多邻域中值滤波算法在医学图像中的应用
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摘要
目的:克服现有的滤波算法在噪声检测与噪声滤除上的缺陷,进一步提高去噪性能.方法:提出了多邻域中值滤波算法,对噪声检测和噪声滤除的方法分别进行改进.算法用邻域中的灰度极值进行噪声检测,对检测出来的可疑噪声,用邻域的中值作进一步的噪声检测.对噪声像素,在其邻近的9个邻域中分别求出信号像素的中值,然后用所有中值的中值作为噪声像素新的灰度.结果:基于医学图像的实验结果证明,相对于现有的算法,所提出的算法的去噪图像更加清晰,去噪结果的PSNR和SSIM值更高.结论:所提出的算法在彻底去除噪声的同时,很好地保持了图像的纹理边缘和细节,相对于现有的滤波算法,具有更好的去噪性能.
Objective: To overcome the drawbacks of existing filters in noise detection and removal, and further improve the denoising performance. Methods: We propose a multi-neighborhood median filter to improve the techniques of noise detection and removal.This proposed filter first performs noise detection by the extreme intensity values of neighborhood,and then performs further noise detection by the median of neighborhood. For noisy pixel, it searches for the medians of nine neighboring neighborhood respectively, and then take the median of all medians as the new intensity of noisy pixel. Results: The experimental results with medical images,show that compared to the existing filters, the propose method obtains clearer denoised images, and achieves higher PSNR and SSIM values. Conclusion: The proposed method can preserve the edges and details of image very well, while removing the noises thoroughly; comparing to the existing filters, the proposed method shows better denoising performance.
Objective: To overcome the drawbacks of existing filters in noise detection and removal, and further improve the denoising performance. Methods: We propose a multi-neighborhood median filter to improve the techniques of noise detection and removal.This proposed filter first performs noise detection by the extreme intensity values of neighborhood,and then performs further noise detection by the median of neighborhood. For noisy pixel, it searches for the medians of nine neighboring neighborhood respectively, and then take the median of all medians as the new intensity of noisy pixel. Results: The experimental results with medical images,show that compared to the existing filters, the propose method obtains clearer denoised images, and achieves higher PSNR and SSIM values. Conclusion: The proposed method can preserve the edges and details of image very well, while removing the noises thoroughly; comparing to the existing filters, the proposed method shows better denoising performance.
引文
[1] BALASUBRAMANIAN G, CHILAMBUCHELVAN A, VIJAYAN S, et al. An extremely fast adaptive high-performance filter to remove salt and pepper noise using overlapping medians in images[J]. Imaging Science Journal, 2016, 64(5): 241-252.
[2] ZHANG J, WU J, COATRIEUX J L, et al. A correlation based strategy for the acceleration of nonlocal means filtering algorithm[J]. Mathematical Problems in Engineering, 2016, 2016: 1-7.
[3] SAID A B, HADJIDJ R, MELKEMI K E, et al. Multispectral image denoising with optimized vector non-local mean filter[J]. Digital Signal Processing, 2016, 58:115-126.
[4] WANG X, SHEN S, SHI G, et al. Iterative non-local means filter for salt and pepper noise removal[J]. Journal of Visual Communication and Image Representation, 2016, 38: 440-450.
[5] CHARALAMPIDIS D. Steerable weighted median filters[J]. IEEE Transaction on Image Processing, 2010, 19(4): 882-894.
[6] RAMAMOORTHY K, CHELLADURAI T, SUNDARARAJAN P N. Edge Analysis for noise suppression in ultrasound kidney images using weighted median filter[J]. International Journal of Systems Signal Control and Engineering Application, 2014, 7(2): 36-42.
[7] PARK H H. Making joint-histogram-based weighted median filter much faster[J]. Transactions on Information and Systems, 2015, E98-D(3): 721-725.
[8] WANG G H, LI D H, PAN W M, et al. Modified switching median filter for impulse noise removal[J]. Signal Processing, 2010, 90(2010):3213-3218.
[9] WANG W, LU P Z. An efficient switching median filter based on local outlier factor[J]. IEEE Signal Processing Letters, 2011,18(10): 551-554.
[10] 陆玉婧, 李海燕, 费勤水, 等. 基于简化的PCNN在超声乳腺癌图像去噪方面的应用[J]. 生物医学工程研究, 2013, 32(2):80-83. LU Y J,LI H Y,FEI Q S, et al. Applications in the denoising of ultrasound breast image based on simplified PCNN[J].Journal of Biomedical Engineering Research, 2013, 32(2):80-83.
[11] 张思杰, 喻洁, 徐鹏, 等. 加密图像的开关中值滤波去噪算法[J]. 重庆大学学报, 2014, 37(2):113-116. ZHAN S J, YU J, XU P, et al. The switching median filter denoising algorithm for encrypted images[J]. Journal of Chongqing University, 2013, 32(2):80-83.
[12] 高振清, 庄建军. 基于参数自调整开关中值滤波的图像去噪方法研究[J]. 激光杂志, 2015, 36(10):50-54. GAO Z Q, ZHUANG J J. Research of image denoising methods based on parameter self-adjusting switch median filtering[J].Laser Journal, 2015, 36(10):50-54.
[13] 宗永胜, 胡晓辉, 张荣光. 一种自适应双阈值中值滤波方法[J]. 小型微型计算机系统, 2017, 38(7):1642-1647. ZONG Y S, HU X H, ZHANG R G. Adaptive dual-threshold median filtering method[J]. Journal of Chinese Computer Systems, 2017, 38(7):1642-1647.
[14] 周萌萌, 任子晖, 周旋旋. 基于窗口的自适应中值滤波算法[J]. 电视技术, 2015, 39(22):15-18. ZHOU M M, REN Z H, ZHOU X X. Adaptive median filtering algorithm based on window[J]. Video Engineering, 2015, 39(22):15-18.
[15] 张辰锐, 樊养余, 刘姝, 等. 一种基于模糊决策的开关矢量中值滤波方法[J]. 计算机应用研究, 2013, 30(12):3870-3873. ZHANG C R, FAN Y Y, LIU S, et al. Switching vector median filter based on fuzzy decision[J]. Application Research of Computers, 2013, 30(12):3870-3873.
[16] BALASUBRAMANIAN G, CHILAMBUCHELVAN A, VIJAYAN S, et al. Probabilistic decision based filter to remove impulse noise using patch else trimmed median[J]. AEU-International Journal of Electronics and Communications 2016, 70(4): 471-481.
[17] 李迅波, 蒋东升, 王振林. 梯度相似性的椒盐图像加权中值滤波算法[J]. 电子科技大学学报, 2012, 41(1):114-119. LI X B, JIANG D S, WANG Z L. Weighted median filtering of im based on grads similarity[J].Journal of University of Electronic Science and Technology of China, 2012, 41(1):114-119.
[18] 史再峰, 许泽昊, 庞科,等.一种基于开关型加权中值滤波的随机脉冲噪声去除方法[J]. 南开大学学报(自然科学版),2018,51(4):63-67. SHI Z F, XU Z H, PANG K, et al. A random-value impulse noise removal method based on switching weighted median filter[J]. Acta Scientiarum Naturalium Universitatis Nankaiensis, 2018,51(4):63-67.
[19] FARAGALLAHAC O S, IBRAHEMB H M. Adaptive switching weighted median filter framework for suppressing salt-and-pepper noise. AEU-International Journal of Electronics and Communications, 2016, 70(8): 1034-1040.
[20] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004, 13(4):600-612.
[2] ZHANG J, WU J, COATRIEUX J L, et al. A correlation based strategy for the acceleration of nonlocal means filtering algorithm[J]. Mathematical Problems in Engineering, 2016, 2016: 1-7.
[3] SAID A B, HADJIDJ R, MELKEMI K E, et al. Multispectral image denoising with optimized vector non-local mean filter[J]. Digital Signal Processing, 2016, 58:115-126.
[4] WANG X, SHEN S, SHI G, et al. Iterative non-local means filter for salt and pepper noise removal[J]. Journal of Visual Communication and Image Representation, 2016, 38: 440-450.
[5] CHARALAMPIDIS D. Steerable weighted median filters[J]. IEEE Transaction on Image Processing, 2010, 19(4): 882-894.
[6] RAMAMOORTHY K, CHELLADURAI T, SUNDARARAJAN P N. Edge Analysis for noise suppression in ultrasound kidney images using weighted median filter[J]. International Journal of Systems Signal Control and Engineering Application, 2014, 7(2): 36-42.
[7] PARK H H. Making joint-histogram-based weighted median filter much faster[J]. Transactions on Information and Systems, 2015, E98-D(3): 721-725.
[8] WANG G H, LI D H, PAN W M, et al. Modified switching median filter for impulse noise removal[J]. Signal Processing, 2010, 90(2010):3213-3218.
[9] WANG W, LU P Z. An efficient switching median filter based on local outlier factor[J]. IEEE Signal Processing Letters, 2011,18(10): 551-554.
[10] 陆玉婧, 李海燕, 费勤水, 等. 基于简化的PCNN在超声乳腺癌图像去噪方面的应用[J]. 生物医学工程研究, 2013, 32(2):80-83. LU Y J,LI H Y,FEI Q S, et al. Applications in the denoising of ultrasound breast image based on simplified PCNN[J].Journal of Biomedical Engineering Research, 2013, 32(2):80-83.
[11] 张思杰, 喻洁, 徐鹏, 等. 加密图像的开关中值滤波去噪算法[J]. 重庆大学学报, 2014, 37(2):113-116. ZHAN S J, YU J, XU P, et al. The switching median filter denoising algorithm for encrypted images[J]. Journal of Chongqing University, 2013, 32(2):80-83.
[12] 高振清, 庄建军. 基于参数自调整开关中值滤波的图像去噪方法研究[J]. 激光杂志, 2015, 36(10):50-54. GAO Z Q, ZHUANG J J. Research of image denoising methods based on parameter self-adjusting switch median filtering[J].Laser Journal, 2015, 36(10):50-54.
[13] 宗永胜, 胡晓辉, 张荣光. 一种自适应双阈值中值滤波方法[J]. 小型微型计算机系统, 2017, 38(7):1642-1647. ZONG Y S, HU X H, ZHANG R G. Adaptive dual-threshold median filtering method[J]. Journal of Chinese Computer Systems, 2017, 38(7):1642-1647.
[14] 周萌萌, 任子晖, 周旋旋. 基于窗口的自适应中值滤波算法[J]. 电视技术, 2015, 39(22):15-18. ZHOU M M, REN Z H, ZHOU X X. Adaptive median filtering algorithm based on window[J]. Video Engineering, 2015, 39(22):15-18.
[15] 张辰锐, 樊养余, 刘姝, 等. 一种基于模糊决策的开关矢量中值滤波方法[J]. 计算机应用研究, 2013, 30(12):3870-3873. ZHANG C R, FAN Y Y, LIU S, et al. Switching vector median filter based on fuzzy decision[J]. Application Research of Computers, 2013, 30(12):3870-3873.
[16] BALASUBRAMANIAN G, CHILAMBUCHELVAN A, VIJAYAN S, et al. Probabilistic decision based filter to remove impulse noise using patch else trimmed median[J]. AEU-International Journal of Electronics and Communications 2016, 70(4): 471-481.
[17] 李迅波, 蒋东升, 王振林. 梯度相似性的椒盐图像加权中值滤波算法[J]. 电子科技大学学报, 2012, 41(1):114-119. LI X B, JIANG D S, WANG Z L. Weighted median filtering of im based on grads similarity[J].Journal of University of Electronic Science and Technology of China, 2012, 41(1):114-119.
[18] 史再峰, 许泽昊, 庞科,等.一种基于开关型加权中值滤波的随机脉冲噪声去除方法[J]. 南开大学学报(自然科学版),2018,51(4):63-67. SHI Z F, XU Z H, PANG K, et al. A random-value impulse noise removal method based on switching weighted median filter[J]. Acta Scientiarum Naturalium Universitatis Nankaiensis, 2018,51(4):63-67.
[19] FARAGALLAHAC O S, IBRAHEMB H M. Adaptive switching weighted median filter framework for suppressing salt-and-pepper noise. AEU-International Journal of Electronics and Communications, 2016, 70(8): 1034-1040.
[20] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004, 13(4):600-612.