Method Based on Mathematical Morphology and BM3D for Reduction of Noise in Dental CT Images Low Radiation
Abstract
The impact in reducing the radiation dose in computed tomography (CT) exams is directly related to the quality of the images obtained in this exam. Such images are degraded by undesirable artifacts, known as noise. In order to improve the quality of these images and provide an accurate medical diagnosis, it is necessary to apply noise reduction techniques. In this article, it is proposed a method to filter noise in low-dose dental CT images, based on mathematical morphology and block-matching 3D (BM3D) filtering. Experimental results of the proposed method were compared with several existing methods and valida- ted using the PSNR, SSIM and MSE metrics. Through several experiments, the proposed method demonstrated superior performance compared to the analyzed filters, reducing noise and preserving details in a more satisfactory way.
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Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K. (2007). Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Transactions on image processing, 16(8):2080–2095.
Diwakar, M. and Kumar, M. (2018). A review on ct image noise and its denoising. Biomedical Signal Processing and Control, 42:73–88. http://dx.doi.org/10.1016/j.bspc.2018.01.010
Donoho, D. L. and Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the american statistical association, 90(432):1200–1224.
El Hassani, A. and Majda, A. (2016). Efficient image denoising method based on mathematical morphology reconstruction and the non-local means filter for the mri of the head. In Information Science and Technology (CiSt), 2016 4th IEEE International Colloquium on, pages 422–427. IEEE.
Feng, J., Ding, M., and Zhang, X. (2014). Decision-based adaptive morphological filter for fixed-value impulse noise removal. Optik-International Journal for Light and Electron Optics, 125(16):4288–4294. http://dx.doi.org/10.1016/j.ijleo.2014.03.037
He, K., Sun, J., and Tang, X. (2013). Guided image filtering. IEEE transactions on pattern analysis & machine intelligence, (6):1397–1409.
Hore, A. and Ziou, D. (2010). Image quality metrics: Psnr vs. ssim. In Pattern recognition (icpr), 2010 20th international conference on, pages 2366–2369. IEEE. http://dx.doi.org/10.1109/ICPR.2010.579
Li, Z., Yu, L., Trzasko, J. D., Lake, D. S., Blezek, D. J., Fletcher, J. G., McCollough, C. H., and Manduca, A. (2014). Adaptive nonlocal means filtering based on local noise level for ct denoising. Medical physics, 41(1). http://dx.doi.org/10.1118/1.4851635
Loupas, T., McDicken, W., and Allan, P. L. (1989). An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE transactions on Circuits and Systems, 36(1):129–135. http://dx.doi.org/10.1109/31.16577
Marques Filho, O. and Neto, H. V. (1999). Processamento digital de imagens. Brasport.
Pisano, E. D., Zong, S., Hemminger, B. M., DeLuca, M., Johnston, R. E., Muller, K., Braeuning, M. P., and Pizer, S. M. (1998). Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. Journal of Digital imaging, 11(4):193. http://dx.doi.org/10.1007/BF03178082
Pitas, I. and Venetsanopoulos, A. N. (1992). Order statistics in digital image processing. Proceedings of the IEEE, 80(12):1893–1921. http://dx.doi.org/10.1109/5.192071
Reza, A. M. (2004). Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. Journal of VLSI signal processing systems for signal, image and video technology, 38(1):35–44. http://dx.doi.org/10.1023/B:VLSI.0000028532.53893.82
Soille, P. (2013). Morphological image analysis: principles and applications. Springer Science & Business Media.
Tomasi, C. and Manduchi, R. (1998). Bilateral filtering for gray and color images. In Computer Vision, 1998. Sixth International Conference on, pages 839–846. IEEE.
Treece, G. (2016). The bitonic filter: linear filtering in an edge-preserving morphological framework. IEEE Transactions on Image Processing, 25(11):5199–5211. http://dx.doi.org/10.1109/TIP.2016.2605302
Ze-Feng, D., Zhou-Ping, Y., and You-Lun, X. (2007). High probability impulse noise-removing algorithm based on mathematical morphology. IEEE signal processing Letters, 14(1):31–34. http://dx.doi.org/10.1109/LSP.2006.881524
Published
2019-06-11
How to Cite
STRINGHINI, Rômulo Marconato; WELFER, Daniel.
Method Based on Mathematical Morphology and BM3D for Reduction of Noise in Dental CT Images Low Radiation. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 19. , 2019, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 210-221.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2019.6255.
