gaussian probabilistic approach with Maximum likelihood estimation (MLE), statistical measures for the classify of image region, post processing by morphological operations and Freeman Chain Codes for contour detection. For these detected areas of abnormalities, compactness are evaluated on segmented mammographic images. The validation of the proposed method is established by using mammogram images from different databases. From experimental results of the proposed method we can claim the superiority over other usual methods.
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Biswas, B., Choudhuri, R., Dey, K.N. (2016). Automated Segmentation Scheme Based on Probabilistic Method and Active Contour Model for Breast Cancer Detection. In: Nagar, A., Mohapatra, D., Chaki, N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 43. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2538-6_57
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DOI: https://doi.org/10.1007/978-81-322-2538-6_57
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