Share：
Share this content in WeChat
X
[Chinese][PDF]702103
• Review •
Progress of magnetic resonance image on denoising
WANG Hao  KANG Xiao-dong  LIU Ling-ling  GENG Jia-jia  GUO Jun 

DOI:10.3969/j.issn.1674-8034.2012.03.015.


[Abstract] Magnetic resonance imaging (MRI) is of great importance in medical diagnosis, and denoise is one of the basic problems in MR image processing and analysis. Therefore, development of methods for eliminating image noise in MRI has important clinical significance and application value. In this paper, denoise in the space domain, transform domain and by multi-scale analysis and other aspects of MRI image denoising algorithms are reviewed, analyzed and their performances are compared. Finally, prospects and future trends for the field of medical image denoising are analyzed.
[Keywords] Magnetic resonance imaging；Noise

WANG Hao * Department of Interventional Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China; School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China

KANG Xiao-dong School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China

LIU Ling-ling School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China

GENG Jia-jia School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China

GUO Jun School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China

*Correspondence to: Wang H, E-mail: mouseking2008@163.com

Conflicts of interest   None.

Received  2011-12-10
Accepted  2012-01-05
DOI: 10.3969/j.issn.1674-8034.2012.03.015
DOI:10.3969/j.issn.1674-8034.2012.03.015.

[1]
戴建平，陈红艳. 磁共振脉冲序列在中枢神经系统中的应用(一). 磁共振成像, 2010, 1(3): 220-226.
[2]
戴建平，沈慧聪，李少武. 磁共振脉冲序列在中枢神经系统中的应用(二). 磁共振成像, 2010, 1(3): 305-310.
[3]
康晓东. 医学影像图像处理. 北京: 人民卫生出版社, 2009: 43-60.
[4]
章毓晋. 图像处理和分析基础. 北京: 高等教育出版社, 2002: 94-117.
[5]
黄世亮，叶朝辉，裘鉴卿. 基于小波变换的磁共振图像去噪声. 波谱学杂志, 2006, 23(4): 529-541.
[6]
Wood WL, Bronskill MJ, Mulkern RV, et al. Physical MR desktop data. J Magn Reson Imaging, 1993, 3(Suppl): S19-S24.
[7]
Edelstein WA, Glover G, Hardy C, et al. The intrinistic signal-to-noise ratio in NMR imaging. Magn Reson Med, 1986, 3(4): 604-618.
[8]
Alexander ME, Baumgartner R, Summer AR, et al. A wavelet-based method for improving signal-to-noise ratio and contrast in MR images. Magn Reson Imaging, 2000, 18 (2): 169-180.
[9]
Sijbers J, den Dekker AJ, der Linden AV, et al. Adaptive anisotropic noise filtering for magnitude MR data. Magn Reson Imaging, 1999, 17(10): 1533-1539.
[10]
杨帆. 基于Contourlet变换的图像去噪算法研究. 北京: 北京交通大学硕士学位论文, 2008: 4-5.
[11]
朱秀昌，刘峰，胡栋. 数字图像处理与图像通信. 北京: 北京邮电大学出版社, 2002: 58-76.
[12]
Hamerly G, Elkan C. Altematives to the k2 means algorithm that find better clusterings//Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies. New York: ACM Press, 2002: 600-607.
[13]
Ramponi G. A cubic unsharp masking technique for contrast enhancement. Signal Process, 1998, 67 (2): 211-222.
[14]
Polesel A, Ramponi G, Mathews VJ. Image enhancement via adaptive unsharp masking. IEEE Trans Image Process, 2000, 9(3):505-510.
[15]
Fu SJ, Ruan QQ, Wang WQ, et al. A feature-dependent fuzzy bidirectional flow for adaptive image sharpening. Neurocomputing, 2007, 70 (4-6): 883-895.
[16]
阮秋琦，彭玉华. 数字图像处理学. 北京: 电子工业出版社, 2001: 63-211.
[17]
谢杰成，张大力，徐文立. 小波图像去噪综述. 中国图像图形学报, 2002, 7(3): 209-217.
[18]
Candes EJ. Monoseale ridgelets for the representation of images with edges. California: Department of Statisties, Stanford University, 1999: 1-26.
[19]
Johnstone IM, Silverman BW. Wavelet threshold estimators for data with correlated noise. J Royal Statics Society Series, 1997, 59(2): 319-351.
[20]
Xu Y, Weaver JB, Healy DM, et al. Wavelet transforms domain filters: a spatially selective noise filtration technique. IEEE Trans Image Process, 1994, 3(6):747-758.
[21]
Mallat S, Hwang WL. Singularity detection and processing with wavelets. IEEE Trans, 1992, 38(2):617-643.
[22]
Jiao LC, Tan S. Development and prospect of image multiscale geometric analysis. Acta Electronica Sinica, 2003, 31(12A): 1975-1981.
[23]
Do MN, Vetterli M. Contourlets: a directional multiresolution image representation. Rochester: IEEE International Conference on Image Processingm, 2002: 357-360.
[24]
Lu Y, Do MN. CRISP-Contourlet:a critically sampled directional multiresolution image representation. SanDiego: ProSPIE Conf on Wavelets X, 2003: 1-10.
[25]
da Cunha AL, Zhou J, Do MN. The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process, 2006, 15(10): 3089-3101.
[26]
Meyer FG, Coifiman RR. Brushlets: a tool for directional image analysis and image compression. Applied and Comput Harmonic Analysis, 1997, 4(2): 147-187.
[27]
Candes EJ. Ridgelets: theory and applications. California: Stanford University, 1998: 1-121.
[28]
Candes EJ, Donoho DL. Curvelets: a surprisingly effective nonadaptive representation for objects with edges: technical report. California: Stanford University, 2000: 1-16.
[29]
Pennec EL, Mallat S. Image compression with geometrical wavelets. Vancouver, Canada, September: Proc. of ICIP, 2000: 661-664.
[30]
Donoho DL, Huo XM. Beamlets and multiscale image analysis. Stanford: Stanford University, 2001: 1-49.
[31]
Velisavljević V, Beferull-Lozano B, Vetterli M, et al. Directionlets: anisotropic multidirectional representation with separable filtering. IEEE Trans Image Process, 2006, 15(7): 1916-1933.

PREV The value of clinical application and research of MRS in temporal lobe epilepsy
NEXT Attention shall be paid to the study and application of MR imaging on musculoskeletal system
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn