The appearance of computer aided technologies image handling procedures have turned out to be progressively essential in a wide assortment of restorative applications. Mediation between the insurance of helpful indicative data and noise concealment must be cherished in therapeutic images. Image de-noising is an appropriate issue found in differing image handling and computer vision issues. There are different existing techniques to denoise images. The imperative property of a decent image de-noising model is that it ought to totally evacuate noise beyond what many would consider possible just as save edges. This paper shows a survey of some real work in region of image de-noising. The target in all control is to extricate data about the scene being imaged. The quick advancement in automated therapeutic image recreation and the related improvements in investigation strategies and computer helped determination has supported medicinal imaging into a standout amongst the most vital sub-fields in logical imaging Ultrasound, MRI, CT-Scan are the restorative procedures basically utilized by the radiologist for representation of inside structure of the human body with no medical procedure. These give sufficient data about the human delicate tissue, which helps in the finding of human sicknesses.

Medical Image Processing, Medical Image Enhancement, Mammogram

[1] A.K. Jain, Fundamentals of Digital Image Processing.
[2] B. Zhang, Computer Vision vs. Human Vision.
[3] R.C. Gonzalez, Digital Image Processing, Pearson Education India, 2009.
[4] N. Patel, A. Shah, M. Mistry, K. Dangarwala. "International Conference on Convergence of Technology-2014". IEEE-2014.
[5] R. Sumalathaand M. V. Subramanyam, "Hierarchical Lossless Image Compression for Telemedicine Applications" Sciene Direct IMCIP-2015.
[6] L. Lin, W. Yang, C. Li, J. Tang, and X. Cao, "Inference with Collaborative Model for Interactive Tumor Segmentation in Medical Image Sequences". IEEE Transactions on Cybernetics.
[7] F. Riaz, A. Hassan, R. Nisar, M. DinisRibeiro & M.T. Coimbra, "Content-Adaptive Region-Based Color Texture Descriptors for Medical Images". IEEE 2015 Journal of Biomedical & Health Informatics.
[8] M. Becker and N.M. Thalmann, "Muscle Tissue Labeling of Human Lower Limb in Multi - Channel mDixon MR Imaging: Concepts and Applications". IEEE / ACM Transactions on Computational Biology and Bioinformatics.
[9] V. Kumbhakarna, V.R.Patil, S. Kawathekar, "Review on Speckle Noise Reduction Techniques for Medical Ultrasound Image Processing". I. J. of Computer Techniques – Volume 2 Issue 1, 2015.
[10] N.T. Binh and A. Khare "Adaptive complex wavelet technique for medical image de-noising" in proceedings of third Int Conf on development of Biomedical Engineering, pp. 195-198, Vietnam, January 11-14, 2010.
[11] P.H. Tsui, C.K. Yeh, C.C. Huang, "Noise-Assisted Correlation Algorithm for Suppressing Noise-Induced Artifacts in Ultrasonic Nakagami Images". IEEE Trans Information Technology in Biomedicine. Vol. 16, No. 3, May 2012.
[12] K.M.M. Rao, V.D.P. Rao, Medical Image Processing.
[13] N.R. Pal, B. Bhowmick, S.K. Patel, S. Pal, J. Das, "A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms", Neuro computing (2008), 2625–2634.
[14] H.D. Cheng, J. Shan, W Ju, Y. Guo, L.Zhang, "Automated breast cancer detection, classification using ultra sound images-a survey", Pattern Recognition (2010).
[15] T.K. Yeong, "Contrast enhancement using brightness preserving bi-histogram equalization", IEEE Trans. Consum. Electron., 1997, 43, (1), pp. 1–8
[16] Y. Wang, Q. Chen, B. Zhang, "Image enhancement based on equal area dualistic sub-image histogram equalization method", IEEE Trans. Consum. Electron., 1999, 45, (1), pp. 68–75
[17] S. Chen, A.R. Ramli, "Minimum mean brightness error bi-histogram equalization in contrast enhancement", IEEE Trans. Consum. Electron., 2003, 49, (4), pp. 1310–1319
[18] S. Chen, A.R. Ramli, "Contrast enhancement using recursive meanseparate histogram equalization for scalable brightness preservation", IEEE Trans. Consum. Electron., 2003, 49, (4), pp. 1301–1309
[19] M. Tiwari, B. Gupta, M. Shrivastava, "High-speed quantile-based histogram equalization for brightness preservation and contrast enhancement", IET Image Processing, vol. 9(1), 2014, pp. 80-89.
[20] R.A. Hummel, "Image Enhancement by Histogram Transformation". Computer Graphics and Image Processing 6 (1977) 184195.
[21] S. M. Pizer, E. P. Amburn, J. D. Austin, et al., "Adaptive Histogram Equalization and Its Variations". Computer Vision, Graphics, and Image Processing 39 (1987) 355-368.
[22] K. Zuiderveld, "Contrast Limited Adaptive Histogram Equalization". In, P. Heckbert, Graphics Gems IV, Academic Press 1994, ISBN 0-12-336155-9
[23] T. Sund & A. Møystad, "Sliding window adaptive histogram equalization of intra-oral radiographs", effect on diagnostic quality. Dentomaxillofac Radiol. 2006 May;35(3):133-8.
[24] Sundaram M., Ramar K., Arumugam N. and Prabin G., 2011. Histogram based contrast enhancement for mammogram images. International Conference on Signal Processing, Communication, Computing and Networking Technologies, pp. 842-846.
[25] Sundaram M., Ramar K., Arumugam N. and Prabin G., 2011. Histogram modified local contrast enhancement for mammogram images. Applied Soft Computing, pp. 5809-5816.
[26] Sundaram M., Ramar K., Arumugam N. and Prabin G., 2012. Histogram modified local contrast enhancement for mammogram images. International Journal of Biomedical Engineering and Technology, vol. 9(1), doi: 10.1504/ijbet.2012.047371.
[27] T.K. Agarwal, M. Tiwari, S.S. Lamba, "Modified histogram based contrast enhancement using homomorphic filtering for medical images", Advance Computing Conference (IACC), 2014 IEEE International, pp. 964-968.
[28] A. Buades, B. Coll, and J. Morel, "A review of image de-noising algorithms, with a new one," Multiscale Model. Simul., vol. 4, no. 2, pp. 490–530, 2005.
[29] A. Buades, B. Coll, and J. Morel, "A non-local algorithm for image de-noising," in IEEE Comput. Soc. Conf. on Comput. Vision & Pattern Recognition, Jun. 2005, vol. 2, pp. 60–65.
[30] D. Van De Ville and M. Kocher, "Sure-based non-local means," IEEE Signal Process. Lett., vol. 16, no. 11, pp. 973–976, 2009.
[31] R. Vignesh, B. T. Oh, and C.-C. Kuo, "Fast non-local means computation with probabilistic early termination," IEEE Signal Process. Lett., vol. 17, no. 3, pp. 277–280, Mar. 2010.
[32] M. Saxena, "An expeditious algorithm for random valued impulse noise removal in fingerprint images using basis splines", in 49th Annual Convention of the Computer Society of India (CSI), pp. 215–222 (2015).
[33] K.S. Srinivasan, D. Ebenezer, "A new fast and efficient decision-based algorithm for removal of highdensity impulse noises". IEEE Signal Process. Lett. 14(3), 189–192 (2007).
[34] T. Sun, Y. Neuvo, "Detail-preserving median based filters in image processing". Pattern Recognit. Lett. 15(4), 341–347 (1994).
[35] H. Talebi, P. Milanfar, "Global image de-noising". IEEE Trans. Image Process. 23(2), 755–768 (2014).