Channel Adaptive Ultrasound Image Denoising Method Based on Residual Encoder-decoder Networks

ZENG Xianhua; LI Yancheng; GAO Ge; ZHAO Xueting

doi:10.11999/JEIT210331

Volume 44 Issue 7

Jul. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2547-2558

ZENG Xianhua, LI Yancheng, GAO Ge, ZHAO Xueting. Channel Adaptive Ultrasound Image Denoising Method Based on Residual Encoder-decoder Networks[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2547-2558. doi: 10.11999/JEIT210331

Citation:

ZENG Xianhua, LI Yancheng, GAO Ge, ZHAO Xueting. Channel Adaptive Ultrasound Image Denoising Method Based on Residual Encoder-decoder Networks[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2547-2558. doi: 10.11999/JEIT210331

Citation:

PDF( 10293 KB)

Channel Adaptive Ultrasound Image Denoising Method Based on Residual Encoder-decoder Networks

doi: 10.11999/JEIT210331

ZENG Xianhua^{1, 2
,
,},
LI Yancheng^{1, 2},
GAO Ge^{1, 2},
ZHAO Xueting³

1.
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of Image Cognition, Chongqing 400065, China
3.
Department of Ultrasound, Chongqing Angel Obstetrics and Gynecology Hospital, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62076044), The Key Project of Chongqing Natural Science Foundation (cstc2019jcyjzdxmX0011)

Received Date: 2021-04-20
Accepted Date: 2022-03-07
Rev Recd Date: 2021-12-24

Available Online: 2022-03-19

Publish Date: 2022-07-10

Abstract

Abstract

The denoising of ultrasound images is very important to improve the visual quality of ultrasound images and to accomplish other related computer vision tasks. The feature information in ultrasound images is similar to the speckle noise signal. The existing denoising methods for ultrasound images denoising are easy to cause the loss of texture features of ultrasound images, which will cause serious interference to the accuracy of clinical diagnosis. Therefore, in the process of speckle noise removal, the edge texture information of images should be retained as far as possible to complete better the task of ultrasound images denoising. RED-SENet (Residual Encoder-Decoder with Squeeze-and-Excitation Network), a channel adaptive denoising model based on residual encoder-decoder is presented, which can effectively remove speckle noise in ultrasound images. By introducing the attention deconvolution residual block in the decoder part of the denoising model, the model can learn and use the global information, selective emphasizing the content features of the key channels and suppress the useless features, which can improve the denoising performance of the model. The model is qualitatively evaluated and quantitatively analyzed on 2 private datasets and 2 public datasets, respectively. Compared with some advanced methods, the denoising performance of the model is significantly improved, and it has advantages in noise suppression and structure preservation.
- Image denoising,
- Ultrasound image,
- Deep learning,
- Channel adaptation,
- Attention deconvolution residual block

FullText(HTML)

References(22)

References

[1]	OUAHABI A and TALEB-AHMED A. Deep learning for real-time semantic segmentation: Application in ultrasound imaging[J]. Pattern Recognition Letters, 2021, 144: 27–34. doi: 10.1016/j.patrec.2021.01.010
[2]	LOUPAS T, MCDICKEN W, and ALLAN P L. An adaptive weighted median filter for speckle suppression in medical ultrasonic images[J]. IEEE transactions on Circuits and Systems, 1989, 36(1): 129–135. doi: 10.1109/31.16577
[3]	GARG A and KHANDELWAL V. Despeckling of Medical Ultrasound Images Using Fast Bilateral Filter and Neighshrinksure Filter in Wavelet Domain[M]. RAWAT B, TRIVEDI A, MANHAS S, et al. Advances in Signal Processing and Communication. Singapore: Springer, 2019: 271–280.
[4]	YANG Qingsong, YAN Pingkun, ZHANG Yanbo, et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss[J]. IEEE Transactions on Medical Imaging, 2018, 37(6): 1348–1357. doi: 10.1109/TMI.2018.2827462
[5]	SHAHDOOSTI H R and RAHEMI Z. Edge-preserving image denoising using a deep convolutional neural network[J]. Signal Processing, 2019, 159: 20–32. doi: 10.1016/j.sigpro.2019.01.017
[6]	LIU Denghong, LI Jie, and YUAN Qiangqiang. A spectral grouping and attention-driven residual dense network for hyperspectral image super-resolution[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(9): 7711–7725. doi: 10.1109/TGRS.2021.3049875
[7]	XIA Hao, CAI Nian, WANG Huiheng, et al. Brain MR image super-resolution via a deep convolutional neural network with multi-unit upsampling learning[J]. Signal, Image and Video Processing, 2021, 15(5): 931–939. doi: 10.1007/s11760-020-01817-x
[8]	MAO Xiaojiao, SHEN Chunhua, and YANG Yubin. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections[C]. The 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, 2016: 2810–2818.
[9]	HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778.
[10]	CHEN Hu, ZHANG Yi, KALRA M K, et al. Low-dose CT with a residual encoder-decoder convolutional neural network[J]. IEEE Transactions on Medical Imaging, 2017, 36(12): 2524–2535. doi: 10.1109/TMI.2017.2715284
[11]	CHANG Meng, LI Qi, FENG Huajun, et al. Spatial-adaptive network for single image denoising[C]. The 16th European Conference on Computer Vision, Glasgow, UK, 2020: 171–187.
[12]	MATEO J L and FERNÁNDEZ-CABALLERO A. Finding out general tendencies in speckle noise reduction in ultrasound images[J]. Expert Systems with Applications, 2009, 36(4): 7786–7797. doi: 10.1016/j.eswa.2008.11.029
[13]	OYEDOTUN O K, AL ISMAEIL K, and AOUADA D. Training very deep neural networks: Rethinking the role of skip connections[J]. Neurocomputing, 2021, 441: 105–117. doi: 10.1016/j.neucom.2021.02.004
[14]	HU Jie, SHEN Li, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011–2023. doi: 10.1109/TPAMI.2019.2913372
[15]	KINGMA D P and BA J. Adam: A method for stochastic optimization[J]. arXiv: 1412.6980, 2014.
[16]	VAN DEN HEUVEL T L A, DE BRUIJN D, DE KORTE C L, et al. Automated measurement of fetal head circumference using 2D ultrasound images[J]. PLoS One, 2018, 13(8): e0200412. doi: 10.1371/journal.pone.0200412
[17]	LECLERC S, SMISTAD E, PEDROSA J, et al. Deep learning for segmentation using an open large-scale dataset in 2D echocardiography[J]. IEEE Transactions on Medical Imaging, 2019, 38(9): 2198–2210. doi: 10.1109/TMI.2019.2900516
[18]	BURGER H C, SCHULER C J, and HARMELING S. Image denoising: Can plain neural networks compete with BM3D?[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 2392–2399.
[19]	DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 2007, 16(8): 2080–2095. doi: 10.1109/TIP.2007.901238
[20]	CHEN Yang, YIN Xindao, SHI Luyao, et al. Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing[J]. Physics in Medicine & Biology, 2013, 58(16): 5803–5820. doi: 10.1088/0031-9155/58/16/5803
[21]	CHEN Hu, ZHANG Yi, ZHANG Weihua, et al. Low-dose CT via convolutional neural network[J]. Biomedical Optics Express, 2017, 8(2): 679–694. doi: 10.1364/BOE.8.000679
[22]	ZHANG Yulun, TIAN Yapeng, KONG Yu, et al. Residual dense network for image restoration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(7): 2480–2495. doi: 10.1109/TPAMI.2020.2968521