Speckle removal algorithm for ultrasonic image based on multi-scale fast non-local means filtering

doi:10.11772/j.issn.1001-9081.2021040620

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (6): 1950-1956.DOI: 10.11772/j.issn.1001-9081.2021040620

• Multimedia computing and computer simulation • Previous Articles Next Articles

Speckle removal algorithm for ultrasonic image based on multi-scale fast non-local means filtering

Lulu LEI¹^,², Yingyue ZHOU¹^,²(), Chi LI¹^,², Xinyu WANG¹^,², Jiaqi ZHAO¹^,²

^1.School of Information Engineering，Southwest University of Science and Technology，Mianyang Sichuan 621010，China
^2.Sichuan Provincial Key Laboratory of Robot Technology Used for Special Environment （Southwest University of Science and Technology），Mianyang Sichuan 621010，China

Received:2021-04-20 Revised:2021-07-01 Accepted:2021-07-20 Online:2022-06-22 Published:2022-06-10
Contact: Yingyue ZHOU
About author:LEI Lulu，born in 1997，M. S. candidate. Her research interests include image restoration.
LI Chi，born in 1998，M. S. candidate. His research interests include digital image processing.
WANG Xinyu，born in 1997，M. S. candidate. His research interests include artificial intelligence.
ZHAO Jiaqi，born in 1998，M. S. candidate. His research interests include artificial intelligence.
Supported by:
National Natural Science Foundation of China(61401379);Key Research and Development Project of Science and Technology Department of Sichuan Province(2021YFG0383);LongShan Academic Talent Research Support Program of Southwest University of Science and Technology(17LZX648)

基于多尺度快速非局部平均滤波的超声图像去斑算法

雷露露¹^,², 周颖玥¹^,²(), 李驰¹^,², 王欣宇¹^,², 赵家琦¹^,²

^1.西南科技大学信息工程学院，四川绵阳 621010
^2.特殊环境机器人技术四川省重点实验室（西南科技大学），四川绵阳 621010

通讯作者: 周颖玥
作者简介:雷露露（1997—），女，四川广安人，硕士研究生，主要研究方向：图像恢复
李驰（1998—），男，四川成都人，硕士研究生，主要研究方向：数字图像处理
王欣宇（1997—），男，四川德阳人，硕士研究生，主要研究方向：人工智能
赵家琦（1998—），男，四川成都人，硕士研究生，主要研究方向：人工智能。
基金资助:
国家自然科学基金资助项目(61401379);四川省科技厅重点研发项目(2021YFG0383);西南科技大学龙山学术人才科研支持计划项目(17LZX648)

Abstract

Abstract:

Ultrasound imaging is widely used in clinical diagnosis because of its advantages of convenience， low cost and non-radiation， however， speckle noise in the image may adversely affect clinical diagnosis or subsequent image analysis.As a typical denoising technology， when using Non-Local Means Filter（NLMF）for speckle removal of ultrasonic image，there will be shortcomings such as high time consumption and difficulty in setting filtering parameters. Therefore， a Multi-scale Fast Non-Local Means Filter （MF-NLMF） algorithm was proposed to remove speckle noise of ultrasonic image. A Fast NLMF （F-NLMF） algorithm was first give out to reduce the computing time by using the mutual correlation filtering technique. Then multiple window parameters were set to obtain multiple speckle removal results， and the model parameters were able to be adjusted adaptively according to the window size. The final speckle removal image was obtained by fusing the multiple speckle removal results. Experimental results show that under the same experimental conditions， the F-NLMF algorithm reduces the computing time by at least 96.04% compared with the traditional NLMF algorithm. Compared with other six algorithms such as Iterative Bayesian Non-Local Mean Filtering （IBNLMF）， the proposed MF-NLMF has the speckle removal image with the Peak Signal-to-Noise Ratio （PSNR） value improved by more than 0.73 dB， the Feature SIMilarity index （FSIM） value increased by more than 0.011， the Contrast-to-Noise Ratio （CNR） and Signal-to-Noise Ratio （SNR） values raised by more than 0.000 5 and 0.001 6 respectively.

Key words: speckle noise, Non-Local Means Filter (NLMF), multi-scale, adaptive, fast filter

摘要：

超声成像因其便捷、廉价、无辐射等优点被广泛应用于临床诊断中，然而图像中的斑点噪声可能对临床诊断或后续图像分析产生不利影响。作为一种典型的去噪技术，在利用非局部平均滤波（NLMF）对超声图像进行去斑时，会存在时耗高、滤波参数不易设置等不足，因此，提出一种多尺度快速非局部平均滤波（MF-NLMF）算法用来去除超声图像的斑点噪声。首先提出快速非局部平均滤波（F-NLMF）算法，利用互相关滤波技术减少运算时耗；接着设置多种窗口参数获得多幅去斑结果，而模型参数值可根据窗口尺寸自适应调节；最后将多幅去斑结果进行融合得到最终的去斑图像。实验结果表明：在相同实验条件下，与传统NLMF算法相比，F-NLMF算法的运算时间至少减少了96.04%；而MF-NLMF算法与迭代贝叶斯非局部均值滤波（IBNLMF）等算法相比，去斑图像的峰值信噪比（PSNR）值、特征相似度测度（FSIM）值、对比度噪声比（CNR）和信噪比（SNR）分别提高了0.73 dB、0.011、0.000 5、0.001 6以上。

关键词: 斑点噪声, 非局部平均滤波, 多尺度, 自适应, 快速滤波

CLC Number:

TP391.41

Lulu LEI, Yingyue ZHOU, Chi LI, Xinyu WANG, Jiaqi ZHAO. Speckle removal algorithm for ultrasonic image based on multi-scale fast non-local means filtering[J]. Journal of Computer Applications, 2022, 42(6): 1950-1956.

雷露露, 周颖玥, 李驰, 王欣宇, 赵家琦. 基于多尺度快速非局部平均滤波的超声图像去斑算法[J]. 《计算机应用》唯一官方网站, 2022, 42(6): 1950-1956.

Figures/Tables 9

Fig. 1 Simulated speckle noise image and its image block schematic diagrams with different positions and sizes

Tab. 1 Similarity distance d values for different image block sizes

噪声强度 $σ$	局部图像1			局部图像2
噪声强度 $σ$	$5 × 5$	$7 × 7$	$9 × 9$	$5 × 5$	$7 × 7$	$9 × 9$
5	19.98	37.07	61.17	12.68	29.55	76.07
15	111.80	208.40	397.20	96.87	245.50	447.30
25	337.00	829.40	1 430.00	283.30	520.80	953.90

Tab. 1 Similarity distance d values for different image block sizes

噪声强度 $σ$	局部图像1			局部图像2
噪声强度 $σ$	$5 × 5$	$7 × 7$	$9 × 9$	$5 × 5$	$7 × 7$	$9 × 9$
5	19.98	37.07	61.17	12.68	29.55	76.07
15	111.80	208.40	397.20	96.87	245.50	447.30
25	337.00	829.40	1 430.00	283.30	520.80	953.90

Fig. 2 Test image examples

Tab. 2 Comparison of despeckling speed of different algorithms to “head” phantom image with speckle noise

匹配区域 $p × p$	搜索区域 $q × q$	算法的运行时间/s			NLMF与F-NLMF运行时间比	IBNLMF与F-NLMF运行时间比
匹配区域 $p × p$	搜索区域 $q × q$	NLMF	IBNLMF	F-NLMF	NLMF与F-NLMF运行时间比	IBNLMF与F-NLMF运行时间比
5×5	13×13	45.19	3.12	1.79	25.25	1.74
5×5	73×73	1 202.39	37.61	7.75	155.15	4.85
7×7	13×13	48.81	3.34	1.85	26.38	1.80
7×7	73×73	1 841.00	38.70	8.53	214.82	4.52
9×9	13×13	65.00	3.60	1.98	32.82	1.82
9×9	73×73	2 040.80	39.96	9.08	226.67	4.40

Tab. 2 Comparison of despeckling speed of different algorithms to “head” phantom image with speckle noise

匹配区域 $p × p$	搜索区域 $q × q$	算法的运行时间/s			NLMF与F-NLMF运行时间比	IBNLMF与F-NLMF运行时间比
匹配区域 $p × p$	搜索区域 $q × q$	NLMF	IBNLMF	F-NLMF	NLMF与F-NLMF运行时间比	IBNLMF与F-NLMF运行时间比
5×5	13×13	45.19	3.12	1.79	25.25	1.74
5×5	73×73	1 202.39	37.61	7.75	155.15	4.85
7×7	13×13	48.81	3.34	1.85	26.38	1.80
7×7	73×73	1 841.00	38.70	8.53	214.82	4.52
9×9	13×13	65.00	3.60	1.98	32.82	1.82
9×9	73×73	2 040.80	39.96	9.08	226.67	4.40

Tab.3 Despeckling results obtained by different algorithms to “head” phantom image with speckle noise

算法	$σ = 5$		$σ = 10$		$σ = 15$		$σ = 20$		$σ = 25$
算法	PSNR/dB	FSIM	PSNR/dB	FSIM	PSNR/dB	FSIM	PSNR/dB	FSIM	PSNR/dB	FSIM
斑点图像	33.55	0.813 3	27.45	0.721 3	23.72	0.660 0	21.50	0.625 5	19.94	0.604 2
SARD	31.53	0.974 3	29.93	0.941 7	27.07	0.814 2	24.69	0.739 1	22.85	0.689 4
SRBF	25.45	0.863 1	24.72	0.838 7	23.31	0.812 3	23.01	0.799 0	22.31	0.790 9
TBNLMF	38.88	0.957 4	32.66	0.894 6	28.66	0.855 5	25.40	0.827 1	22.07	0.796 7
OBNLMF	38.74	0.945 8	32.42	0.872 7	28.33	0.801 9	25.41	0.737 8	23.14	0.701 2
WRNLMF	39.89	0.967 3	33.66	0.918 6	30.18	0.885 5	28.13	0.851 7	25.82	0.830 5
IBNLMF	40.39	0.974 9	34.16	0.942 4	30.42	0.920 6	28.14	0.859 4	25.64	0.851 8
MF-NLMF	41.20	0.985 9	35.70	0.968 2	31.86	0.944 0	29.27	0.940 3	26.55	0.930 8

Tab.3 Despeckling results obtained by different algorithms to “head” phantom image with speckle noise

算法	$σ = 5$		$σ = 10$		$σ = 15$		$σ = 20$		$σ = 25$
算法	PSNR/dB	FSIM	PSNR/dB	FSIM	PSNR/dB	FSIM	PSNR/dB	FSIM	PSNR/dB	FSIM
斑点图像	33.55	0.813 3	27.45	0.721 3	23.72	0.660 0	21.50	0.625 5	19.94	0.604 2
SARD	31.53	0.974 3	29.93	0.941 7	27.07	0.814 2	24.69	0.739 1	22.85	0.689 4
SRBF	25.45	0.863 1	24.72	0.838 7	23.31	0.812 3	23.01	0.799 0	22.31	0.790 9
TBNLMF	38.88	0.957 4	32.66	0.894 6	28.66	0.855 5	25.40	0.827 1	22.07	0.796 7
OBNLMF	38.74	0.945 8	32.42	0.872 7	28.33	0.801 9	25.41	0.737 8	23.14	0.701 2
WRNLMF	39.89	0.967 3	33.66	0.918 6	30.18	0.885 5	28.13	0.851 7	25.82	0.830 5
IBNLMF	40.39	0.974 9	34.16	0.942 4	30.42	0.920 6	28.14	0.859 4	25.64	0.851 8
MF-NLMF	41.20	0.985 9	35.70	0.968 2	31.86	0.944 0	29.27	0.940 3	26.55	0.930 8

Fig. 3 Despeckling results of different despeckling algorithms to “head” phantom image with speckle noise when σ=10

Fig. 4 Speckle removal results of different despeckling algorithms for Field Ⅱ simulated “cyst” ultrasound image

Tab. 4 Despeckling quantitative results of different despeckling algorithms for Field Ⅱ simulated “cyst” ultrasound image unit： dB

算法	第一个区域		第二个区域		第三个区域
算法	CNR	SNR	CNR	SNR	CNR	SNR
噪声图像	0.046 8	0.112 4	0.019 0	0.059 3	0.012 0	0.156 3
SARD	0.112 8	0.275 6	0.026 7	0.082 4	0.047 6	0.590 1
SRBF	0.093 3	0.238 1	0.023 9	0.074 1	0.022 6	0.313 4
TBNLMF	0.186 6	0.512 8	0.028 7	0.084 8	0.078 2	0.942 0
OBNLMF	0.079 4	0.200 4	0.026 0	0.080 8	0.057 4	0.658 5
WRNLMF	0.384 1	1.080 8	0.033 5	0.097 8	0.164 2	2.275 3
IBNLM	0.396 2	1.344 6	0.038 6	0.117 1	0.222 6	3.775 9
MF-NLMF	0.420 2	1.472 1	0.039 1	0.118 7	0.240 7	3.784 0

Fig. 5 Speckle removal results of MF-NLMF algorithm for three real ultrasound images

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Speckle removal algorithm for ultrasonic image based on multi-scale fast non-local means filtering

基于多尺度快速非局部平均滤波的超声图像去斑算法

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