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Performance evaluation of improved median-modified Wiener filter with segmentation method to improve resolution in computed tomographic images

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Abstract

This study aimed to model and apply an improved median-modified Wiener filter (MMWF) with region growing (RG) segmentation technique in computed tomography (CT) images to improve the noise and blurring effects. To model the improved MMWF, the MATLAB program was used to provide an additional application of median filter and RG techniques from the smoothening process of Wiener filter. In addition, the kernel size of the improved MMWF was set to 7 × 7 and applied to abdominal CT images of the acquired whole body phantom PBU-50 (Kyoto Kagaku, Japan) with tube currents of 50, 100, 200, and 300 mAs, and quantitatively compared to images with conventional MMWF and without MMWF (noisy). Compared with noisy images, the coefficient of variation (COV) and contrast-to-noise ratio (CNR) of the conventional and improved MMWFs improved by approximately 3.41 and 3.32 times, respectively, for the images at all tube current conditions. The improved MMWF showed improved separation between different tissues compared with noisy images even though the smoothening was performed on images with low-dose conditions (50 and 100 mAs). Moreover, a comparative evaluation with other conventional filters (median, Wiener, total variation, and non-local means) demonstrated the improved MMWF performance at low-dose conditions. The COV and CNR of the improved MMWF were overall balanced; however, the improved MMWF performed better than other filters to restore the boundary signal between the two tissues. In conclusion, the improved MMWF with RG technique could acquire CT images with improved characteristics by removing the noise and blurring effects.

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References

  1. L.M.T. Phan et al., Nanomaterial-based optical and electrochemical biosensors for amyloid beta and tau: potential for early diagnosis of Alzheimer’s disease. Expert Rev. Mol. Diagn. 21, 175 (2021). https://doi.org/10.1080/14737159.2021.1887732

    Article  Google Scholar 

  2. J.W. Seo et al., Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach. Sci. Rep. (2023). https://doi.org/10.1038/s41598-022-25849-0

    Article  Google Scholar 

  3. A. Chaudhary, S.S. Singh, Lung cancer detection on CT Images by using image processing. 2012 Int. Conf. Comput. Sci. (2012). https://doi.org/10.1109/ICCS.2012.43

    Article  Google Scholar 

  4. M. Diwakar, M. Kumar, A review on CT image noise and its denoising. Biomed. Signal Process. Control 42, 73 (2018). https://doi.org/10.1016/j.bspc.2018.01.010

    Article  Google Scholar 

  5. X. Duan et al., Electronic noise in CT detectors: impact on image noise and artifacts. AJR Am. J. Roentgenol.Roentgenol. 201, W626 (2013). https://doi.org/10.2214/AJR.12.10234

    Article  Google Scholar 

  6. J.H. Kim, Y. Chang, J.B. Ra, Denoising of polychromatic CT images based on their own noise properties. Med. Phys. 43, 2251 (2016). https://doi.org/10.1118/1.4945022

    Article  Google Scholar 

  7. S. Gou et al., CT image sequence restoration based on sparse and low-rank. PLoS One 8, e72696 (2013). https://doi.org/10.1371/journal.pone.0072696

    Article  ADS  Google Scholar 

  8. A. Manduca et al., Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med. Phys. 36, 4911 (2009). https://doi.org/10.1118/1.3232004

    Article  Google Scholar 

  9. A. Khmag, A.R. Ramli, N. Kamarudin, Clustering-based natural image denoising using dictionary learning approach in wavelet domain. Soft. Comput.Comput. 23, 8013 (2019). https://doi.org/10.1007/s00500-018-3438-9

    Article  Google Scholar 

  10. D.J. Vincent, V.S. Hari, R.A. Muhammed, Edge enhancement and noise smoothening of CT images with anisotropic diffusion filter and unsharp masking. In: 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS). 55 (2018). https://doi.org/10.1109/RAICS.2018.8635086

  11. D. Sadykova, A. P. James, Quality assessment metrics for edge detection and edge-aware filtering: a tutorial review, In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2366 (2017). https://doi.org/10.1109/ICACCI.2017.8126200

  12. Y. Zhang, Tensor decomposition and non-local means based spectral CT image denoising. J. Xray Sci. Technol. 27, 397 (2019). https://doi.org/10.3233/XST-180413

    Article  Google Scholar 

  13. K. Leng, An improved non-local means algorithm for image denoising. In 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP). 149 (2017). https://doi.org/10.1109/SIPROCESS.2017.8124523

  14. I. Ram, M. Elad, I. Cohen, Generalized tree-based wavelet transform. IEEE Trans. Signal Process. 59, 4199 (2011). https://doi.org/10.1109/TSP.2011.2158428

    Article  ADS  MathSciNet  Google Scholar 

  15. J. Liang, R. Liu, Stacked denoising autoencoder and dropout together to prevent overfitting in deep neural network. In 2015 8th International Congress on Image and Signal Processing (CISP). 697 (2015). https://doi.org/10.1109/CISP.2015.7407967

  16. M. Gholizadeh-Ansari, J. Alirezaie, P. Babyn, Deep learning for low-dose CT denoising using perceptual loss and edge detection layer. J. Digit. Imaging 33, 504 (2020). https://doi.org/10.1007/s10278-019-00274-4

    Article  Google Scholar 

  17. N. Gallagher, G. Wise, A theoretical analysis of the properties of median filters. IEEE Trans. Acoust. Speech Signal Process.Acoust. Speech Signal Process. 29, 1136 (1981). https://doi.org/10.1109/TASSP.1981.1163708

    Article  Google Scholar 

  18. A. A. Omer et al, Denoising CT images using median based filters: a review. In 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). 1 (2018). https://doi.org/10.1109/ICCCEEE.2018.8515829

  19. M. Tabuchi, N. Yamane, Y. Morikawa, Adaptive Wiener filter based on gaussian mixture model for denoising chest X-ray CT image. In SICE Annual Conference 2007. 682 (2007). https://doi.org/10.1109/SICE.2007.4421069

  20. C. Anam et al., New noise reduction method for reducing CT scan dose: combining Wiener filtering and edge detection algorithm. AIP Conf. Proc. 1677, 040004 (2015). https://doi.org/10.1063/1.4930648

    Article  Google Scholar 

  21. C.V. Cannistraci, F.M. Montevecchi, M. Alessio, Median-modified Wiener filter provides efficient denoising, preserving spot edge and morphology in 2-DE image processing. Proteomics 9, 4908 (2009). https://doi.org/10.1002/pmic.200800538

    Article  Google Scholar 

  22. X. Yang et al., A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points. Comput. Methods Programs Biomed.. Methods Programs Biomed. 113, 69 (2014). https://doi.org/10.1016/j.cmpb.2013.08.019

    Article  Google Scholar 

  23. A. Baâzaoui et al., Semi-automated segmentation of single and multiple tumors in liver CT Images using entropy-based fuzzy region growing. IRBM. 38, 98 (2017). https://doi.org/10.1016/j.irbm.2017.02.003

    Article  Google Scholar 

  24. S. Loncaric, D. Kovacevic, E. Sorantin, Semi-automatic active contour approach to segmentation of computed tomography volumes. Proc. SPIE 3979, 917 (2000). https://doi.org/10.1117/12.387757

    Article  ADS  Google Scholar 

  25. C. Militello et al., A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans. Comput. Biol. Med.. Biol. Med. 114, 103424 (2019). https://doi.org/10.1016/j.compbiomed.2019.103424

    Article  Google Scholar 

  26. S. Rafiei et al., Liver segmentation in abdominal CT images using probabilistic atlas and adaptive 3D region growing. Ann. Int. Conf. IEEE Eng. Med. Biol. Soc. 2019, 6310 (2019). https://doi.org/10.1109/EMBC.2019.8857835

    Article  Google Scholar 

  27. R. Adams, L. Bischof, Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell.Intell. 16, 641 (1994). https://doi.org/10.1109/34.295913

    Article  Google Scholar 

  28. M.K. Kalra, L. Bischof et al., Low-dose CT of the abdomen: evaluation of image improvement with use of noise reduction filters pilot study. Radiology 228, 251 (2003). https://doi.org/10.1148/radiol.2281020693

    Article  Google Scholar 

  29. L. Shao et al., From heuristic optimization to dictionary learning: a review and comprehensive comparison of image denoising algorithms. IEEE Trans. Cybern. 44, 1001 (2014). https://doi.org/10.1109/TCYB.2013.2278548

    Article  Google Scholar 

  30. L.I. Rudin, S. Osher, E. Fatemi, Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60, 259 (1992). https://doi.org/10.1016/0167-2789(92)90242-F

    Article  ADS  MathSciNet  Google Scholar 

  31. Z. Tian et al., Low-dose CT reconstruction via edge-preserving total variation regularization. Phys. Med. Biol. 56, 5949 (2011). https://doi.org/10.1088/0031-9155/56/18/011

    Article  Google Scholar 

  32. A. Buades, B. Coll, J. M. Morel, A non-local algorithm for image denoising, In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). 2, 60 (2005). https://doi.org/10.1109/CVPR.2005.38

  33. Z. Li et al., Adaptive nonlocal means filtering based on local noise level for CT denoising. Med. Phys. 41, 011908 (2014). https://doi.org/10.1118/1.4851635

    Article  Google Scholar 

  34. J. Li et al., Temporal non-local means filtering for studies of intrinsic brain connectivity from individual resting fMRI. Med. Image Anal. 61, 101635 (2020). https://doi.org/10.1016/j.media.2020.101635

    Article  Google Scholar 

  35. K. Huang et al, Adaptive non-local means denoising algorithm for cone-beam computed tomography projection images, In 2009 Fifth International Conference on Image and Graphics. 33 (2000). https://doi.org/10.1109/ICIG.2009.37

  36. A. Grossmann, J. Morlet, Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM J. Math. Anal. 15, 723 (1984). https://doi.org/10.1137/0515056

    Article  MathSciNet  Google Scholar 

  37. O. Tischenko, C. Hoeschen, E. Buhr, An artefact-free, structure-saving noise reduction using the correlation between two images for threshold determination in the wavelet domain. Proc. SPIE 5747, 1066 (2005). https://doi.org/10.1117/12.595863

    Article  ADS  Google Scholar 

  38. T. Meinhardt et al, Learning proximal operators: using denoising networks for regularizing inverse imaging problems, In 2017 IEEE International Conference on Computer Vision (ICCV). 1799 (2017). https://doi.org/10.1109/ICCV.2017.198

  39. F. Hashimoto et al., Dynamic PET image denoising using deep convolutional neural networks without prior training datasets. IEEE Access. (2019). https://doi.org/10.1109/ACCESS.2019.2929230

    Article  Google Scholar 

  40. C.R. Park, S.H. Kang, Y. Lee, Median modified wiener filter for improving the image quality of gamma camera images. Nucl. Eng. Technol.. Eng. Technol. 52, 2328 (2020). https://doi.org/10.1016/j.net.2020.03.022

    Article  Google Scholar 

  41. S. Ju, S.H. Kang, Y. Lee, Optimization of mask size for median-modified Wiener filter according to matrix size of computed tomography images. Nucl. Instrum. Methods Phys. Res. A. 1010, 165508 (2021). https://doi.org/10.1016/j.nima.2021.165508

    Article  Google Scholar 

  42. M. Mahmoudi, G. Sapiro, Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Process. Lett. 12, 839 (2005). https://doi.org/10.1109/LSP.2005.859509

    Article  ADS  Google Scholar 

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Acknowledgements

This study was supported by a Grant from the National Foundation of Korea (NRF) funded by the Korean government (Grant No. NRF-2021R1F1A1061440). This work was also supported by the Gachon University research fund of 2023 (Grant No. GCU-2023-03880001). Juyoung Park and Seyoung Song contributed equally to the writing of this paper.

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Correspondence to Seong-Hyeon Kang or Youngjin Lee.

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Park, J., Song, S., Kang, SH. et al. Performance evaluation of improved median-modified Wiener filter with segmentation method to improve resolution in computed tomographic images. J. Korean Phys. Soc. 84, 573–581 (2024). https://doi.org/10.1007/s40042-024-01020-y

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