Abstract: Mammography is specialized medical imaging for scanning the breasts. A mammography exam (A Mammogram) helps in the early detection and diagnosis of breast cancer. Mammogram image segmentation is useful in detecting the breast cancer regions, hence, better diagnosis. In this paper, we applied enhanced double thresholding-based approach for Mammograms’ image segmentation. Moreover, we added the borders of the final segmented image as a contour to the original image helping physicians to easily detect the breast cancer into different Mammograms. The result is enhanced wise effect onto breast cancer qualitative detection into Mammograms, helping physicians for better diagnosis. Generalization for our study is possible for not only x-ray based Mammograms, but also for all biomedical images, as an enhanced segmentation way for better visualization, detection, and feature extraction, thus better diagnosis. Moreover, this manual thresholding method has the advantage of not only reducing processing time but also the processing storage area.

Samir M. Badawy, Alaa A. Hefnawy, Hassan E. Zidan and Mohammed T. GadAllah, “Breast Cancer Detection with Mammogram Segmentation: A Qualitative Study” International Journal of Advanced Computer Science and Applications(IJACSA), 8(10), 2017. http://dx.doi.org/10.14569/IJACSA.2017.081016

@article{Badawy2017,
title = {Breast Cancer Detection with Mammogram Segmentation: A Qualitative Study},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.081016},
url = {http://dx.doi.org/10.14569/IJACSA.2017.081016},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {10},
author = {Samir M. Badawy and Alaa A. Hefnawy and Hassan E. Zidan and Mohammed T. GadAllah}
}