研究论文

用于磁共振图像灰度校正的CLIC改进模型

  • 郑慧 ,
  • 郭天 ,
  • 杨光 ,
  • 赵献策 ,
  • 谢海滨
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  • 1. 华东师范大学物理系, 上海市磁共振重点实验室, 上海 200062;
    2. 上海卡勒幅磁共振技术有限公司, 上海 201614

收稿日期: 2016-04-20

  修回日期: 2017-04-17

  网络出版日期: 2017-06-05

基金资助

国家高技术研究发展计划资助项目(2014AA123400).

Magnetic Resonance Image Intensity Inhomogeneity Correction Based on Coherent Local Intensity Clustering

  • ZHENG Hui ,
  • GUO Tian ,
  • YANG Guang ,
  • ZHAO Xian-ce ,
  • XIE Hai-bin
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  • 1. Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai 200062, China;
    2. Shanghai Colorful Magnetic Resonance Technology Co. Ltd., Shanghai 201614, China

Received date: 2016-04-20

  Revised date: 2017-04-17

  Online published: 2017-06-05

摘要

针对磁共振图像中存在的灰度不均匀问题,该文在灰度校正的连贯局部灰度聚类(coherent local intensityclustering,CLIC)模型的基础上,提出一种新的灰度校正算法.该算法通过引入图像边缘信息来更快寻找到组织边界,在CLIC模型中采用较大的高斯窗函数以保证偏场的光滑性,并结合分裂布雷格曼迭代来加速算法.将改进后的算法用于处理模拟和真实的磁共振图像,实验结果表明,使用该算法能够获得比使用CLIC模型更好的效果.

本文引用格式

郑慧 , 郭天 , 杨光 , 赵献策 , 谢海滨 . 用于磁共振图像灰度校正的CLIC改进模型[J]. 波谱学杂志, 2017 , 34(2) : 164 -174 . DOI: 10.11938/cjmr20170205

Abstract

We proposed a novel method for correcting intensity inhomogeneity in magnetic resonance images based on the coherent local intensity clustering (CLIC) model. The method used edge information to help identify tissue boundaries. A large Gaussian kernel was used to keep the bias field smooth. Split Bregman iteration was used to accelerate convergence. Phantom and in vivo images were used to evaluate the performance of the proposed method.

参考文献

[1] HAACKE E M, BROWN R W, THOMPSON M R,et al. Magnetic resonance imaging:Physical principles and sequence design[M]. New Jersey:John Wiley & Sons, 2014.
[2] 周康荣, 陈祖望. 体部磁共振成像[M]. 上海:上海医科大学出版社, 2000.
[3] VOVK U, PERNUS F, LIKAR B. A review of methods for correction of intensity inhomogeneity in MRI[J]. IEEE T Med Imaging, 2007, 26(3):405-421.
[4] DAWANT B M, ZIJDENBOS A P, MARGOLIN R A. Correction of intensity variations in MR images for computer-aided tissue classification[J]. IEEE T Med Imaging, 1993, 12(4):770-781.
[5] ERNST T, KREIS R, ROSS B D. Absolute quantitation of water and metabolites in the human brain. I. compartments and water[J]. J Magn Reson, 1993, 102(1):1-8.
[6] PRUESSMANN K P, WEIGER M, SCHEIDEGGER M B, et al. SENSE:sensitivity encoding for fast MRI[J]. Magn Reson Med, 1999, 42(5):952-962.
[7] MIHARA H, IRIGUCHI N, UENO S. A method of RF inhomogeneity correction in MR imaging[J]. Magn Reson Mater Phys, 1998, 7(2):115-120.
[8] HOU Z J. A review on MR image intensity inhomogeneity correction[J]. Int J Biomed Imaging, 2006, 2006(49515):1-11.
[9] BALAFAR M A, RAMLI A R, MASHOHOR S. A new method for MR grayscale inhomogeneity correction[J]. ArtifIntell Rev, 2010, 34(2):195-204.
[10] SLED J G, ZIJDENBOS A P, EVANS A C. A nonparametric method for automatic correction of intensity nonuniformity in MRI data[J]. IEEE T Med Imaging, 1998, 17(1):87-97.
[11] ZHUGE Y, UDUPA J K, LIU J, et al. Image background inhomogeneity correction in MRI via intensity standardization[J]. Comput Med Imag Grap, 2009, 33(1):7-16.
[12] GARCIA-SEBASTIAN M, FERN-NDEZ E, GRANA M, et al. A parametric gradient descent MRI intensity inhomogeneity correction algorithm[J]. Pattern Recogn Lett, 2007, 28(13):1657-1666.
[13] MAKSIMOVIC R, STANNKOVIC S, MILOVANOVIC D, et al. Computed tomography image analyzer:Segmentation applying active contour models——"snakes"[J]. Stud Health Technol Inform, 1999, 68:395-399.
[14] ZHANG S, SHE L H, WANG H Y, et al. Brain MR image segmentation and bias field estimation using coherent local and non-local spatial constraints[C]. Guiyang:Proceedings of the 25th Chinese Control and Decision Conference (CCDC), 2013.
[15] LI C M, XU C Y, ANDERSON A W, et al. MRI tissue classification and bias field estimation based on coherent local intensity clustering:A unified energy minimization framework[J]. Inf Process Med Imaging, 2009, 21(2):88-99.
[16] LI C M, HUANG R, DING Z H, et al. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI[J]. IEEE T Image Process, 2011, 20(7):2007-2016.
[17] OSHER S, SETHIAN J A. Fronts propagating with curvature-dependent speed:Algorithms based on Hamilton-Jacobi formulations[J]. J Comput Phys, 1988, 79(1):12-49.
[18] BREGMAN L M. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming[J]. USSR Comput Math Math Phys, 1967, 7:200-217.
[19] OSHER S, BURGER M, GOLDFARB D, et al. An iterative regularization method for total variation-based image restoration[J]. Multiscale Model Sim, 2006, 4(2):460-489.
[20] YIN W T, OSHER S, GOLDFARB D, et al. Bregman iterative algorithms for l1-minimization with applications to compressed sensing[J]. SIAM J Imaging Sci, 2008, 1(1):143-168.
[21] GOLDSTEIN T, OSHER S. The split bregman method for l1-regularized problems[J]. Siam J Imaging Sci, 2009, 2(2):323-343.
[22] SMITH D S, GORE J C, YANKEELOV T E, et al. Real-time compressive sensing MRI reconstruction using GPU computing and split Bregman methods[J]. Int J Biomed Imaging, 2012, 1-6.
[23] YANG Y Y, LI C M, KAO C Y, et al. Split Bregman method for minimization of region-scalable fitting energy for image segmentation[M]. Berlin:Springer Berlin Heidelberg, 2010.
[24] GOLDSTEIN T, BRESSON X, OSHER S. Geometric applications of the split Bregman method:Segmentation and surface reconstruction[J]. J Sci Comput, 2010, 45(1):272-293.
[25] YANG Y Y, ZHAO Y, WU B Y. Split Bregman method for minimization of fast multiphase image segmentation model for inhomogeneous images[J]. J Optimiz Theory App, 2014, 166(1):285-305.
[26] LIKAR B, VIERGEVER M A, PERNUS F. Retrospective correction of MR intensity inhomogeneity by information minimization[J]. IEEE T Med Imaging, 2001, 20(12):1398-1410.
[27] LI C M, XU C Y, GUI C F, et al. Level set evolution without re-initialization:A new variational formulation[J]. CVPR'05, 2005. doi:10.1109/CVPR.2005.213.
[28] 杨云云. 基于Split Bregman方法的快速图像分割模型的研究[D]. 哈尔滨:哈尔滨工业大学, 2012.
[29] BRESSON X, ESEDOGLU S, VANDERGHEYNST P, et al. Fast global minimization of the active contour/snake model[J]. J Math Imaging Vis, 2007, 28(2):151-167.
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