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KMID : 0357520240470010039
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Journal of Radiological Science and Technology
2024 Volume.47 No. 1 p.39 ~ p.48
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Optimization of Non-Local Means Algorithm in Low-Dose Computed Tomographic Image Based on Noise Level and Similarity Evaluations
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Jeong Ha-Seon
Kim Ie-Jun
Park Su-Bin
Park Su-Yeon
Oh Yun-Ji
Lee Woo-Seok
Seo Kang-Hyeon
Lee Young-Jin
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Abstract
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In this study, we optimized the FNLM algorithm through a simulation study and applied it to a phantom scanned by low-dose CT to evaluate whether the FNLM algorithm can be used to obtain improved image quality images. We optimized the FNLM algorithm with MASH phantom and FASH phantom, which the algorithm was applied with MATLAB, increasing the smoothing factor from 0.01 to 0.05 with increments of 0.001 and measuring COV, RMSE, and PSNR values of the phantoms. For both phantom, COV and RMSE decreased, and PSNR increased as the smoothing factor increased. Based on the above results, we optimized a smoothing factor value of 0.043 for the FNLM algorithm. Then we applied the optimized FNLM algorithm to low dose lung CT and lung CT under normal conditions. In both images, the COV de- creased by 55.33 times and 5.08 times respectively, and we confirmed that the quality of the image of low dose CT ap- plying the optimized FNLM algorithm was 5.08 times better than the image of lung CT under normal conditions. In conclusion, we found that the smoothing factor of 0.043 among the factors of the FNLM algorithm showed the best results and validated the performance by reducing the noise in the low-quality CT images due to low dose with the optimized FNLM algorithm.
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KEYWORD
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Radiation, Computed Tomography, Dose Reduction, FNLM Algorithm, Lung Image
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