2D Gradient Algorithms for Noise Reduction in Radiological Images
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Abstract
In areas such as biomedical image processing, the techniques or methods used to recover the content in noise-contaminated signals are essential. One of them has been adaptive filtering, which, by adjusting to the desired signal through real-time updating of the coefficients, allows improvement and deconvolution in the recovery of degraded or contaminated images, attracting the attention of researchers in inverse problems. In this paper, the 2D-AR gradient algorithm is used in noise reduction in dental radiological images, for which simulations are performed to obtain the best configuration of the hyperparameters, and a statistical analysis of the values obtained is performed. Based on the simulation results and the established metrics, it is demonstrated that the algorithm achieves a slightly higher noise reduction than the other 2D gradient algorithms (LMS and NLMS).
Keywords
2D adaptive filter, Noise cancellation, Signal processing, Radiological images, Gradient algorithm
References
- F. Schopper, J. Ninkovic, R. Richter, G. Schaller, T. Selle, J. Treis, “High resolution X-ray imaging with pnCCDs,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment., vol. 912, pp. 11–15, 2018. https://doi.org/10.1016/j.bspc.2022.1040 31
- S. Lee, M. S. Lee, M. G. Kang, “Poisson--Gaussian noise analysis and estimation for low-dose X-ray images in the NSCT domain,” Sensors, vol. 18, no. 4, e1019, 2018. https://doi.org/10.3390/s18041019
- T. B. Chandra, K. Verma, “Analysis of quantum noise-reducing filters on chest X-ray images: A review,” Measurement, vol. 153, e107426, 2020. https://doi.org/10.1016/j.measurement.2019.107426
- T. Kirti, K. Jitendra, S. Ashok, “Poisson noise reduction from X-ray images by region classification and response median filtering,” Sādhanā, vol. 42, no. 6, pp. 855–863, 2017. https://doi.org/10.1007/s12046-017-0654-4
- S. Kockanat, N. Karaboga, “A novel 2D-ABC adaptive filter algorithm: a comparative study,” Digital Signal Processing, vol. 40, pp. 140–153, 2015. https://doi.org/10.1016/j.dsp.2015.02.010
- J. Collazos Ramirez, P. E. Jojoa Gomez, J. P. Hoyos Sanchez, "Extension and Analysis of the ARG algorithm to 2D," IEEE Latin America Transactions, vol. 20, no. 12, pp. 2448-2454, 2022. https://doi.org/ 10.1109/TLA.2022.9905613
- A. M. S. Esfand, S. Nikbakht, “Image denoising with two-dimensional adaptive filter algorithms”, Iranian Journal of Electrical and Electronic Engineering, vol. 7, pp. 84-105, 2011.
- M. S. E. Abadi, S. N. Aali, “The novel two-dimensional adaptive filter algorithms with the performance analysis,” Signal Processing, vol. 103, pp. 348–366, 2014. https://doi.org/10.1016/j.sigpro.2013.12.016
- A. Abdi, S. Kasaei, “Panoramic dental X-rays with segmented mandibles,” Mendeley Data, v2, 2020. https://doi:10.17632/hxt48yk462.2
- R. C. Gonzalez, R. E. Woods, “Digital image processing, prentice hall,” Up. Saddle River, NJ, 2008.
- N. Kamolkunasiri, P. Punyabukkana, E. Chuangsuwanich, "A Comparative Study on Out of Scope Detection for Chest X-ray Images," in 20th International Joint Conference on Computer Science and Software Engineering, Phitsanulok, Thailand, 2023, pp. 73-78. https://doi.org/10.1109/JCSSE58229.202 3.10202003
- M. Jha, Y. Hasija, "Artificial Intelligence In Field of Medical Imaging Informatics," in 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, Greater Noida, India, 2023, pp. 661-666. https://doi.org/10.1109/ICACITE57410.2023.10182498
- O. Rodríguez-Bastidas, H. F. Vargas-Rosero, “Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession,” Revista Facultad de Ingeniería, vol. 29, no. 54, e10173. https://doi.org/10.19053/01211129.v29.n54.2020
- V. Göreke, "A novel method based on Wiener filter for denoising Poisson noise from medical X-Ray images," Biomedical Signal Processing and Control , vol.79, e104031, 2023. https://doi.org/10.1016/j. bspc.2022.104031
- S. Lee, M. G. Kang, "Poisson-Gaussian Noise Reduction for X-Ray Images Based on Local Linear Minimum Mean Square Error Shrinkage in Nonsubsampled Contourlet Transform Domain," IEEE Access, vol. 9, pp. 100637-100651, 2021. https://doi.org/10.1109/ACCESS.2021.3097078