Image preprocessing is the important phase in digital image processing domain to analyze the undesirable signal present in an image and removal of the same. For any further processing of an image, noise removal is mandatory to get the desired resultant. Before applying any noise removal algorithm to an image, it is obligatory to understand what kind of noise that exactly presents in an image. In this paper, we have handled the impulse noise presence in mammogram image and various spatial based median filters are applied to it. Initially, to confirm the impulse noise presence, the sub-window of an image is subjected to undergo the detection process, where the impulse noise pixels are identified. Secondly, for the detected noise window the traditional median filter is applied. This process does not affect the image quality and produces the noise-free enhanced image. Finally, we have experimented and compared the five different median based denoising algorithms, each one has different detection framework and found the best denoising algorithm for impulse noise removal with the help of qualitative and quantitative metrics.

Preprocessing, Impulse Noise, Denoising, Mammogram Image

[1] Campilho, A.C., 2000. Medical Image Analysis. An overview. Electrónica e Telecomunicações, 3(2), pp.135-142.
[2] Ramani, R., Vanitha, N.S. and Valarmathy, S., 2013. The pre-processing techniques for breast cancer detection in mammography images. International Journal of Image, Graphics and Signal Processing, 5(5), p.47.
[3] Tashk, A., Helfroush, M.S., Danyali, H. and Akbarzadeh, M., 2013, May. An automatic mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classification. In Information and Knowledge Technology (IKT), 2013 5th Conference on (pp. 406-410). IEEE.
[4] Halalli, B. and Makandar, A., 2018. Computer Aided Diagnosis-Medical Image Analysis Techniques. In Breast Imaging. InTech.
[5] Buades, A., Coll, B. and Morel, J.M., 2005, June. A non-local algorithm for image denoising. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 2, pp. 60-65). IEEE.
[6] Alajlan, N., 2010. A Novel Recursive Algorithm For Detail-Preserving Impulse Noise Removal. Journal of King Saud University-Computer and Information Sciences, 22, pp.37-44.
[7] Garnett, R., Huegerich, T., Chui, C. and He, W., 2005. A universal noise removal algorithm with an impulse detector. IEEE Transactions on image processing, 14(11), pp.1747-1754.
[8] Piao, W., Yuan, Y. and Lin, H., 2018. A Digital Image Denoising Algorithm Based on Gaussian Filtering and Bilateral Filtering. In ITM Web of Conferences (Vol. 17, p. 01006). EDP Sciences.
[9] Sun, T. and Neuvo, Y., 1994. Detail-preserving median based filters in image processing. Pattern Recognition Letters, 15(4), pp.341-347.
[10] Nasri, M., Saryazdi, S. and Nezamabadi-pour, H.S.N.L.M., 2013. SNLM: A switching non-local means filter for removal of high density salt and pepper noise. Scientia Iranica, 20(3), pp.760-764.
[11] Mingliang, X., Pei, L., Mingyuan, L., Hao, F., Hongling, Z., Bing, Z., Yusong, L. and Liwei, Z., 2016. Medical image denoising by parallel non-local means. Neurocomputing, 195, pp.117-122.
[12] Singh, P. and Shree, R., 2017. A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion. Journal of King Saud University-Computer and Information Sciences.
[13] Wang, M., Yan, W. and Zhou, S., 2018. Image Denoising Using Singular Value Difference in the Wavelet Domain. Mathematical Problems in Engineering, 2018.
[14] Nair, M.S. and Mol, P.A., 2013. Direction based adaptive weighted switching median filter for removing high density impulse noise. Computers & Electrical Engineering, 39(2), pp.663-689.
[15] Gupta, V., Chaurasia, V. and Shandilya, M., 2015. Random-valued impulse noise removal using adaptive dual threshold median filter. Journal of visual communication and image representation, 26, pp.296-304.
[16] Faragallah, O.S. and Ibrahem, H.M., 2016. Adaptive switching weighted median filter framework for suppressing salt-and-pepper noise. AEU-International Journal of Electronics and Communications, 70(8), pp.1034-1040.
[17] Meher, S.K. and Singhawat, B., 2014. An improved recursive and adaptive median filter for high density impulse noise. AEU-International Journal of Electronics and Communications, 68(12), pp.1173-1179.
[18] Ismaeil, M., Pritamdas, K., Devi, K.J.K. and Goyal, S., 2017, April. Performance analysis of new adaptive decision based median filter on FPGA for impulsive noise filtering. In Electronics, Materials Engineering and Nano-Technology (IEMENTech), 2017 1st International Conference on (pp. 1-5). IEEE.
[19] Bakwad, K.M., Pattnaik, S.S., Sohi, B.S., Devi, S., Panigrahi, B.K. and Gollapudi, S.V., 2009. Bacterial foraging optimization technique cascaded with adaptive filter to enhance peak signal to noise ratio from single image. IETE Journal of Research, 55(4), pp.173-179.
[20] Dong, Y. and Xu, S., 2007. A new directional weighted median filter for removal of random-valued impulse noise. IEEE Signal Processing Letters, 14(3), pp.193-196.
[21] Esakkirajan, S., Veerakumar, T., Subramanyam, A.N. and PremChand, C.H., 2011. Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal processing letters, 18(5), pp.287-290.
[22] Meher, S.K. and Singhawat, B., 2014. An improved recursive and adaptive median filter for high density impulse noise. AEU-International Journal of Electronics and Communications, 68(12), pp.1173-1179.