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多发性硬化症MR图像分割新算法研究 被引量：3

Novel segmentation algorithm for multiple sclerosis lesions in MR images
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摘要 提出了一种针对多发性硬化症病灶T2加权脑部磁共振(MR)图像的分割算法。根据多发性硬化症病灶和脑脊液在T2加权像上同表现为高亮度信号的特点,把模糊C均值分割算法与形态学方法相结合,提出了基于核模糊C均值的多发性硬化症病灶分割算法。该算法首先用改进的核模糊C均值算法做基础分割,再用形态学方法提取出多发性硬化症病灶得到最终分割结果。通过对多发性硬化症模拟脑部MR图像的分割结果表明,算法能够比较准确地分割多发性硬化症病灶。 A novel approach to the segmentation of Multiple Sclerosis（MS） lesions in T2-weighted Magnetic Resonance（MR） images is presented.According to the characteristic of MS lesions show the same high brightness with CerebroSpinal Fluid（CSF） in T2-weighted images,combining the strengths of the kernel fuzzy C-means algorithm and morphology characteristics of MS lesion tissues,the segmentation of MS lesions based on kernel fuzzy C-means algorithm is presented.The modified kernel fuzzy C-means algorithm is used to basic segmentation.Then the MS lesions are extracted by morphological method.The MS segmentation in simulated T2-weighted MR images show that the proposed algorithm can provide a powerful segmentation.
作者 余学飞 李彬 陈武凡
机构地区 南方医科大学生物医学工程学院
出处 《计算机工程与应用》 CSCD 北大核心 2009年第2期176-178,共3页 Computer Engineering and Applications
基金 国家重点基础研究发展规划(973)No.2003CB716101 广东省科技计划项目(No.2007B010400057)~~
关键词 图像分割 核模糊C均值 多发性硬化症 segmentation of image kernel fuzzy C-means multiple sclerosis lesions
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
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参考文献10

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二级参考文献15

  • 1林亚忠,程跃斌,陈武凡.基于修正的分段模糊吉伯斯随机场模型的图像分割[J].计算机应用,2005,25(11):2606-2608. 被引量：1
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共引文献4

同被引文献32

  • 1李海云,李光颖,王筝.一种基于水平集的脊柱MRI图像分割算法的研究[J].北京生物医学工程,2004,23(3):178-180. 被引量：6
  • 2黎俊锋,朱锋峰.基于样本密度的FCM改进算法[J].科学技术与工程,2007,7(4):636-638. 被引量：12
  • 3LLADO X, OLIVER A, CABEZAS M, et al. Segmentation of mul- tiple sclerosis lesions in brain MRI: A review of automated approa- ches[J]. Information Sciences, 2012, 186(1) : 164 - 185.
  • 4GARC[A-LORENZO D, FRANCIS S, NARAYANAN S, et al. Re- view of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging[ J]. Med- ical Image Analysis, 2013, 17(1) : 1 - 18.
  • 5BOUDRAA A O, DEHAK S M R, ZHU Y M, et al. Automated segmentation of multiple sclerosis lesions in muhispectral MR ima- ging using fuzzy clustering[ J]. Computers in Biology and Medicine, 2000, 30(1): 23-40.
  • 6GREENSPAN H, RUF A, GOLDBERGER J. Constrained Gaussian mixture model framework for automatic segmentation of MR brain im- ages[J]. IEEE Transactions on Medical Imaging, 2006, 25(9):1233 - 1245.
  • 7ZHANG D Q, CHEN S C. Clustering incomplete data using kernel- based fuzzy C-means algorithm [ J]. Neural Processing Letters, 2003, 18(3) : 155 - 162.
  • 8ZHANG D Q, CHEN S C. A novel kemelized fuzzy C-means algo- rithm with application in medical image segmentation[ J]. Artificial Intelligence in Medicine, 2004, 32( 1): 37 -50.
  • 9GRAVES D, PEDRYCZ W. Performance of kernel-based fuzzy clus- tering [ J]. Electronics Letters, 2007, 43(25) : 1445 - 1446.
  • 10SHAD D, SUTI'ON J P. Towards automated enhancement, segmen- tation and classification of digital brain images using networks of net- works[J1. Information Sciences, 2001, 138(1): 45-77.

引证文献3

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计算机工程与应用
2009年 第2期

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