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RETRACTED ARTICLE: Medical Image Enhancement by a Bilateral Filter Using Optimization Technique

  • Image & Signal Processing
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This article was retracted on 11 May 2022

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Abstract

For researchers, denoising of Magnetic Resonance (MR) image is a greatest challenge in digital image processing. In this paper, the impulse noise and Rician noise in the medical MR images are removed by using Bilateral Filter (BF). The novel approaches are presented in this paper; Enhanced grasshopper optimization algorithm (EGOA) is used to optimize the BF parameters. To simulate the medical MR images (with different variances), the impulse and Rician noises are added. The EGOA is applied to the noisy image in searching regions of window size, spatial and intensity domain to obtain the filter parameters optimally. The PSNR is taken as fitness value for optimization. We examined the proposed technique results with other MR images After the optimal parameters assurance. In order to comprehend the BF parameters selection importance, the results of proposed denoising method is contrasted with other previously used BFs, genetic algorithm (GA), gravitational search algorithm (GSA) using the quality metrics such as signal-to-noise ratio (SNR), structural similarity index metric (SSIM), mean squared error (MSE), and PSNR. The outcome shows that the EOGA method with BF shows good results than the earlier methods in both edge preservation and noise elimination from medical MR images. The experimental results demonstrate the performance of the proposed method with the accuracy, computational time, and maximum deviation, Peak Signal to Noise Ratio (PSNR), MSE, SSIM, and entropy values of MR images over the existing methods.

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References

  1. R. Biswas, D. Purkayastha and S. Roy, Denoising of MRI Images Using Curvelet Transform. Advances in Systems, Control and Automation, pp. 575–583, 2017.

    Google Scholar 

  2. Jifara, W., Jiang, F., Rho, S., Cheng, M., and Liu, S., Medical image denoising using convolutional neural network: a residual learning approach. J. Supercomput., 2017.

  3. Ben Said, A., Hadjidj, R., and Foufou, S., Total Variation for Image Denoising Based on a Novel Smart Edge Detector: An Application to Medical Images. Journal of Mathematical Imaging and Vision, 2018.

  4. K. Das, M. Maitra, P. Sharma and M. Banerjee, Early Started Hybrid Denoising Technique for Medical Images. Recent Trends in Signal and Image Processing, pp. 131–140, 2018.

    Google Scholar 

  5. Lysaker, M., Lundervold, A., and Tai, X.-C., Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Trans. Image Process. 12(12):1579–1590, 2003.

    Article  Google Scholar 

  6. Arora, S., Hanmandlu, M., and Gupta, G., Filtering impulse noise in medical images using information sets. Pattern Recogn. Lett., 2018.

  7. Bai, J., Song, S., Fan, T., and Jiao, L., Medical image denoising based on sparse dictionary learning and cluster ensemble. Soft. Comput. 22(5):1467–1473, 2017.

    Article  Google Scholar 

  8. Lee, Y., Improved total-variation noise-reduction technique with gradient method using iteration counter and its application in medical diagnostic chest and abdominal X-ray imaging. Optik 170:475–483, 2018.

    Article  CAS  Google Scholar 

  9. Caldairou, B., Passat, N., Habas, P., Studholme, C., and Rousseau, F., A non-local fuzzy segmentation method: Application to brain MRI. Pattern Recogn. 44(9):1916–1927, 2011.

    Article  Google Scholar 

  10. Yang, J., Fan, J., Ai, D., Wang, X., Zheng, Y., Tang, S., and Wang, Y., Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image. Neurocomputing 195:88–95, 2016.

    Article  Google Scholar 

  11. Gai, S., Zhang, B., Yang, C., and Yu, L., Speckle noise reduction in medical ultrasound image using monogenic wavelet and Laplace mixture distribution. Digital Signal Processing 72:192–207, 2018.

    Article  Google Scholar 

  12. Sudeep, P., Palanisamy, P., Rajan, J., Baradaran, H., Saba, L., Gupta, A., and Suri, J., Speckle reduction in medical ultrasound images using an unbiased non-local means method. Biomedical Signal Processing and Control 28:1–8, 2016.

    Article  Google Scholar 

  13. Zhang, Y., Tian, X., and Ren, P., An adaptive bilateral filter based framework for image denoising. Neurocomputing 140:299–316, 2014.

    Article  Google Scholar 

  14. Qi, M., Zhou, Z., Liu, J., Cao, J., Wang, H., Yan, A., Wu, D., Zhang, H., and Tang, L., Image Denoising Algorithm via Spatially Adaptive Bilateral Filtering. Adv. Mater. Res. 760-762:1515–1518, 2013.

    Article  Google Scholar 

  15. Zhang, M., and Gunturk, B., Multiresolution Bilateral Filtering for Image Denoising. IEEE Trans. Image Process. 17(12):2324–2333, 2008.

    Article  Google Scholar 

  16. Ramanandan, S., Lehmann, B., and Kraus, D., Optimal parameters for bilateral filtering and SAS image denoising. 2011 International Symposium on Ocean Electronics, 2011.

  17. Shi, H., Determination of bilateral filter coefficients based on particle swarm optimization. 2012 5th International Congress on Image and Signal Processing, 2012.

  18. Borntrager, C., Chorpita, B., Orimoto, T., Love, A., and Mueller, C., Validity of Clinician’s Self-Reported Practice Elements on the Monthly Treatment and Progress Summary. The Journal of Behavioral Health Services & Research 42(3):367–382, 2013.

    Article  Google Scholar 

  19. Farooq, M., Application of Genetic Algorithm & Morphological Operations for Image Segmentation. IJARCCE:195–199, 2015.

  20. Hu, K., Cheng, Q., Li, B., and Gao, X., The complex data denoising in MR images based on the directional extension for the undecimated wavelet transform. Biomedical Signal Processing and Control 39:336–350, 2018.

    Article  Google Scholar 

  21. Sharif, M., Arfan Jaffar, M., and Tariq Mahmood, M., Optimal composite morphological supervised filter for image denoising using genetic programming: Application to magnetic resonance images. Eng. Appl. Artif. Intell. 31:78–89, 2014.

    Article  Google Scholar 

  22. Özmen, G., and Özşen, S., A new denoising method for fMRI based on weighted three-dimensional wavelet transform. Neural Comput. & Applic. 29(8):263–276, 2017.

    Article  Google Scholar 

  23. Malik, M., Ahsan, F., and Mohsin, S., Adaptive image denoising using cuckoo algorithm. Soft. Comput. 20(3):925–938, 2014.

    Article  Google Scholar 

  24. de Paiva, J., Toledo, C., and Pedrini, H., An approach based on hybrid genetic algorithm applied to image denoising problem. Appl. Soft Comput. 46:778–791, 2016.

    Article  Google Scholar 

  25. Zhang, J., Lin, G., Wu, L., and Cheng, Y., Speckle filtering of medical ultrasonic images using wavelet and guided filter. Ultrasonics 65:177–193, 2016.

    Article  CAS  Google Scholar 

  26. Miri, S. S., Rashidi, S., and Ghods, M., Medical image denoising based on 2D discrete cosine transform via ant colony optimization. Optik 156:938–948, 2018.

    Article  Google Scholar 

  27. Naimi, H., Adamou-Mitiche, A., and Mitiche, L., Medical image denoising using dual tree complex thresholding wavelet transform and Wiener filter. Journal of King Saud University - Computer and Information Sciences 27(1):40–45, 2015.

    Article  Google Scholar 

  28. Marschner, H., Pampel, A, and Moeller, H., Method and device for magnetic resonance imaging with improved sensitivity by noise reduction. U.S. Patent Application No. 15/572,009, 2018.

  29. Sudeep, P. V. et al., An improved nonlocal maximum likelihood estimation method for denoising magnetic resonance images with spatially varying noise levels. Pattern Recogn. Lett., 2018.

  30. Chen, G. et al., Noise reduction in diffusion MRI using non-local self-similar information in joint x− q space. Med. Image Anal. 53:79–94, 2019.

    Article  Google Scholar 

  31. Riji, R., Rajan, J., Sijbers, J. and Nair, M., Iterative bilateral filter for Rician noise reduction in MR images, 2018.

  32. Sijbers, J., den Dekker, A., Van Audekerke, J., Verhoye, M., and Van Dyck, D., Estimation of the Noise in Magnitude MR Images. Magn. Reson. Imaging 16(1):87–90, 1998.

    Article  CAS  Google Scholar 

  33. Gudbjartsson, H., and Patz, S., The Rician distribution of noisy MRI data. Magn. Reson. Med. 34(6):910–914, 1995.

    Article  CAS  Google Scholar 

  34. Mirjalili, S., Saremi, S., Faris, H., and Aljarah, I., Grasshopper optimization algorithm for multi-objective optimization problems. Appl. Intell. 48(4):805–820, 2017.

    Article  Google Scholar 

  35. Medical MR image database: https://github.com/sfikas/medical-imaging-datasets

  36. Tomasi, C., Manduchi, R., Bilateral filtering for gray and color images. In: Computer Vision, 1998. Sixth International Conference on, (pp. 839–846). IEEE, 1998.

  37. Bhonsle, D., Chandra, V., and Sinha, G. R., Medical image denoising using bilateral filter. International Journal of Image, Graphics and Signal Processing 4(6):36, 2012.

    Article  Google Scholar 

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Correspondence to V. Anoop.

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V. Anoop declares that he has no conflict of interest. Bipin PR declares that he has no conflict of interest.

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This article is part of the Topical Collection on Image & Signal Processing

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Anoop, V., Bipin, P.R. RETRACTED ARTICLE: Medical Image Enhancement by a Bilateral Filter Using Optimization Technique. J Med Syst 43, 240 (2019). https://doi.org/10.1007/s10916-019-1370-x

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  • DOI: https://doi.org/10.1007/s10916-019-1370-x

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