一种基于深度学习的自适应医学超声图像去斑方法

付晓薇; 杨雪飞; 陈芳; 李曦

doi:10.11999/JEIT190580

一种基于深度学习的自适应医学超声图像去斑方法

doi: 10.11999/JEIT190580

付晓薇^{1, 2, 3, ,},
杨雪飞^{1, 2},
陈芳⁴,
李曦⁵

1.
武汉科技大学计算机科学与技术学院武汉 430065
2.
智能信息处理与实时工业系统湖北省重点实验室武汉 430065
3.
华中科技大学材料成形与模具技术国家重点实验室武汉 430074
4.
武汉科技大学校医院超声影像科武汉 430065
5.
华中科技大学人工智能与自动化学院武汉 430074

基金项目: 国家自然科学基金(61873323)，材料成形与模具技术国家重点实验室开放课题研究基金(P2018-016)，湖北省自然科学基金(2017CFB506)，智能信息处理与实时工业系统湖北省重点实验室开放课题项目(2016znss02A, znxx2018ZD01)，大学生科技创新基金项目(18ZRA076)

详细信息

作者简介:
付晓薇：女，1977年生，教授，研究方向为图像处理、计算机视觉、信号处理与分析

杨雪飞：女，1994年生，硕士生，研究方向为图像处理、深度学习

陈芳：女，1972年生，研究方向为肌骨超声图像的调节

李曦：男，1977年生，教授，研究方向为计算机应用，复杂非线性系统的建模和控制

通讯作者:
付晓薇　fxw_wh0409@wust.edu.cn

中图分类号: TN911.73
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- 被引次数: 0
出版历程
- 收稿日期: 2019-07-31
- 修回日期: 2020-03-18
- 网络出版日期: 2020-04-11
- 刊出日期: 2020-07-23

An Adaptive Medical Ultrasound Images Despeckling Method Based on Deep Learning

Xiaowei FU^{1, 2, 3
, ,},
Xuefei YANG^{1, 2},
Fang CHEN⁴,
Xi LI⁵

1.
College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China
2.
Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China
3.
State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan 430074, China
4.
Department of Ultrasound and Imaging, Wuhan University of Science and Technology Hospital, Wuhan 430065, China
5.
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Funds: The National Natural Science Foundation of China (61873323), The Open Fund Project of State Key Laboratory of Material Processing and Die & Mould Technology (P2018-016), The Natural Science Foundation of Hubei Provincial (2017CFB506), The Open Fund Project of Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System (2016znss02A, znxx2018ZD01), The University Student Science and Technology Innovation Fund Project (18ZRA076)

摘要

摘要:
针对传统医学超声图像去斑方法的不足，该文提出一种自适应多曝光融合框架和前馈卷积神经网络模型图像去斑方法。首先，制作超声图像训练数据集；然后，提出一种自适应增强因子的多曝光融合框架，增强图像进行有效特征提取；最后，通过网络训练去斑模型并获得去斑后的图像。实验结果表明，该文较已有的方法，能更有效地滤除医学超声图像中的斑点噪声并更多的保留图像细节。
- 超声图像 /
- 深度学习 /
- 多曝光融合框架 /
- 乘性斑点噪声
Abstract:
Considering the shortage of traditional medical ultrasound image despeckle methods, an adaptive multi-exposure fusion framework and feedforward convolutional neural network model image despeckle method is proposed. Firstly, an ultrasound image training data set is produced. Then, a multi-exposure fusion framework with adaptive enhancement factors is proposed to enhance the image for effective feature extraction.Finally, a speckle model is trained through the network and a speckle image is obtained. Experimental results show that, compared with the existing methods, this paper can more effectively remove speckle noise in medical ultrasound images and retain more image details.
- Ultrasound image /
- Deep learning /
- Multi-exposure fusion framework /
- Multiplicative speckle noise

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图 1 残差网络的基本结构

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图 2 本文流程图

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图 3 网络结构图

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图 4 模拟斑点肝脏超声图像1实验比较

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图 5 模拟斑点肝脏超声图像2实验比较

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图 6 真实医学超声图像去斑后结果比较

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表 1 模拟斑点肝脏超声图像1不同方法PSNR结果(dB)

方法	斑点噪声的标准差σ
方法	0.5	0.6	0.7	0.8	0.9
BI-DTCWT	35.4225	33.8461	32.5368	31.4265	30.3688
NPSM	34.5827	32.9573	31.5366	30.3250	29.2056
NL-means	34.9289	34.1934	33.3554	32.5658	31.6134
BM3D	36.1701	35.7552	35.2485	34.8951	34.2035
Local_entropy_qsp	36.7812	36.1083	35.4363	35.0726	34.5014
DnCNN	35.7701	35.8394	35.8180	35.6769	35.3885
本文方法	36.7203	36.7139	36.6025	36.3568	35.9492

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表 4 模拟斑点肝脏超声图像2不同方法$\beta $结果

方法	斑点噪声的标准差σ
方法	0.5	0.6	0.7	0.8	0.9
BI-DTCWT	0.7078	0.6359	0.5612	0.5099	0.4661
NPSM	0.6830	0.6197	0.5479	0.4990	0.4517
NL-means	0.7191	0.6761	0.6037	0.5449	0.4899
BM3D	0.8030	0.7950	0.7826	0.7683	0.7355
Local_entropy_qsp	0.8263	0.8090	0.7787	0.7567	0.7384
DnCNN	0.9286	0.9238	0.9156	0.9029	0.8812
本文方法	0.9394	0.9325	0.9217	0.9653	0.8836

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表 2 模拟斑点肝脏超声图像2不同方法PSNR结果(dB)

方法	斑点噪声的标准差σ
方法	0.5	0.6	0.7	0.8	0.9
BI-DTCWT	31.0477	29.5409	28.0856	27.4342	26.2056
NPSM	31.5374	30.0985	28.6745	27.6699	26.6843
NL-means	32.7360	31.7539	30.4860	29.5105	28.4174
BM3D	33.8786	33.3096	32.5436	32.0199	31.2079
Local_entropy_qsp	34.3157	33.2426	32.1706	31.5329	30.8599
DnCNN	34.9760	35.0382	34.8497	34.3851	33.6562
本文方法	35.9280	35.9170	35.6289	35.0301	34.1677

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表 3 模拟斑点肝脏超声图像1不同方法$\beta $结果

方法	斑点噪声的标准差σ
方法	0.5	0.6	0.7	0.8	0.9
BI-DTCWT	0.6416	0.5611	0.4823	0.4291	0.3846
NPSM	0.5972	0.5154	0.4352	0.3817	0.3393
NL-means	0.4522	0.4102	0.3564	0.3262	0.2949
BM3D	0.5969	0.5820	0.5685	0.5477	0.5016
Local_entropy_qsp	0.6540	0.6287	0.5991	0.5842	0.5621
DnCNN	0.7803	0.7726	0.7595	0.7393	0.7106
本文方法	0.8128	0.8011	0.7831	0.7564	0.7208

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表 5 真实斑点超声图像不同方法ENL结果

方法	ENL等效视数值
BI-DTCWT	61.2209
NPSM	64.6016
NL-means	109.5584
BM3D	93.4877
Local_entropy_qsp	79.1016
DnCNN	132.9184
本文方法	134.3287

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表 6 50张真实斑点超声图像不同方法ENL平均值比较

方法	ENL等效视数值平均值
BI-DTCWT	75.5182
NPSM	75.5941
NL-means	110.6393
BM3D	110.9127
Local_entropy_qsp	93.7911
DnCNN	140.3622
本文方法	147.0689

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