Comparison of Gradient-Based Edge Detectors Applied on Mammograms
Breast cancer is one of the most common types of cancer amongst women, but it is also one of the most frequently cured cancers. Because of this, early detection is crucial, and this can be done through mammography screening. With the increasing need of an automated interpretation system, a lot of methods have been proposed so far and, regardless of the algorithms, they all share a step: pre-processing. That is, identifying the image orientation, detecting the breast and eliminating irrelevant parts.
This paper aims to describe, analyze, compare and evaluate six of the most commonly used edge detection operators: Sobel, Roberts Cross, Prewitt, Farid and Simoncelli, Scharr and Canny. We detail the algorithms, their implementations and the metrics used for evaluation and continue by comparing the operators both visually and numerically, finally concluding that Canny best suit our needs.
 Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 68, 6 (2018), 394–424.
 Canny, J. A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, 6 (1986), 679–698.
 Desai, S. D., Megha, G., Avinash, B., Sudhanva, K., Rasiya, S., and Linganagouda, K. Detection of microcalcification in digital mammograms by improved mmgw segmentation algorithm. In 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies (2013), IEEE, pp. 213–218.
 Duque, A. E. R., G´omez, D. C. A., and Nieto, J. K. A. Breast lesions detection in digital mammography: An automated pre-diagnosis. In 2014 XIX Symposium on Image, Signal Processing and Artificial Vision (2014), IEEE, pp. 1–5.
 Es-salhi, R., Daoudi, I., Tallal, S., Medromi, H., et al. A survey on segmentation techniques of mammogram images. In International Symposium on Ubiquitous Networking (2016), Springer, pp. 545–556.
 Farid, H., and Simoncelli, E. P. Optimally rotation-equivariant directional derivative kernels. In International Conference on Computer Analysis of Images and Patterns (1997), Springer, pp. 207–214.
 Ferrari, R., Frere, A., Rangayyan, R., Desautels, J., and Borges, R. Identification of the breast boundary in mammograms using active contour models. Medical and Biological Engineering and Computing 42, 2 (2004), 201–208
 Harun, M., Izzah, M., Ibrahim, N., and Aziz, N. S. Comparative study of edge detection algorithm: vessel wall elasticity measurement for deep vein thrombosis diagnosis. ARPN Journal of Engineering and Applied Sciences 10, 19 (2015), 8635–8641.
 Indra Kanta Maitra, Sanjay Nag, S. K. B. A novel edge detection algorithm for digital mammogram. International Journal of Information and Communication Technology Research (2012).
 Jose, A., Dixon, K. D. M., Joseph, N., George, E. S., and Anjitha, V. Performance study of edge detection operators. In 2014 International Conference on Embedded Systems (ICES) (2014), IEEE, pp. 7–11.
 Kumar, S., Singh, M., and Shaw, D. Comparative analysis of various edge detection techniques in biometric application. International Journal of Engineering and Technology (IJET) 8, 6 (2016), 2452–2459.
 Lau, T.-K., and Bischof, W. F. Automated detection of breast tumors using the asymmetry approach. Computers and biomedical research 24, 3 (1991), 273–295.
 Mat Harun, N. H., Ibrahim, N., and Aziz, N. S. Comparative study of edge detection algorithm: vessel wall elasticity measurement for deep vein thrombosis diagnosis.
 Mirzaalian, H., Ahmadzadeh, M. R., Sadri, S., and Jafari, M. Pre-processing algorithms on digital mammograms. In MVA (2007), pp. 118–121.
 Park, J., and Murphey, Y. Edge Detection in Grayscale, Color, and Range Images. 04 2008.
 Poobathy, D., and Chezian, R. M. Edge detection operators: Peak signal to noise ratio based comparison. IJ Image, Graphics and Signal Processing 10 (2014), 55–61.
 Ramani, R., Vanitha, N. S., and Valarmathy, S. The pre-processing techniques for breast cancer detection in mammography images. International Journal of Image, Graphics and Signal Processing 5, 5 (2013), 47.
 Rosenfeld, A., and Kak, A. Digital picture processing academic press. New York (1982), 242.
 Sharma, J., and Sharma, S. Mammogram image segmentation using watershed. Int J Info Tech and Knowledge Management 4 (2011), 423–5.
 Sian, C., Jiye, W., Ru, Z., and Lizhi, Z. Cattle identification using muzzle print images based on feature fusion. In IOP Conference Series: Materials Science and Engineering (2020), vol. 853, IOP Publishing, p. 012051.
 SUCKLING J, P. The mammographic image analysis society digital mammogram database. Digital Mammo (1994), 375–386.
 Vairalkar, M. K., and Nimbhorkar, S. Edge detection of images using sobel operator. International Journal of Emerging Technology and Advanced Engineering 2, 1 (2012), 291–293.
 Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13, 4 (2004), 600–612.
 Wirth, M. A. A nonrigid approach to medical image registration: matching images of the breast. Royal Melbourne Institute of Technology, 1999.
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