基于Anscombe变换的X射线图像序列盲源分离降噪

沈帆; 李翰林; 孙斌

doi:10.3788/OPE.20202801.0244

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基于Anscombe变换的X射线图像序列盲源分离降噪

信息科学 | 更新时间：2020-08-13

- 基于Anscombe变换的X射线图像序列盲源分离降噪
- X-ray image denoising using blind source separation in anscombe domain
- 光学精密工程 2020年28卷第1期页码：244-250
- 作者机构：
  
  1.南京邮电大学电子与光学工程学院、微电子学院，江苏南京 210023；
  2.南京邮电大学自动化学院，江苏南京 210023
- 作者简介：
  
  [ "沈帆 (1995-)，男，江苏盐城人，硕士研究生，主要从事X射线成像及图像降噪算法研究。E-mail:18751965190s@sina.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61801239);中央高校基本科研业务费专项资金资助项目(30918014106);南京邮电大学校企合作项目资助(2018外002);南京邮电大学校企合作项目资助(2019外157)
- DOI：10.3788/OPE.20202801.0244
  中图分类号： TP391.41
- 收稿日期：2019-07-08，
  
  录用日期：2019-8-29，
  
  纸质出版日期：2020-01-25
- 稿件说明：
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沈帆, 李翰林, 孙斌. 基于Anscombe变换的X射线图像序列盲源分离降噪[J]. 光学精密工程, 2020,28(1):244-250.

Fan SHEN, Han-lin LI, Bin SUN. X-ray image denoising using blind source separation in anscombe domain[J]. Optics and precision engineering, 2020, 28(1): 244-250.
沈帆, 李翰林, 孙斌. 基于Anscombe变换的X射线图像序列盲源分离降噪[J]. 光学精密工程, 2020,28(1):244-250. DOI： 10.3788/OPE.20202801.0244.

Fan SHEN, Han-lin LI, Bin SUN. X-ray image denoising using blind source separation in anscombe domain[J]. Optics and precision engineering, 2020, 28(1): 244-250. DOI： 10.3788/OPE.20202801.0244.

摘要

为降低泊松噪声对X射线图像质量的影响，本研究提出一种采用非线性主分量分析(NLPCA)对X射线图像序列进行盲源分离的降噪方法。该降噪方法首先采样一序列X射线图像，并通过Anscombe变换将图像中泊松噪声转化为高斯噪声；然后将每张含噪声图像视为噪声分量和信号分量的组合，进而采用NLPCA将信号分量和噪声分量分离达到降噪目的；最后通过Anscombe逆变换获取最终降噪图像。研究结果表明：当序列中含噪声图像张数从2增加到50时，提出的降噪方法可以将Shepp-Logan头模型含噪声图像的PSNR值由28.289 4 dB提高到37.267 8 dB、SSIM值由0.700 7提高到0.963 8。相比较常用的降噪算法，提出的降噪方法在有效消除X射线图像中泊松噪声的同时，使图像中细节轮廓保留更完整。

Abstract

To remove the Poisson noise from the X-ray images

in this paper

it was proposed that noise was reduced by using Nonlinear Principal Component Analysis (NLPCA) from the X-ray image sequence. At first

an X-ray image sequence was sampled and the Poisson noise in images was converted into Gaussian noise through Anscombe transform; every noisy image was regarded as a combination of the noise components and the signal component

and then NLPCA was used to separate the signal component from the noise components to reduce noise; the final denoised image was obtained by using Anscombe inverse transform. The results show that

when the number of noisy images in the sequence increases from 2 to 50

the proposed denoising method increases the noisy Shepp-Logan image's PSNR value from 28.289 4 dB to 37.267 8 dB and increases the SSIM value from 0.700 7 to 0.963 8. Compared with other denoising methods

the proposed denoising method can preserve more image details while reducing the Poisson noise.

关键词

Keywords

references

KANG E, MIN J, YE J C. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction[J]. Medical Physics , 2017, 44(10): 360-375.

WANG C, YANG J. Poisson noise removal of images on graphs using tight wavelet frames[J]. The Visual Computer , 2017, 34(10): 1357-5369.

费彬, 孙京阳, 张俊举, 等.基于稀疏处理的多能X射线分离成像[J].光学精密工程, 2017, 25(4): 574-579.

FEI B, SUN J Y, ZHANG J J, et al .. Separation of multi-energy X-ray imaging based on sparse processing[J]. Opt. Precision Eng., 2017, 25(4): 574-579. (in Chinese).

SELESNICK IW, SENDUR L. Bicariate shrinkage functions for wavelet-based denoising exploiting interscale dependency[J]. IEEE Transactions on signal processing , 2002, 50(11): 2744-2756.

骆乐, 吴长强, 林杰, 等.基于光子计数激光雷达的时域去噪[J].光学精密工程, 2018, 26(5): 1175-1180.

LUO L, WU CH Q, LIN J, et al .. Time-domain denoising based on photon-counting LiDAR[J]. Opt. Precision Eng ., 2018, 26(5): 1175-1180. (in Chinese).

MA L, MOISAN L, YU J, et al .. A dictionary learning approach for poisson image deblurring[J]. IEEE Transactions on Medical Imaging , 2013, 32(7): 1277-1289.

孙京阳, 喻春雨, 董仕佳.非局部均值噪声预测的独立成分分析降噪研究[J].光学精密工程, 2018, 26(2): 511-516.

SUN J Y, YU CH Y, DONG SH J, et al .. Noise reduction of independent component analysis based on NLmeans noise prediction[J]. Opt. Precision Eng. , 2018, 26(2): 511-516. (in Chinese).

MANDUCA A, YU L, TRZASKO J D, et al .. Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT[J]. Medical Physics , 2009, 36(11): 4911-4919.

毕一鸣, 马建华, 刘楠, 等.基于Anscombe变换域BM3D滤波的低剂量CT重建[J].计算机工程与应用, 2010, 46(13): 216-220.

BI Y M, MA J H, LIU N, et al .. Anscombe transform and BM3D filtering based projection restoration for low-does CT Reconstruction[J]. Computer Engineering and Applications , 2010, 46(13): 216-220. (in Chinese).

张倩颖, 谢晓振.加权Schatten范数低秩表示的高光谱图像恢复[J].光学精密工程, 2019, 27(2): 421-432.

ZHANG Q Y, XIE X ZH. Hyperspectral image restoration via weighted Schatten norm low-rank representation[J]. Opt. Precision Eng ., 2019, 27(2): 421-432.(in Chinese).

REVELL L J, KLAUS S, EUGENIO V, et al .. Graphs in phylogenetic comparative analysis: Anscombe's quartet revisited[J]. Methods in Ecology and Evolution , 2018, 9(10): 2145-2154.

BORGES L R, AZZARI L, BAKIC P R, et al .. Restoration of low-dose digital breast tomosynthesis[J]. Measurement Science and Technology , 2018, 29(6): 064003.

GROTAS S, YAKOBY Y, GERA I, et al .. Power systems topology and state estimation by graph blind source separation[J]. IEEE Transactions On Signal Processing , 2018, 67(8): 2036-2051.

戚佳佳, 顾国华, 陈远金, 等.基于贝叶斯自适应估计的光子计数集成成像[J].光学精密工程, 2018, 26(3): 565-571.

QI J J, GU G H, CHEN Y J, et al .. Photon counting integral imaging based on adaptive Bayesian estimation[J]. Opt. Precision Eng. , 2018, 26(3): 565-571.(in Chinese).

FENG P, WANG W, NAQVI S M, et al .. Adaptive retrodiction particle phd filter for multiple human tracking[J]. IEEE Signal Processing Letters , 2016, 23(11): 1592-1596.

GRANIC A, DAVIES K, MARTIN-RUIZ C, et al .. Grip strength and inflammatory biomarker profiles in very old adults[J]. Age and Ageing , 2017, 46(6): 976-982.

王迪, 司玉娟, 刘通, 等.基于主成分分析进行特征融合的心拍分类[J].光学精密工程, 2015, 23(10): 453-458.

WANG D, SI Y J, LIU T, et al .. Heart beat classification based on feature fusion by principle component analysis[J]. Opt. Precision Eng ., 2015, 23(10): 453-458. (in Chinese).

QI H L, ZHOU L H, XU Y, et al .. A Bayesian-map method based on TV for CT image reconstruction from sparse and limited data[J]. Chinese Journal of Biomedical Engineering , 2017, 26(2): 82-88.

YU C Y, LI Y, FEI B, et al .. Blind source separation based x-ray image denoising from an image sequence[J]. Review of Scientific Instruments , 2015, 86(9): 093701.

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