Abstract
In the current world, breast cancer is a fatal disease that is causing women mortality at a larger rate. The central idea in preventing this fatal disease is by early diagnosis through the use of computer aided mammography, which can be accomplished by developing intelligent algorithms and software techniques to process these mammography images automatically, and eventually extract information assisting the physicians in detecting and diagnosing the abnormal growth of the cancer cells. The current research work is an effort in the aforesaid direction, where automatic breast cancer detection is made feasible at early stages, with the image processing technique of segmentation and breast density estimation through mammographic images using the well-known Watershed Algorithm. The watershed transformation is a well-known technique for contour based feature extraction and region segmentation, which provided us the motivation to explore it further for assisting the detection of breast cancer. The proposed technique is tested with publicly available MIAS dataset, and accuracy thus obtained is comparable with the state-of-the-art techniques available in the literature with improved computational efficiency.
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Nayak, T., Bhat, N., Bhat, V., Shetty, S., Javed, M., Nagabhushan, P. (2019). Automatic Segmentation and Breast Density Estimation for Cancer Detection Using an Efficient Watershed Algorithm. In: Nagabhushan, P., Guru, D., Shekar, B., Kumar, Y. (eds) Data Analytics and Learning. Lecture Notes in Networks and Systems, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-2514-4_29
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DOI: https://doi.org/10.1007/978-981-13-2514-4_29
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