低剂量CT图像的自适应广义总变分降噪算法

doi:10.11772/j.issn.1001-9081.2016.01.0243

计算机应用 ›› 2016, Vol. 36 ›› Issue (1): 243-247.DOI: 10.11772/j.issn.1001-9081.2016.01.0243

低剂量CT图像的自适应广义总变分降噪算法

何琳¹, 张权¹, 上官宏¹, 张芳¹, 张鹏程¹, 刘祎¹, 孙未雅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

收稿日期:2015-07-13 修回日期:2015-09-11 出版日期:2016-01-10 发布日期:2016-01-09
通讯作者: 桂志国(1972-),男,天津蓟县人,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
作者简介:何琳(1991-),女,山西运城人,硕士研究生,主要研究方向:图像处理与重建;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;上官宏(1988-),女,山西临汾人,博士研究生,主要研究方向:图像处理、医学图像重建;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;张鹏程(1984-),男,内蒙古巴彦淖尔人,讲师,博士,主要研究方向:剂量计算、方案优化;刘祎(1987-),女,河南睢县人,博士研究生,主要研究方向:图像处理、医学图像重建;孙未雅(1991-),女,河北遵化人,硕士研究生,主要研究方向:基于低剂量CT的图像重建。
基金资助:
国家自然科学基金资助项目(61071192,61271357,61171178);山西省国际合作项目(2013081035);山西省研究生优秀创新项目(2009011020-2,20123098);中北大学第十届研究生科技基金项目(20131035);山西省高等学校优秀青年学术带头人支持计划项目;中北大学2013年校科学基金计划项目。

Adaptive total generalized variation denoising algorithm for low-dose CT images

HE Lin¹, ZHANG Quan¹, SHANGGUAN Hong¹, ZHANG Fang¹, ZHANG Pengcheng¹, LIU Yi¹, SUN Weiya¹, GUI Zhiguo^1,2

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

Received:2015-07-13 Revised:2015-09-11 Online:2016-01-10 Published:2016-01-09
Supported by:
This work is partially supported by the National Nature Science Foundation of China (61071192, 61271357, 61171178), the International S & T Cooperation Program of Shanxi Province (2013081035), the Graduate Outstanding Innovative Projects of Shanxi Province (2009011020-2, 20123098), the Tenth Graduate Technology Fund Project of the North University (20131035), the Top Young Academic Leaders of Higher Learning Institutions of Shanxi Province, the Science Foundation Project of North University of China in 2013.

摘要/Abstract

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,提出一种自适应广义总变分(ATGV)降噪算法。该算法考虑了传统广义总变分(TGV)算法在降噪时模糊图像边缘信息的缺点,把可以有效区分图像平滑区和细节区的直觉模糊熵应用到传统TGV中,对图像的不同区域进行不同强度的去噪,从而达到保护图像细节的效果。该算法首先采用滤波反投影(FBP)算法得到低剂量CT重建图像;然后利用基于直觉模糊熵的边缘指示函数对传统TGV模型进行改进;最后用改进后的模型对重建图像进行降噪处理。采用Shepp-Logan模型和数字胸腔模型(thorax phantom)仿真低剂量CT重建图像来验证算法的有效性。实验结果表明,所提算法的归一化均方距离(NMSD)和归一化平均绝对距离(NAAD)均比总变分(TV)降噪算法和广义总变分(TGV)降噪算法小,且可分别获得26.90 dB和44.58 dB的峰值信噪比(PSNR)。该算法在去除条形伪影的同时可以较好地保持图像的边缘和细节信息。

关键词: 低剂量计算机断层扫描, 直觉模糊熵, 边缘指示函数, 总变分, 广义总变分

Abstract: A new denoising algorithm, Adaptive Total Generalized Variation (ATGV), was proposed for removing streak artifacts within the reconstructed image of low-dose Computed Tomography (CT). Considering the shortage that the traditional Total Generalized Variation (TGV) would blur the edge details, the intuitionistic fuzzy entropy which can distinguish the smooth and detail regions was introduced into the TGV algorithm. Different areas of the image were processed with different denoising intensities. As a result, the image details could be well preserved. Firstly, the Filtered Back Projection (FBP) algorithm was used to obtain a reconstructed image. Secondly, the edge indicator function based on intuitive fuzzy entropy was applied to improve the TGV algorithm. Finally, the new algorithm was employed to reduce the noise in the reconstructed image. The simulations of the low-dose CT image reconstruction for 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 algorithm has the smaller values of the Normalized Mean Square Distance (NMSD) and Normalized Average Absolute Distance (NAAD) in the two experiment images, compared with the Total Variation (TV) algorithm and TGV algorithm. Meanwhile, the two experiment images processed with the new method can obtain high Peak Signal-to-Noise Ratios (PSNR) of 26.90 dB and 44.58 dB, respectively. So the proposed algorithm can effectively preserve image details and edges, while reducing streak artifacts.

Key words: low-dose Computed Tomography (CT), intuitionistic fuzzy entropy, edge indicator function, Total Variation (TV), Total Generalized Variation (TGV)

中图分类号:

TP391.413

何琳, 张权, 上官宏, 张芳, 张鹏程, 刘祎, 孙未雅, 桂志国. 低剂量CT图像的自适应广义总变分降噪算法[J]. 计算机应用, 2016, 36(1): 243-247.

HE Lin, ZHANG Quan, SHANGGUAN Hong, ZHANG Fang, ZHANG Pengcheng, LIU Yi, SUN Weiya, GUI Zhiguo. Adaptive total generalized variation denoising algorithm for low-dose CT images[J]. Journal of Computer Applications, 2016, 36(1): 243-247.

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低剂量CT图像的自适应广义总变分降噪算法

Adaptive total generalized variation denoising algorithm for low-dose CT images

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