一种鲁棒的人脑组织核磁共振图像分割算法研究

林盘; 郑崇勋; 杨勇; 阎相国; 顾建文

一种鲁棒的人脑组织核磁共振图像分割算法研究

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出版历程
- 收稿日期: 2004-09-15
- 修回日期: 2005-03-09
- 刊出日期: 2005-09-19

A Robust Method for Segmentation of Human Brain Tissue from Magnetic Resonance Images

摘要

摘要: 自动的人脑核磁共振(MR)图像分割是许多医学图像应用的关键问题.该文提出了一种有效的自动脑核磁共振图像的分割方法框架体系,脑MR分割框架体系由3个处理步骤构成.首先,采用基于水平集的方法将MR图像中非脑组织剔除,从脑图像中提取大脑组织结构.然后,对MR脑结构图像进行灰度不均匀性校正.最后,该算法采用最大后验分类器可以将人脑组织分为脑白质、脑灰质、脑髓液.在实验中对大量的MR脑图像数据应用该分割算法.实验结果充分证明该方法的有效性.这种分割算法适用于人脑核磁图像分析的各种实际临床应用.
- 核磁共振成像; 偏场校正; 水平集; 马尔可夫随机场; 分割
Abstract: Automatic segmentation of brain magnetic resonance images is a critical problem in many medical imaging applications. In this paper, a robust automated segmentation algorithm is presented for the brain magnetic resonance images. The segmentation framework is composed of three stages. First, it uses level set method to perform the brain stripping operation. In the second stage, it compensates for nonuniformity in the brain image based on computing estimates of tissue intensity variation. Finally, a maximum aposteriori classifier is used to partition the brain into gray matter, white matter, and cerebrospinal fluid. The proposed method has been tested using magnetic resonance dada. This algorithm may be applied to various research and clinical investigations in which brain segmentation and volume measurement involving Magnetic resonance images dada are needed.

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Wells W M, Kikinis R, Grimson W E L, et al.. Adaptive segmentation of MRI data[J].IEEE Trans on Medical Imaging.1996, 15(5):429-[2]Leemput K Van, Maes F, Vandermeulen D, et al.. Automated model based t 10 classification of MR images of the brain[J].IEEE Trans. on Medical Imaging.1999, 18( 10):897-[3]Cocosco C A, Zijden.bos A P, Evans A C. A fully automatic and robust brain MRI tissue classification method. Medical Image Analysis, 2003, 7(4): 513 - 527.[4]Shattuck D W, Sandor-Leahy S R, Schaper K A, et al.. Magnetic resonance image tissue classification using a partial volume model. NeuroImage, 2001, 13(5): 856 - 876.[5]Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm[J].IEEE Trans. on Medical Imaging.2001, 20(1):45-[6]Osher S, Sethian J A. Fronts propagating with curvaturedependent speed[J].Journal of Computational Physics.1998, 79(1):12-[7]Geman S, Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.1984, 6(6):721-[8]Besag J. On the statistical analysis of dirty pictures. J. Roy. Statist.Soc, Ser. B, 1986, 48(3): 259 - 302.[9]Collins D L, Zijdenbos A P, Kollokian V, et al.. Design and construction of a realistic digital brain phantom[J].IEEE Trans. on Medical Imaging.1998,17(3):463-

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