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基于MITK的CT序列图像模糊连接度分割算法研究 被引量:3

MITK-based Fuzzy Connectedness on CT Series Image Segmentation
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摘要 MITK是一个集成化的医学影像处理与分析C++类库的软件包.本文在VC++6.0环境下对MITK的区域生长Filter进行了扩充,实现了模糊连接度算法的Filter,并对腹部CT序列图像中的肝内血管进行分割,获得了较好的结果,为下一步利用MITK算法平台进行医学图像分割和可视化等开发打下基础. MITK is an integrated software package for medical image process and analysis with C++class libraries.The region growing filter is extended by using MITK under VC6.A new filter based on fuzzy connectedness algorithm is designed and is used to segment liver bloods on CT images of belly.Good results is achieved,which builds hope that the medical image process software could be developed with MITK platform.
出处 《湘南学院学报》 2011年第2期38-40,48,共4页 Journal of Xiangnan University
基金 湖南省教育厅优秀青年项目(09B097) 郴州市科技局计划项目(2009g106)
关键词 模糊连接度 MITK CT序列 图像分割 fuzzy connectedness,MITK,CT series,image segmentation
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  • 1潘建江,杨勋年,汪国昭.基于模糊连接度的图像分割及算法[J].软件学报,2005,16(1):67-76. 被引量:31
  • 2赵明昌,田捷,薛健,朱珣,何晖光,吕科.医学影像处理与分析开发包MITK的设计与实现[J].软件学报,2005,16(4):485-495. 被引量:41
  • 3邱明,张二虎.医学图像分割方法[J].计算机工程与设计,2005,26(6):1557-1559. 被引量:18
  • 4魏敏,李朝峰.基于模糊连接度的卫星图像道路提取新方法[J].计算机工程与应用,2006,42(13):230-232. 被引量:6
  • 5Udupa J K,Samarasekera S.Fuzzy connectedness and object definition:theory,algorithms,and applications in image segmentation[J]. Graphical Models and Image Processing, 1996,58 (3) : 246-261.
  • 6Saha P K,Udupa J K,Odhner D.Scale-based fuzzy connected image segmentation:theory,algorithms,and validation[J].Computer Vision and Image Understanding,2000,77(9):145-174.
  • 7Udupa J K,Saha P K,Lotufo R A.Relative fuzzy connectedness and object definition:theory,algorithms,and applications in image segmentation[J].IEEE Transaction on Pattern Analysis and Machine Intelligence, 2002,24( 11 ) : 1485-1500.
  • 8He Hao,Chen Yan-qiu.Fuzzy aggregated connectedness for image segmentation[J].Pattern Recognition(Rapid and Brief Communication),2001,34(12) :2565-2568.
  • 9Herman G T,Carvalho B M.Muhiseeded segmentation using fuzzy connectedness[J].IEEE Transaction on Pattern Analysis and Machine Intelligence, 2001,33 (5) : 460-474.
  • 10Kniss J, Kindlmann G, Hansen C. Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets. In: Bailey M, Hansen C, e ds. Proc. of the IEEE Visualization 2001. IEEE Computer Society Press, 2001. 253-262.

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  • 1芦蓉,沈毅.一种改进的二维直方图的图像阈值分割方法[J].系统工程与电子技术,2004,26(10):1487-1490. 被引量:18
  • 2潘建江,杨勋年,汪国昭.基于模糊连接度的图像分割及算法[J].软件学报,2005,16(1):67-76. 被引量:31
  • 3于龙,王乘,李利军,袁晓辉.一种改进的模糊连接度图像分割方法[J].微计算机信息,2007,23(27):273-275. 被引量:2
  • 4李彬,陈武凡.基于模糊连接度的多发性硬化症MR图像自动分割算法[J].中国生物医学工程学报,2007,26(5):664-668. 被引量:7
  • 5Amini L, Soltanian-Zadeh H, Lucas C, et al. Automatic segmentation of thalamus from brain MRI integrating fuzzy clustering and dynamic contours [ J ]. Biomedical Engineering, IEEE Transactions on,2004, 51 (5) : 800-811.
  • 6HeckenburgG, Xi Yongjian, Duan Ye, et al. Thalamus segmentation from MRI images by lagrangian surface flow [ J ]. Engineering in Medicine and Biology Society, 2005: 3039- 3042.
  • 7Duan Y, Heckenburg G, Yongjian Xi, et al. Thalamus segmentation from diffusion tensor magnetic resonance imaging [J].Engineering in Medicine and Biology Society, 2006:3628 -3631.
  • 8Rosenfeld A. Fuzzy digital topology[J].Information and Control, 1979, 40( 1 ) :76-87.
  • 9Udupa J K, Samarasekera S. Fuzzy connectedness and objeet definition: theory, algorithms, and applications in image segmentation [ J ]. Graphical Models and Image Processing, 1996, 58(3) : 246-261.
  • 10Harati V, Khayati R, Farzan A. Fully automated tumor segmentation based on improved fuzzy eonnectedness algorithm in brain MR images[J]. Biology and Medicine, 2011, 41: 483- 492.

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