Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This is the first GA based-optimization study to find optimal parameters of bilateral filter.Both the simulated and clinical brain MR images re used for Rician noise removal.The preservation of edges and removal of noise re investigated for different noise levels.A better performance in computation time of our approach was observed.The quality of the denoised images with the proposed parameters was validated using quantitative metrics. Noise elimination is an important pre-processing step in magnetic resonance (MR) images for clinical purposes. In the present study, as an edge-preserving method, bilateral filter (BF) was used for Rician noise removal in MR images. The choice of BF parameters affects the performance of denoising. Therefore, as a novel approach, the parameters of BF re optimized using genetic algorithm (GA). First, the Rician noise with different variances (?=10, 20, 30) was added to simulated T1-ighted brain MR images. To find the optimum filter parameters, GA was applied to the noisy images in searching regions of window size 3×3, 5×5, 7×7, 11×11, and 21×21, spatial sigma 0.1-10 and intensity sigma 1-60. The peak signal-to-noise ratio (PSNR) was adjusted as fitness value for optimization.After determination of optimal parameters,  investigated the results of proposed BF parameters with both the simulated and clinical MR images. In order to understand the importance of parameter selection in BF,  compared the results of denoising with proposed parameters and other previously used BFs using the quality metrics such as mean squared error (MSE), PSNR, signal-to-noise ratio (SNR) and structural similarity index metric (SSIM). The quality of the denoised images with the proposed parameters was validated using both visual inspection and quantitative metrics. The experimental results shod that the BF with parameters proposed by us shod a better performance than BF with other previously proposed parameters in both the preservation of edges and removal of different level of Rician noise from MR images. It can be concluded that the performance of BF for denoising is highly dependent on optimal parameter selection.
['Saime Akdemir Akar']
I am writing a research paper on a idea Determination of optimal parameters for bilateral filter in brain MR image denoising
[{"from": "human", "value": "I am writing a research paper on a idea Determination of optimal parameters for bilateral filter in brain MR image denoising"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This is the first GA based-optimization study to find optimal parameters of bilateral filter.Both the simulated and clinical brain MR images re used for Rician noise removal.The preservation of edges and removal of noise re investigated for different noise levels.A better performance in computation time of our approach was observed.The quality of the denoised images with the proposed parameters was validated using quantitative metrics. Noise elimination is an important pre-processing step in magnetic resonance (MR) images for clinical purposes. In the present study, as an edge-preserving method, bilateral filter (BF) was used for Rician noise removal in MR images. The choice of BF parameters affects the performance of denoising. Therefore, as a novel approach, the parameters of BF re optimized using genetic algorithm (GA). First, the Rician noise with different variances (?=10, 20, 30) was added to simulated T1-ighted brain MR images. To find the optimum filter parameters, GA was applied to the noisy images in searching regions of window size 3\u00d73, 5\u00d75, 7\u00d77, 11\u00d711, and 21\u00d721, spatial sigma 0.1-10 and intensity sigma 1-60. The peak signal-to-noise ratio (PSNR) was adjusted as fitness value for optimization.After determination of optimal parameters,  investigated the results of proposed BF parameters with both the simulated and clinical MR images. In order to understand the importance of parameter selection in BF,  compared the results of denoising with proposed parameters and other previously used BFs using the quality metrics such as mean squared error (MSE), PSNR, signal-to-noise ratio (SNR) and structural similarity index metric (SSIM). The quality of the denoised images with the proposed parameters was validated using both visual inspection and quantitative metrics. The experimental results shod that the BF with parameters proposed by us shod a better performance than BF with other previously proposed parameters in both the preservation of edges and removal of different level of Rician noise from MR images. It can be concluded that the performance of BF for denoising is highly dependent on optimal parameter selection."}]