微信服务号

微信订阅号

2025年3月24日 12:42 星期一
首页 > 过刊浏览>2009年第20卷第5期 >1106-1115
PDF HTML阅读 XML下载 导出引用 引用提醒
能量传导模型及在医学图像分割中的应用
作者:
基金项目:

Supported by the National Natural Science Foundation of China under Grant Nos.10527003, 60672104 (国家自然科学基金); the National Basic Research Program of China under Grant No.2006CB705705 (国家重点基础研究发展计划(973)); the Joint Research Subject of Beijing Education Committee of China under Grant No.JD100010607 (北京市共建项目); the Beijing Municipal Natural Science Foundation of China under Grant No.3073019 (北京市自然科学基金); the Upgrading Subject of Instrument in Science and Technological Ministry of China under Grant No.2006JG1000 (科技部仪器升级改造项目)

  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [23]
    • |
  • 相似文献
    • |
  • 引证文献
    • | |
  • 文章评论
    摘要:

    提出了一种基于水平集框架的能量传导模型ECM(energy conduction model)用于对医学图像进行分割.该模型通过对图像中的灰度分布和空间中的温度场分布进行对比,有效定义了图像能量和图像能量的传导方程,并通过模拟热量传递的过程对方程进行求解.ECM模型的优点在于,它在描述图像灰度分布的全局特征的同时,有效地捕捉到图像局部区域的灰度对比度变化,因此它能够对灰度分布不均匀和含有噪声的图像进行精确分割.基于水平集函数本身的拓扑可变性,该方法还能够实现同一图像中的多目标分割.使用该方法对模拟和真实的医学图像进行了分割实验,实验结果表明了该方法的有效性和可靠性.

    Abstract:

    This paper proposes an energy conduction model (ECM) based on the level set framework, which takes advantage of the heat conduction equation to construct the image energy. After comparing the image intensity distribution with the spatial distribution of the temperature field, an energy conduction function is defined, which well simulates the process of heat conducting. The advantage of the ECM is that it captures the global feature of an image and takes the local intensity information into account. Thus, ECM is able to accurately segment medical images with inhomogeneity and noise, as well as for the medical images with multi-targets. Synthetic and real medical images are tested with ECM, which shows its robustness and efficiency.

    参考文献
    [1] Osher S, Sethian J. Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulation. Journal of Computer Physics, 1988,79:12?49.
    [2] Caselles V, Kimmel R, Sapiro G. Geodesic active contours. Int’l Journal on Computer Vision, 1997,22(1):61?97.
    [3] Chan TF, Vese LA. Active contours without edges. IEEE Trans. on Image Processing, 2001,10(2): 266?277.
    [4] Vese LA, Chan TF. A multiphase level set framework for image segmentation using the Mumford and Shah model. Int’l Journal of Computer Vision, 2002,50(3):271?293.
    [5] Chen J, Tian J, Xue J, Dai YK. Level set method with multi-speed-function and its application in segmentation of medical images. Journal of Software, 2007,18(4):842?849 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/18/842.htm
    [6] Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, 1989,42(4):577?685.
    [7] Tsai A, Yezzi A, Willsky AS. Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. on Image Processing, 2001,10(8):1169?1186.
    [8] Tsai A, Yezzi A, Willsky AS. A curve evolution approach to smoothing and segmentation using the Mumford-Shah functional. In: Proc. of the IEEE Conf. Computer Vision Pattern Recognition. Hilton Head Island, 2000. 119?124.
    [9] Gazit MH, Goldsher D, Kimmel R. Hierarchical segmentation of thin structures in volumetric medical images. In: Proc. of the 6th Int’l Conf. on Medical Image Computing and Computer-Assisted Intervention—MICCAI 2003. 2003. 562?569.
    [10] Jeon M, Alexander M, Pedrycz W. Unsupervised hierarchical image segmentation with level set and additive operator splitting. Pattern Recognition Letter, 2005,26(10):1461?1469.
    [11] Li S, Fevens T, Krzyzak A. A SVM-based framework for autonomous volumetric medical image segmentation using hierarchical and coupled level sets. In: Proc. of the Int’l Congress Series. 2004. 207?211.
    [12] Li S, Fevens T, Krzyzak A. Semi-Automatic computer aided lesion detection in dental X-rays using variational level set. Pattern Recognition, 2007,40(10):2861?2873.
    [13] Li S, Fevens T, Krzyzak A. An automatic variational level set segmentation framework for computer aided dental X-rays analysis in clinical environments. Computerized Medical Imaging and Graphics, 2006,30:65?74.
    [14] Li S, Fevens T, Krzyzak A. Image segmentation adapted for clinical settings by combining pattern classification and level sets. In: Proc. of the7th Int’l Conf. on Medical Image Computing and Computer-Assisted Intervention—MICCAI 2004. 2004. 160?167.
    [15] Perona P, Malik J. Scale-Space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1990,12(7):629?639.
    [16] Zhu FP, Tian J, Lin Y, Ge XF. Medical image segmentation based on level set method. Journal of Software, 2002,13(9):1866?1872 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/13/1866.htm
    [17] Lu HY. Studies and applications of modeling techniques in active contours for medical image segmentation [Ph.D. Thesis]. Beijing: Peking University, 2006 (in Chinese with English abstract).
    [18] Oshe S, Fedkiw R. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. 23?93.
    [19] Zhao HK, Chan T, Merriman B. A variational level set approach to multiphase motion. Journal of Computational Physics, 1996, 127(1):179?195.
    [20] Rudin LI, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physics D, 1992,60(1-4):259?268. 附中文参考文献:
    [5] 陈健,田捷,薛健,戴亚康.多速度函数水平集算法及在医学分割中的应用.软件学报,2007,18(4),842?849. http://www.jos.org.cn/ 1000-9825/18/842.htm
    [16] 朱付平,田捷,林瑶,葛行飞.基于Level Set方法的医学图像分割.软件学报,2002,13(9):1866?1872. http://www.jos.org.cn/1000- 9825/13/1866.htm
    [17] 吕红宇.医学图像分割中主动轮廓法的模型化研究及应用[博士学位论文].北京,北京大学,2006.
    相似文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

段侪杰,马竟锋,张艺宝,侯凯,包尚联.能量传导模型及在医学图像分割中的应用.软件学报,2009,20(5):1106-1115

复制
分享
文章指标
  • 点击次数:8746
  • 下载次数: 8114
  • HTML阅读次数: 0
  • 引用次数: 0
历史
  • 收稿日期:2008-08-27
  • 最后修改日期:2008-12-15
文章二维码
友情链接:中国科学院软件研究所中国计算机学会中国科学院国家自然科学基金委员会CCF数字图书馆Int'l J of Softw Info计算机系统应用
您是第19727868位访问者
版权所有：中国科学院软件研究所 京ICP备05046678号-3
地址：北京市海淀区中关村南四街4号,邮政编码：100190
电话：010-62562563 传真：010-62562533 Email：jos@iscas.ac.cn
技术支持：北京勤云科技发展有限公司

京公网安备 11040202500063号