微信服务号

微信订阅号

2025-4-3- 20
Home > Archive>Volume 20, Issue 5, 2009 >1106-1115
PDF HTML XML Export Cite reminder
Energy Conduction Model and Its Application in Medical Image Segmentation
Author:
Affiliation:

  • Article
    • | |
  • Metrics
    • |
  • Reference [23]
    • |
  • Related
    • |
  • Cited by [1]
    • | |
  • Comments
    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.

    Reference
    [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.
    Related
Get Citation

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

Copy
Share
Article Metrics
  • Abstract:8756
  • PDF: 8130
  • HTML: 0
  • Cited by: 0
History
  • Received:August 27,2008
  • Revised:December 15,2008
Links:Institute of Software, CASChina Computer FederationCASNSFC
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address：4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code：100190
Phone：010-62562563 Fax：010-62562533 Email：jos@iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063