Acta Optica Sinica, Volume. 38, Issue 4, 0410003(2018)
Low-Dose CT Image Denoising Method Based on Convolutional Neural Network
Figures & Tables(9)
Fig. 2. Schematic of dilated convolution. (a) Step size is 1; (b) step size is 2; (c) step size is 3
Fig. 4. Denoising results comparison images. (a) Original CT image; (b) low-dose CT image; (c) RED-CNN; (d) proposed network
Fig. 5. Fig. 4(a) enlargement of the box region. (a) Original CT image; (b) low-dose CT image; (c) RED-CNN; (d) proposed network
Table 1. Objective indexes of all the testing images
View tableTable 1. Objective indexes of all the testing images
Serial number Index Low-dose RED-CNN Proposed network ① PSNR /dB 26.27 32.35 32.73 SSIM 0.8209 0.9378 0.9378 RMSE 0.0486 0.0241 0.0231 ② PSNR /dB 27.49 32.59 32.34 SSIM 0.8461 0.9573 0.9536 RMSE 0.0422 0.0235 0.0241 ③ PSNR /dB 30.43 35.75 36.28 SSIM 0.9065 0.9621 0.9630 RMSE 0.0301 0.0163 0.0153 ④ PSNR /dB 24.72 31.65 33.19 SSIM 0.7711 0.9184 0.9199 RMSE 0.0581 0.0261 0.0219 ⑤ PSNR /dB 22.00 27.07 28.80 SSIM 0.7252 0.8922 0.8968 RMSE 0.0794 0.0443 0.0363 ⑥ PSNR /dB 20.82 26.72 28.16 SSIM 0.6995 0.8950 0.8975 RMSE 0.0909 0.0461 0.0391 ⑦ PSNR /dB 29.30 36.85 36.69 SSIM 0.8843 0.9660 0.9653 RMSE 0.0343 0.0144 0.0146 ⑧ PSNR /dB 29.78 35.51 35.29 SSIM 0.9191 0.9707 0.9695 RMSE 0.0324 0.0168 0.0172 ⑨ PSNR /dB 27.59 37.75 37.53 SSIM 0.8337 0.9618 0.9605 RMSE 0.0417 0.0130 0.0133 ⑩ PSNR /dB 27.27 32.37 33.53 SSIM 0.8607 0.9341 0.9348 RMSE 0.0433 0.0241 0.0211 Average PSNR /dB 26.57 32.86 33.45 SSIM 0.8267 0.9395 0.9399 RMSE 0.0501 0.0249 0.0226
Table 2. Comparision of complexity
View tableTable 2. Comparision of complexity
Item RED-CNN Proposed network Complexity 1848000 216864 Time consumption (CPU) /s 12.472 3.908 Time consumption (GPU) /s 0.180 0.061
Table 3. Comparision of different network structures
View tableTable 3. Comparision of different network structures
Structure description Default Without BN and residual learning Without concatenating feature maps Without dilated convolution PSNR /dB 33.45 32.84 33.33 33.24 SSIM 0.9399 0.9369 0.9382 0.9373 RMSE 0.0226 0.0245 0.0229 0.0231
Table 4. Impact on denoising performance when each ConvBlock contains different numbers of Conv-BN-ReLU
View tableTable 4. Impact on denoising performance when each ConvBlock contains different numbers of Conv-BN-ReLU
Structure description Numbers of Conv-BN-ReLU contained in each ConvBlock 1 2 3 PSNR /dB 33.27 33.45 33.33 SSIM 0.9290 0.9399 0.9397 RMSE 0.0230 0.0226 0.0229
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Yungang Zhang, Benshun Yi, Chenyue Wu, Yu Feng. Low-Dose CT Image Denoising Method Based on Convolutional Neural Network[J]. Acta Optica Sinica, 2018, 38(4): 0410003
Paper Information
Category: Image Processing
Received: Sep. 15, 2017
Accepted: --
Published Online: Jul. 10, 2018
The Author Email: Zhang Yungang (zyg60714@126.com)
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