Quantitative analysis of fast non-local means algorithm smoothing factor impact on lung segmentation accuracy in computed tomography images

Abstract

In computed tomography (CT), noise inevitably occurs during image acquisition and degrades segmentation accuracy. Thus, in this study, we compared and evaluated quantitative factors to derive the optimized smoothing factor of the fast non-local means (FNLM) algorithm, which can improve the accuracy of lung segmentation. Gaussian noise with a standard deviation of 0.05 was added to the lung phantom image and clinically acquired lung image, respectively, and the smoothing factor of the FNLM algorithm was modeled and applied from 0.03 to 0.02 in 0.01 intervals. The segmentation algorithm is modeled based on a region-growing approach. For evaluating segmentation performance, the root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and dice score (DSC) were used. The quantitative evaluation demonstrated that the RMSE, PSNR and DSC exhibited the greatest improvement when the smoothing factor was set as 0.10 for both the phantom and clinically acquired lung images. In the lung phantom images, the RMSE, PSNR, and DSC displayed improvements of 8.40, 1.33, and 1.33 times compared to the noisy images. In the clinically acquired lung images, the RMSE, PSNR, and DSC exhibited enhancements of 9.74, 1.35, 1.37 times respectively, compared to the noisy images. We confirmed that lung segmentation performance was most effectively improved when the smoothing factor of the FNLM algorithm was set to 0.10. Moreover, we demonstrated that adjusting the appropriate smoothing factor in CT images can improve lung segmentation performance.