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Multiscale Transform and Shrinkage Thresholding Techniques for Medical Image Denoising – Performance Evaluation


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1. Bhonsle, D., V. Chandra, G. R. Sinha. Medical Image Denoising Using Bilateral Filter. I. J. Image. – Graphics and Signal Processing, Vol. 4, 2012, No 6. pp. 36-43. DOI:10.5815/ijigsp.2012.06.06.10.5815/ijigsp.2012.06.06 Search in Google Scholar

2. Gonzalez, R. C., R. E. Woods. Digital Image Processing. Second Ed. Prentice-Hall, Inc., 2002. Search in Google Scholar

3. Mallat, S. G. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. – IEEE Transaction on Pattern Recognition and Machine Intelligence, Vol. 11, 1987, No 7. pp. 674-695. DOI:10.1109/34.192463.10.1109/34.192463 Search in Google Scholar

4. Donoho, D. L., I. M. Johnstone. Adatpting Tounknow Smoothness via Wavelet Shrinkage. – Journal of the American Statistical Association, Vol. 90, 1995, No 432. pp. 1200-1224. DOI:10.1.1.161.8697.10.1080/01621459.1995.10476626 Search in Google Scholar

5. Zhang, M., B. K. Guntuk. Multiresolution Bilateral Filtering for Image Denoising. – IEEE Transactions on Image Processing, Vol. 17, 2008, No 12, pp. 2324-2333.10.1109/TIP.2008.2006658261456019004705 Search in Google Scholar

6. Coifman, R., D. Donoho. Translation Invariant Denoising. – In: Lecture Notes in Statistics: Wavelets and Statistics. Vol. 1995. New York, Springer-Verlag. 1995, pp. 125-150.10.1007/978-1-4612-2544-7_9 Search in Google Scholar

7. Selesnick, I. W. The Double Density Dual Tree DWT. – IEEE Transactions on Signal Processing, Vol. 52, 2004, No 5, pp. 1304-1314.10.1109/TSP.2004.826174 Search in Google Scholar

8. Candès, E. J., D. L. Donoho. Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges in Curve and Surface Fitting. Nashville, TN: Vanderbuilt Univ. Press, 2000. Search in Google Scholar

9. Chen, G. Y., B. Kégl. Image Denoising with Complex Ridgelets. – Elsevier, Pattern Recognition, Vol. 40, 2007, No 2, pp. 578-585.10.1016/j.patcog.2006.04.039 Search in Google Scholar

10. Nezamoddini-Kachouie, N., P. Fieguth, E. Jernigan. Bayes Shrink Ridgelets for Image Denoising. – Springer, ICIAR, LNCS 3211, 2004, pp.163-170.10.1007/978-3-540-30125-7_21 Search in Google Scholar

11. Patil, A. A., J. Singhai. Image Denoising Using Curvelettransform: An Approach for Edge Preservation. – Journal of Scientific & Industrial Research, Vol. 69, 2010, No 1, pp. 34-38. Search in Google Scholar

12. Bala, A. A., C. Hati, C. H. Punith. Image Denoising Method Using Curvelet Transform and Wiener Filter. – International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. Vol. 3, 2014, No 1. Search in Google Scholar

13. Starck, J.-L., E. J. Candès, D. L. Donoho. The Curvelet Transform for Image Denoising. – IEEE Transactions on Image Processing, Vol. 11, 2002, No 6, pp. 670-684.10.1109/TIP.2002.101499818244665 Search in Google Scholar

14. Starck, J.-L., E. J. Candes, D. L. Donoho. The Curvelet Transform for Image Denoising. – IEEE Transactions on Image Processing, Vol. 11, 2002, No 6, pp. 670-684.10.1109/TIP.2002.1014998 Search in Google Scholar

15. Mallat, S., G. Peyré. A Review of Bandlet Methods for Geometrical Image Representation. – Numerical Algorithms, Vol. 44, 2007, No 3, pp. 205-234.10.1007/s11075-007-9092-4 Search in Google Scholar

16. Villegas, O. O. V., H. J. O. Domínguez, V. G. C. Sánchez. A Comparison of the Bandelet, Wavelet and Contourlet Transforms for Image Denoising. – In: Proc. of 7th Mexican International Conference on Artificial Intelligence, 2008, pp 207-212.10.1109/MICAI.2008.63 Search in Google Scholar

17. Zhou, Y., J. Wang. Image Denoising Based on the Symmetric Normal Inverse Gaussian Model and Non-Subsampled Contourlet Transform. – IET Image Processing, Vol. 6, 2013, No 8, pp. 1136-1147.10.1049/iet-ipr.2012.0148 Search in Google Scholar

18. Eslami, R., H. Radha. Translation Invariant Contourlet Transform and Its Application to Image Denoising. – IEEE Transactions on Image Processing, Vol. 15, 2006, No 11, pp. 3362-3374.10.1109/TIP.2006.88199217076396 Search in Google Scholar

19. Babu, J. J. J., G. F. Sudha. Non-Subsampled Contourlet Transform Based Image Denoising in Ultrasound Thyroid Images Using Adaptive Binary Morphological Operations. – IET Computer Vision, Vol. 8, 2014, No 6, pp. 718-728.10.1049/iet-cvi.2014.0008 Search in Google Scholar

20. Hao, W., J. Li, X. Qu, Z. Dong. Fast Iterative Contourlet Thresholding for Compressed Sensing MR. – Electronic Letters, Vol. 49, 2013, No 19, pp. 1206-1208.10.1049/el.2013.1483 Search in Google Scholar

21. Bhateja, V., H. Patel, A. Krishn, A. Sahu. Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains. – IEEE Sensors Journal, Vol. 15, 2015, No 12, pp. 6783-6790.10.1109/JSEN.2015.2465935 Search in Google Scholar

22. Mansour, M., H. Mouhadjer, A. Alipacha, K. Draoui. Comparative Analysis on Image Compression Techniques for Chromosome Images. – International Conference on Advances in Biomedical Engineering, 2013, pp. 34-37.10.1109/ICABME.2013.6648840 Search in Google Scholar

23. Jeena, J., P. Salice, J. Neetha. Denoising Using Soft Thresholding. – International Journal of Advanced Reseach in Electrical, Electronic and Instrumentation Engineering, Vol. 2, 2013, No 3, pp. 1027-1032. Search in Google Scholar

24. Chang, S. G., B. Yu, M. Vattereli. Adaptive Wavelet Thresholding for Image Denoising and Compression. – IEEE Transactions of Image Processing, Vol. 9, 2000, No 9, pp. 1532-1546.10.1109/83.86263318262991 Search in Google Scholar

25. Jansen, M. Noise Reduction by Wavelet Thresholding. New York, Springer Verlag, Inc., 2001.10.1007/978-1-4613-0145-5 Search in Google Scholar

26. Fodor, I. K., C. Kamath. Denoising through Wavlet Shrinkage: An Empirical Study. Center for Applied Science Computing Lawrence Livermore National Laboratory, 2001. Search in Google Scholar

27. Donoho, D. L., J. M. Johnstone. Ideal Spatial Adaptation by Wavelet Shrinkage. – Biometrika, Vol. 81, 1994, No 30, pp. 425-455.10.1093/biomet/81.3.425 Search in Google Scholar

28. Dengwen, Z., W. Cheng. Image Denoising with an Optimal Threshold and Neighbouring Window. – Journal Pattern Recognition Letters, Vol. 29, 2008, No 11, pp. 1603-1704.10.1016/j.patrec.2008.04.014 Search in Google Scholar

29. Dewangan, N., A. D. Goswami. Image Denoising Using Wavletthresholding Methods. – Int. J. of Engg. Sci. & Mgmt. (IJESM), Vol. 2, 2012, No 2, pp. 271-275. Search in Google Scholar

30. Kalra, G. S., S. Singh. Efficient Digital Image Denoising for Gray Scale Images. – Multimedia Tools and Applications, Vol. 75, 2016, No 8, pp. 4467-4484.10.1007/s11042-015-2484-x Search in Google Scholar

31. Hosotani, F., Y. Inuzuka, M. Hasegawa, S. Hirobayashi, T. Misawa. Image Denoising with Edge-Preserving and Segmentation Based on Mask NHA. – IEEE Transactions on Image Processing, Vol. 24, 2015, No 12, pp. 6025-6033.10.1109/TIP.2015.249446126513792 Search in Google Scholar

32. Dhannawat, R., A. B. Patankar. Improvement to Blind Image Denoising by Using Local Pixel Grouping with SVD. – Procedia Computer Science, Vol. 79, 2016, pp. 314-320.10.1016/j.procs.2016.03.041 Search in Google Scholar

33. Guo, X., Y. Li, T. Suo, J. Liang. De-Noising of Digital Image Correlation Based on Stationary Wavelet Transform. – Optics and Lasers in Engineering, Vol. 90, 2017, pp. 161-172.10.1016/j.optlaseng.2016.10.015 Search in Google Scholar

34. Candes, E. J., D. L. Donoho. Ridgelets: A Key to Higher-Dimensional Intermittency?. – Phil. Trans. R. Soc. Lond. A., Vol. 357, 1999, No 1760, pp. 2495-2509.10.1098/rsta.1999.0444 Search in Google Scholar

35. Candès, E., D. Donoho. Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edge. – In: A. Cohen, C. Rabut, L. Schumaker, Eds. Curves and Surface Fitting: Saint-Malo. Vanderbilt University Press, Nashville, 2000, pp. 105-120. Search in Google Scholar

36. Do, M. N., M. Vetterli. Thecontourlet Transform: An Efficient Directional Multiresolution Image Representation. – IEEE Trans. Image Process., Vol. 14, 2009, No 12, p. 2091.10.1109/TIP.2005.85937616370462 Search in Google Scholar

37. Le Pennec, E., S. Mallat. Sparse Geometric Image Representations With Bandelets. – IEEE Transactions on Image Processing, Vol. 14, 2005, No 4, pp. 423-438.10.1109/TIP.2005.843753 Search in Google Scholar

38. Peyré, G., S. P. Mallat. Surface Compression with Geometric Bandelets. ACM Transactions on Graphics. – Proceedings of ACM SIGGRAPH, Vol. 24, 2005, No 3, pp. 601-608.10.1145/1073204.1073236 Search in Google Scholar

39. Chen, G. Y.. T. D. Bui, A. Krzyzak. Image Denoising with Neighbour Dependency and Customized Wavelet and Threshold. – Pattern Recognition, Vol. 38, 2005, No 1, pp. 115-124. DOI:10.1016/j.patcog.2004.05.009.10.1016/j.patcog.2004.05.009 Search in Google Scholar

40. Zhou, D., W. Cheng. Image Denoising with an Optimal Threshold and Neighbouring Window. – Elsevier Pattern Recognition, Vol. 29, 2008, No 11, pp. 1694-1697. DOI: 10.1016/j.patrec.2008.04.014.10.1016/j.patrec.2008.04.014 Search in Google Scholar

41. Xie, X. Research on the Image Denoising Method on Partial Differential Equations. – Cybernetics and Information Technologies, Vol. 16, 2016, No 5, pp. 109-118.10.1515/cait-2016-0057 Search in Google Scholar

42. Hashimoto, F., H. Ohba, K. Ote, A. Teramoto, H. Tsukada. Dynamic PET Image Denoising Using Deep Convolutional Neural Network without Prior Traning Datasets. – IEEE Access, Vol. 7, 2019, No 5, pp. 96594-96603.10.1109/ACCESS.2019.2929230 Search in Google Scholar

43. Chen, W., Y. Shao, L. Jia, Y. Wang, Q. Zhang, Y. Shang, Y. Liu, Y. Chen, Y. Liu, Z. Gui. Low-Dose CT Image Denoising Model Based on Sparse Representation by Stationarily Classified Sub-Dictionaries. – IEEE Access, Vol. 7, 2019, No 5, pp. 116859-116874.10.1109/ACCESS.2019.2932754 Search in Google Scholar

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Computer Sciences, Information Technology