[논문]Low-dose CT Image Denoising Using Classification Densely Connected Residual Network

Ming, Jun; Yi, Benshun; Zhang, Yungang; Li, Huixin

doi:10.3837/tiis.2020.06.009

Low-dose CT Image Denoising Using Classification Densely Connected Residual Network 원문보기

KSII Transactions on internet and information systems : TIIS, v.14 no.6, 2020년, pp.2480 - 2496

Ming, Jun (School of Electronic Information, Wuhan University) , Yi, Benshun (School of Electronic Information, Wuhan University) , Zhang, Yungang (School of Electronic Information, Wuhan University) , Li, Huixin (School of Electronic Information, Wuhan University)

Abstract ▼ AI-Helper

Considering that high-dose X-ray radiation during CT scans may bring potential risks to patients, in the medical imaging industry there has been increasing emphasis on low-dose CT. Due to complex statistical characteristics of noise found in low-dose CT images, many traditional methods are difficult to preserve structural details effectively while suppressing noise and artifacts. Inspired by the deep learning techniques, we propose a densely connected residual network (DCRN) for low-dose CT image noise cancelation, which combines the ideas of dense connection with residual learning. On one hand, dense connection maximizes information flow between layers in the network, which is beneficial to maintain structural details when denoising images. On the other hand, residual learning paired with batch normalization would allow for decreased training speed and better noise reduction performance in images. The experiments are performed on the 100 CT images selected from a public medical dataset-TCIA(The Cancer Imaging Archive). Compared with the other three competitive denoising algorithms, both subjective visual effect and objective evaluation indexes which include PSNR, RMSE, MAE and SSIM show that the proposed network can improve LDCT images quality more effectively while maintaining a low computational cost. In the objective evaluation indexes, the highest PSNR 33.67, RMSE 5.659, MAE 1.965 and SSIM 0.9434 are achieved by the proposed method. Especially for RMSE, compare with the best performing algorithm in the comparison algorithms, the proposed network increases it by 7 percentage points.

주제어

표/그림 (15)

그림 Fig. 1. The architecture of our proposed network.
그림 Fig. 2. The algorithm flow chart.
그림 Fig. 3. A NDCT image
그림 Fig. 4. The image of corresponding LDCT
그림 Fig. 5. Qualitative results of the test image A
그림 Fig. 6. Qualitative results of the test image B.
그림 Fig. 7. magnification of the region marked enclosed by the red box in Fig. 5.
그림 Fig. 8. magnification of the region enclosed by the red box in Fig.6.
표 Table 1. Quantitative evaluation indexes of different methods
표 Table 2. Computational cost of different methods
표 Table 3. The evaluation indexes with different number of Blocks
표 Table 4. The evaluation indexes with different number of feature maps
그림 Fig. 9. The graph of the evaluation index with the number of convolution module
그림 Fig. 10. The graph of the evaluation index changes with the quantity of convolutional layer output feature maps
표 Table 5. The evaluation indexes with different structural configurations

AI 본문요약
AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

제안 방법

In order to make the evaluation of the image quality more in line with people's subjective feelings, SSIM evaluates the image quality based on brightness, contrast, and structure.

성능/효과

Among the three methods based on convolutional neural network, D-Resnet has the least number of weights and the fastest CPU/GPU computing speed, but the cost is that its denoising effect is still far from the other two network models. For RED-CNN and the network implemented in this paper, the effect of the network implemented in this paper is slightly ahead of that of RED-CNN, but also reduces the weight by nearly 79%, greatly simplifying the complexity of the model. Meanwhile, the CPU/GPU computing speed of the network implemented in this paper is also significantly faster than that of RED-CNN, and the computing efficiency is significantly improved.
Inspired, we have proposed DCRN for low-dose CT image denoising, which mainly improves the denoising performance through three mechanisms: batch normalization, residual learning and dense connection. From the results of the experiment, our suggested DCRN obtains better performance in both visual quality and quantitative indexes while maintaining a low computational cost. If the proposed network can be trained with real images instead of simulated ones in the future, the reliability of the network model will be greatly improved, which will help translate the research results into actual products.

후속연구

From the results of the experiment, our suggested DCRN obtains better performance in both visual quality and quantitative indexes while maintaining a low computational cost. If the proposed network can be trained with real images instead of simulated ones in the future, the reliability of the network model will be greatly improved, which will help translate the research results into actual products.

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