Método Baseado em Morfologia Matemática e BM3D para Redução de Ruı́do em Imagens Dentais de TC de Baixa Radiação
Resumo
O impacto em reduzir a dose de radiação em exames de tomogra- fia computadorizada (TC) está diretamente relacionado à qualidade das ima- gens. Tais imagens são degradadas por artefatos indesejáveis, conhecidos como ruı́do. Diante disso, para melhorar a qualidade destas imagens e fornecer um diagnóstico médico preciso, é necessário aplicar técnicas que sejam capazes de reduzir ruı́do. Neste artigo, é proposto um método para filtrar ruı́do em imagens dentais de TC de baixa radiação, utilizando operadores da morfologia matemática e filtragem Block-Matching 3D (BM3D). Os resultados do método proposto foram comparados com diversos filtros existentes e validados utili- zando as métricas PSNR, SSIM e MSE. Através de diversos experimentos, o método proposto demonstrou performance superior aos filtros analisados, re- duzindo ruı́do e preservando detalhes de forma mais satisfatória.
Referências
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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
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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.
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Buades, A., Coll, B., and Morel, J.-M. (2005). A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation, 4(2):490–530. http://dx.doi.org/10.1137/040616024
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
Publicado
11/06/2019
Como Citar
STRINGHINI, Rômulo Marconato; WELFER, Daniel.
Método Baseado em Morfologia Matemática e BM3D para Redução de Ruı́do em Imagens Dentais de TC de Baixa Radiação. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (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.
