用于MR图像非局域均值去噪的边缘增强策略

波谱学杂志

用于MR图像非局域均值去噪的边缘增强策略

严序，周敏雄，徐凌，刘薇，杨光*

华东师范大学物理系，上海市磁共振重点实验室，上海 200062

收稿日期:2012-06-04 修回日期:2012-07-09 出版日期:2013-06-05 发布日期:2013-06-05
作者简介:Yan Xu(1984-),male,born in Hubei province,PhD student,specializing in MRI.*通讯联系人：杨光，电话：021-62233873,E-mail:gyang@phy.ecnu.edu.cn.
基金资助:
Supported by Science and Technology Commission of Shanghai Municipality (08DZ1900700).

An Edge Enhancing Scheme for Non-Local Means Denoised MR Images

YAN Xu, ZHOU Min-Xiong, XU Ling, LIU Wei, YANG Guang*

Shanghai Key Laboratory of Magnetic Resonance, Physics Department, East China Normal University, Shanghai 200062, China

Received:2012-06-04 Revised:2012-07-09 Online:2013-06-05 Published:2013-06-05
About author:Yan Xu(1984-),male,born in Hubei province,PhD student,specializing in MRI.*Corresponding author: Yang Guang,Tel: +86-21-62233873,E-mail:gyang@phy.ecnu.edu.cn.
Supported by:
Supported by Science and Technology Commission of Shanghai Municipality (08DZ1900700).

摘要/Abstract

摘要：

非局域均值(NLM)滤波有很好的去噪效果并已成功地应用于磁共振图像的去噪中，但与所有去噪方法相同，总是会在一定程度上模糊图像细节. 该文提出将从原始图像中提取出来的高频信息与NLM去噪图像相融合，来还原在去噪过程中丢失的细节. 首先利用一种基于拉普拉斯金字塔的多分辨率方法，从原始图像中提取出包含丰富的边缘信息的高频组分. 然后利用作者提出的一种新的基于SUSAN算子的边缘检测算子产生一幅连续的边缘图，并利用该边缘图将高频组分与NLM方法去噪的图像相融合. 该方法在图像的平滑区域取得了良好的去噪效果，同时可以保留甚至增强图像的细节. 同时，该方法对图像的增强不会导致增强图像中常见的伪影.

关键词: 磁共振成像, 图像增强, 多分辨增强, 边缘算子, 非局域均值, 图像去噪

Abstract:

Non-Local Means (NLM) is an excellent filter for noise removal and has been applied successfully to MR images. However, just like every other denoising filters, it inevitably blurs fine structures in images to certain extent. In this paper, we propose to combine high frequency components (HFC) extracted from the original image with the NLM-denoised image to recover the lost details. High frequency components, which contain rich edge information, are extracted with a multiresolution method based on Laplacian Pyramid. Then the HFCs are combined with the image denoised by NLM using a continuous edge map produced by an edge detector which is based on SUSAN operator and modified to be more robust to noises. The experiment showed that the proposed scheme achieved good suppression of noise in smooth regions while preserves or even enhances image details. Furthermore, images were enhanced subtly without artifacts that are typical in enhanced images.

Key words: Magnetic Resonance Imaging(MRI), Image enhancing, Multi-resolution enhancing, Edge-Detector, Non-Local Means, Image denoising

中图分类号:

O482.53

严序，周敏雄，徐凌，刘薇，杨光*. 用于MR图像非局域均值去噪的边缘增强策略[J]. 波谱学杂志.

YAN Xu, ZHOU Min-Xiong, XU Ling, LIU Wei, YANG Guang*. An Edge Enhancing Scheme for Non-Local Means Denoised MR Images[J]. Chinese Journal of Magnetic Resonance.

参考文献

[1] Shang Yun(商赟), Zhou Juan(周娟), Lei Dou(雷都), et al. Software system design in an integrated NMR spectrometer console(一体化核磁共振谱仪控制台的软件系统设计)[J]. Chinese J Magn Reson(波谱学杂志), 2012, 29(1): 68-77.

[2] Chen Wei-bo(陈伟波), Li Jian-qi(李建奇), Jiang Xiao-ping(姜小平), et al. Implementation of self-navigated motion correction fast spin echo technique at low field MRI system(自导航快速自旋回波在低场MRI上的实现)[J]. Chinese J Magn Reson(波谱学杂志), 2009, 26(2): 197-205.

[3] Huang Min(黄敏), Chen Jun-bo(陈军波), Xiong Qiong(熊琼), et al. Comparison and implementation of commonly-Used image reconstruction algorithms in parallel MRI(并行MRI图像重建算法比较及软件实现)[J]. Chinese J Magn Reson(波谱学杂志), 2011, 28(1): 99-108.

[4] Buades A, Coll B, Morel J M. A review of image denoising algorithms, with a new one[J]. Multiscale Model Simul, 2005, 4(2): 490-530.

[5] Donoho D. De-noising by soft-thresholding[J]. IEEE T Inform Theo, 1995, 41(3): 613-627.

[6] Chen G, Bui T. Multiwavelets denoising using neighboring coefficients[J]. IEEE Signal Proc Lett, 2003, 10(7): 211-214.

[7] Tomasi C, Manduchi R. Bilateral filtering for gray and color images[C]. Proceedings of the Sixth International Conference on Computer Vision, 1998.

[8] Wong W, Chung A, Yu S. Trilateral filtering for biomedical images[C]. Proceedings of the International Symposium on Biomedical Imaging, 2004.

[9] Dippel S, Stahl M, Wiemker R, et al. Multiscale contrast enhancement for radiographies: Laplacian Pyramid versus fast wavelet transform[J]. IEEE T Med Imaging, 2002, 21(4): 343-353.

[10] Peter J B, Edward H A. The Laplacian Pyramid as a compact image code[J]. IEEE T Commun, 1983, COMM-31(4): 532-40.

[11] Placidi G, Alecci M, Sotgiu A. Post-processing noise removal algorithm for magnetic resonance imaging based on edge detection and wavelet analysis[J]. Phys Med Biol, 2003, 48(13): 1 987-1 995.

[12] Delakis I, Hammad O, Kitney R I. Wavelet-based de-noising algorithm for images acquired with parallel magnetic resonance imaging (MRI)[J]. Phys Med Biol, 2007, 52(13): 3 741-3 751.

[13] Do M, Vetterli M. Framing pyramids[J]. IEEE T Signal Proces, 2003, 51(9): 2 329-2 342.

[14] Smith S, Brady J. SUSAN-A new approach to low level image processing[J]. Int J Comput Vision, 1997, 23(1): 45-78.

[15] Coupe P, Yger P, Prima S, et al. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images[J]. IEEE T Med Imaging, 2008, 27(4): 425-441.

[16] Manjón J V, Carbonell-Caballero J, Lull J J, et al. MRI denoising using Non-Local Means[J]. Medical Image Analysis, 2008, 12(4): 514-523.

[17] Valyear K F, Culham J C, Sharif N, et al. A double dissociation between sensitivity to changes in object identity and object orientation in the ventral and dorsal visual streams: a human fMRI study[J]. Neuropsychologia, 2006, 44(2): 218-228.