Combination of improved diffusion and bilateral filtering for low-dose CT reconstruction

doi:10.11772/j.issn.1001-9081.2016.04.1100

Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (4): 1100-1105.DOI: 10.11772/j.issn.1001-9081.2016.04.1100

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Combination of improved diffusion and bilateral filtering for low-dose CT reconstruction

ZHANG Pengcheng¹, ZHANG Quan¹, ZHANG Fang¹, CHEN Yan¹, HAN Jianning¹, HAO Huiyan¹, GUI Zhiguo^1,2

1. State Key Laboratory for Electronic Measurement Technology(North University of China), Taiyuan Shanxi 030051, China;
2. Key Laboratory of Instrumentation Science and Dynamic Measurement, Ministry of Education(North University of China), Taiyuan Shanxi 030051, China

Received:2015-09-29 Revised:2015-12-30 Online:2016-04-08 Published:2016-04-10
Supported by:
This work is partially supported by the National Nature Science Foundation of China (61271357), the Natural Science Foundation of Shanxi Province (2015011046), the Science Foundation Plan in the North University of China in 2013.

结合改进扩散和双边滤波的低剂量CT重建

张鹏程¹, 张权¹, 张芳¹, 陈燕¹, 韩建宁¹, 郝慧艳¹, 桂志国^1,2

1. 电子测试技术国家重点实验室(中北大学), 太原 030051;
2. 仪器科学与动态测试教育部重点实验室(中北大学), 太原 030051

通讯作者: 桂志国
作者简介:张鹏程(1984-),男,内蒙古巴彦淖尔人,讲师,博士,主要研究方向:图像处理与重建、剂量计算、方案优化; 张权(1974-),男,山西大同人,副教授,博士研究生,主要研究方向:图像处理、科学可视化; 张芳(1989-),女,山西朔州人,硕士研究生,主要研究方向:基于低剂量CT的图像重建; 陈燕(1982-),女,山西太原人,讲师,博士研究生,主要研究方向:信号与信息处理、图像降噪与增强; 韩建宁(1980-),男,山西晋城人,副教授,博士研究生,主要研究方向:图像处理、声信号处理; 郝慧艳(1980-),女,山西武乡人,副教授,博士,主要研究方向:图像处理、声信号处理; 桂志国(1972-),男,天津人,教授,博士,主要研究方向:信号与信息处理、图像处理和识别、图像重建。
基金资助:
国家自然科学基金资助项目(61271357);山西省自然科学基金资助项目(2015011046);中北大学2013年校科学基金资助项目。

Abstract

Abstract: Median Prior (MP) reconstruction algorithm combined with nonlocal means fuzzy diffusion and extended neighborhood bilateral filter was proposed to reduce the streak artifacts in low-dose Computed Tomography (CT) reconstruction. In the new algorithm, the nonlocal means fuzzy diffusion method was used to improve the median of the prior distribution Maximum A Posterior (MAP) reconstruction algorithm at first, which reduced the noise in the reconstruction image; then, the bilateral filtering method based on the expended neighborhood was applied to preserve the edges and details of the reconstruction image and improve the Signal-to-Noise Ratio (SNR). The Shepp-Logan model and the thorax phantom were used to test the effectiveness of the proposed algorithm. The experimental results show that the proposed method has the smaller values of the Normalized Mean Square Distance (NMSD) and Mean Absolute Error (MAE) and the highest SNR (10.20 dB and 15.51 dB, respectively) in the two experiment images, compared with Filtered Back Projection (FBP), Median Root Prior (MRP), NonLocal Mean MP (NLMMP) and NonLocal Mean Bilateral Filter MP (NLMBFMP) algorithms. The experimental results show that the proposed reconstruction algorithm can reduce noise while keeping the edges and details of the image, which improves the deterioration problem of the low-dose CT image and obtains the image with higher SNR and quality.

Key words: low-dose Computed Tomography (CT), nonlocal means, fuzzy diffusion, extended neighborhood, bilateral filtering

摘要： 针对低剂量计算机断层扫描(CT)重建图像时出现明显条形伪影的现象,提出一种结合非局部均值模糊扩散和扩展邻域双边滤波的中值先验(MP)重建算法。首先,使用基于非局部均值模糊扩散方法对中值先验分布的最大后验(MAP)重建算法进行改进,以减少重建图像中的噪声;然后,采用基于扩展邻域的双边滤波方法对重建图像进行处理,以保持图像的边缘和细节信息,进一步提高重建图像的信噪比。采用Shepp-Logan模型和胸腔模型来验证算法的有效性,实验结果表明,与滤波反投影(FBP)、中值根先验(MRP)、非局部均值模糊扩散的MP重建(NLMMP)算法和非局部均值双边滤波的MP重建(NLMBFMP)算法相比,所提新算法的归一化均方距离和均方绝对误差最小,且信噪比最高,分别为10.20 dB和15.51 dB。该重建算法可以在对重建图像进行降噪的同时保持了图像的边缘和细节信息,改善了低剂量CT图像质量退化的问题,获得高信噪比和高质量的重建图像。

关键词: 低剂量计算机断层扫描, 非局部均值, 模糊扩散, 扩展邻域, 双边滤波

CLC Number:

TP391.7

ZHANG Pengcheng, ZHANG Quan, ZHANG Fang, CHEN Yan, HAN Jianning, HAO Huiyan, GUI Zhiguo. Combination of improved diffusion and bilateral filtering for low-dose CT reconstruction[J]. Journal of Computer Applications, 2016, 36(4): 1100-1105.

张鹏程, 张权, 张芳, 陈燕, 韩建宁, 郝慧艳, 桂志国. 结合改进扩散和双边滤波的低剂量CT重建[J]. 计算机应用, 2016, 36(4): 1100-1105.

References

[1] BIAN J G, YANG K, BOONE J M, et al. Investigation of iterative image reconstruction in low-dose breast CT[J]. Physics in Medicine and Biology, 2014, 59(11):2659-2685.
[2] 王丽艳, 韦志辉. 低剂量CT的线性Bregman迭代重建算法[J]. 电子与信息学报, 2013, 35(10):2418-2424.(WANG L Y, WEI Z H. Linearized Bregman iterations for low-dose CT reconstruction[J]. Journal of Electronics and Information, 2013, 35(10):2418-2424.)
[3] CHEN Y, GAO D Z, NIE C, et al. Bayesian statistical reconstruction for low-dose X-ray computed tomography using an adaptive weighting local nonprior[J]. Computerized Medical Imaging and Graphics, 2009, 33(7):495-500.
[4] LI T, LI X, WANG J, et al. Nonlinear sinogram smoothing for low-dose X-ray CT[J]. IEEE Transactions on Nuclear Science, 2004, 51(5):2505-2513.
[5] MA J H, ZHANG H, GAO Y, et al. Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior[J]. Physics in Medicine and Biology, 2012, 57(22):7519-7542.
[6] 何玲君, 潘晋孝, 孔慧华. 自适应正则MAP的CT图像重建方法研究[J]. 计算机工程与应用, 2011, 47(28):198-200.(HE L J, PAN J X, KONG H H. Adaptive regularized MAP of CT image reconstruction method[J]. Computer Engineering and Applications, 2011, 47(28):198-200.)
[7] 张芳, 崔学英, 张权, 等. 基于变指数各向异性扩散和非局部的最大似然期望最大低剂量CT重建算法[J]. 计算机应用, 2014, 34(1):3605-3608, 361.(ZHANG F, CUI X Y, ZHANG Q, et al. MLEM low-dose CT reconstruction algorithm based on variable exponent anisotropic diffusion and non-locality[J]. Journal of Computer Applications, 2014, 34(1):3605-3608, 361.)
[8] 李晓红, 张权, 刘祎, 等. 基于小波收缩和正逆扩散结合的优质中值先验图像重建算法[J]. 计算机应用, 2012, 32(12):3357-3360.(LI X H, ZHANG Q, LIU Y, et al. High quality median prior image reconstruction algorithm based on wavelet shrinkage and forward-and-backward diffusion[J]. Journal of Computer Applications, 2012, 32(12):3357-3360.)
[9] SHEN M F, SU Z F, CHEN T T, et al. An algorithm based on compound anisotropic diffusion for image denoising[J]. Applied Mechanics and Materials, 2014, 687-691:3927-3931.
[10] BARBU T. Robust anisotropic diffusion scheme for image noise removal[J]. Procedia Computer Science, 2014, 35:522-530.
[11] 颜建华. 正电子发射断层图像重建算法研究[D]. 武汉:华中科技大学, 2007:54-70.(YAN J H. Investigation of positron emission tomography image reconstruction[D]. Wuhan:Huazhong University of Science and Technology, 2007:54-70.)
[12] 张芳, 张权, 崔学英, 等.基于小波和非局部的全变差中值先验重建算法[J]. 计算机工程与设计, 2015, 36(8):2152-2156.(ZHANG F, ZHANG Q, CUI X Y, et al. Total variation median prior reconstruction algorithm based on wavelet and nonlocal[J]. Computer Engineering and Design, 2015, 36(8):2152-2156.)
[13] PERONA P, MALIK J. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1990, 12(7):629-639.
[14] CHEN R C, YU P T. Fuzzy selection filters for image restoration with neural learning[J]. IEEE Transactions on Signal Processing, 1999, 47(5):1446-1450.
[15] SHIVANI S, GURSHARANJEET S K. An image denoising framework with multi-resolution bilateral filtering and normal shrink approach[J]. Research Journal of Applied Sciences, 2014, 6(7):1240-1246.
[16] ANNA G R, MATTHIAS K, ROBERT W, et al. An FPGA-based fully synchronized design of a bilateral filter for real-time image denoising[J]. IEEE Transactions on Industrial Electronics, 2014, 8(61):4093-4104.
[17] JOSEPH S P, JOSHIN J M, CHANDRASEKHAR K. MR image enhancement using an extended neighborhood filter[J]. Journal of Visual Communication and Image Representation, 2014, 25(7):1604-1615.
[18] ALENIUS S, RUOTSALAINEN U. Bayesian image reconstruction for emission tomography based on median root prior[J]. European Journal of Nuclear Medicine, 1997, 24(3):258-265.
[19] HSIAO I T, RANGARAJAN A, GINDI G. A new convex edge-preserving median prior with application to tomography[J]. IEEE Transactions on Medical Imaging, 2003, 22(5):580-585.
[20] BUADES A, COLL B, MOREL J M. A review of image denoising algorithms, with a new one[J]. SIAM Journal on Multiscale Modeling & Simulation, 2005, 4(2):490-530.
[21] ZHANG Q, LIU Y, SHU H Z, et al. Application of regularized maximum likelihood algorithm in PET image reconstruction combined with nonlocal fuzzy anisotropic diffusion[J]. Optik-International Journal for Light and Electron Optics, 2013, 124(20):4561-4565.
[22] CUI X Y, ZHANG Q, SHANGGUAN H, et al. The adaptive sinogram restoration algorithm based on anisotropic diffusion by energy minimization for low-dose X-ray CT[J]. Optik-International Journal for Light and Electron Optics, 2014, 125(5):1694-1697.
[23] JI D J, HU C H, YANG H. Image reconstruction algorithm for in-line phase contrast imaging computed tomography with an improved anisotropic diffusion model[J]. Journal of X-ray Science and Technology, 2015, 3(23):311-320.
[24] ZHANG Y, ZHANG J Y, LU H B. Statistical sinogram smoothing for low-dose CT with segmentation-based adaptive filtering[J]. IEEE Transactions on Nuclear Science, 2010, 5(57):2587-2598.

Combination of improved diffusion and bilateral filtering for low-dose CT reconstruction

结合改进扩散和双边滤波的低剂量CT重建

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