Computerized Medical Imaging and Graphics
Volume 33, Issue 8 , Pages 602-607 , December 2009

An improved graph cut segmentation method for cervical lymph nodes on sonograms and its relationship with node's shape assessment

  • Junhua Zhang

      Affiliations

    • Department of Electronic Engineering, Yunnan University, Kunming, 650091, China
    • Tel.: +86 871 5031301; fax: +86 871 5031301.
  • ,
  • Yuanyuan Wang

      Affiliations

    • Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
    • Corresponding Author InformationCorresponding author. Tel.: +86 21 65643526; fax: +86 21 65643526.
  • ,
  • Xinling Shi

      Affiliations

    • Department of Electronic Engineering, Yunnan University, Kunming, 650091, China
    • Tel.: +86 871 5031301; fax: +86 871 5031301.

Received 25 October 2008 ,Revised 5 March 2009 ,Accepted 6 June 2009.

References 

  1. Ahuja AF, Ying MM. An overview of neck node sonography. Invest Radiol. 2002;37(6):333–342
  2. Yashida H, Yusa H, Ueno E, Tohno E, Tsunoda-Shimizu H. Ultrasonographic evaluation of small cervical lymph nodes in head and neck cancer. Ultrasound Med Biol. 1998;24(5):621–629
  3. Toriyabe Y, Nishimura T, Kita S, Saito Y, Miyokawa N. Differentiation between benign and metastatic cervical lymph nodes with ultrasound. Clin. Radiol. 1997;52(12):927–932
  4. Wu C, Lee MM, Huang K, Ko J, Sheen T, Hsieh F. A probability prediction rule for malignant cervical lymphadenopathy using sonography. Head Neck. 2000;22(3):223–228
  5. Adibelli ZH, Ünal G, Gül E, Uslu F, KocakÜ , Abali Y. Differentiation of benign and malignant cervical lymph nodes: value of B-mode and color Doppler sonography. Eur J Radiol. 1998;28(3):230–234
  6. Olabarriaga SD, Smeulders AWM. Interaction in the segmentation of medical images: a survey. Med Image Anal. 2001;5(4):127–142
  7. Boykov Y, Jolly MP. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: Proceedings of the 8th IEEE International Conference on Computer Vision. Vancouver, BC, Canada. 2001;p. 105–112
  8. Boykov Y, Funka-Lea G. Graph cuts and efficient N-D image segmentation. Int J Comput Vis. 2006;70(2):109–131
  9. Funka-Lea G, Boykov Y, Florin C, Jolly MP, Moreau-Gobard R, Ramaraj R. Automatic heart isolation for CT coronary visualization using graph-cuts. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano and Macro. Arlington, VA, USA. 2006;p. 614–617
  10. Arias P, Pini A, Sanguinetti G, Sprechmann P, Cancela P, Fernández A, et al. Ultrasound image segmentation with shape priors: application to automatic cattle rib-eye are estimation. IEEE Trans Image Proc. 2007;16(6):1637–1645
  11. Xie J, Jiang Y, Tsui H. Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans Med Imaging. 2005;24(1):45–57
  12. Zoller T, Buhmann JM. Robust image segmentation using resampling and shape constraints. IEEE Trans Pattern Anal Machine Intell. 2007;29(7):1147–1164
  13. Yuille AL, Cohen DS, Hallinan PW. Feature extraction from faces using deformable templates. In: IEEE Computer Society Conference on Computer Vision, and Pattern Recognition. San Diego, CA, USA. 1989;p. 104–109
  14. Freedman D, Zhang TI. nteractive graph cut based segmentation with shape priors. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA. 2005;p. 755–762
  15. Slabaugh G, Unal G. Graph cuts segmentation using an elliptical shape prior. In: IEEE International Conference on Image Processing. Genoa, Italy. 2005;p. 1222–1225
  16. Jie ZJ. Graph cuts segmentation with geometric shape priors for medical images. In: IEEE Southwest Symposium on Image Analysis and Interpretation. Santa Fe, NM, USA. 2008;p. 109–112
  17. Ford L, Fulkerson D. Flows in networks. Princeton: Princeton Univ. Press; 1962;
  18. Boykov Y, Kolmogorov V. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Machine Intell. 2004;26(9):1124–1137
  19. Kolmogorov V, Zabih R. What energy functions can be minimized via graph cuts?. IEEE Trans Pattern Anal Machine Intell. 2004;26(2):147–159
  20. Greig D, Porteous B, Seheult A. Exact maximum a posteriori estimation for binary images. J Royal StatSoc: Series B. 1989;51(2):271–279
  21. Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Machine Intell. 2001;23(11):1222–1239
  22. Veksler O. Image segmentation by nested cuts. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Hilton Head, SC, USA. 2000;p. 339–344
  23. Cohen LD, Cohen I. Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Trans Pattern Anal Machine Intell. 1993;15(11):1131–1147
  24. Fitzgibbon A, Pilu M, Fisher RB. Direct least square fitting of ellipses. IEEE Trans Pattern Anal Machine Intell. 1999;21(5):476–480
  25. Sahiner B, Petrick N, Chan H, Hadjiiski L, Paramagul C, Helvie M, et al. Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization. IEEE Trans Med Imaging. 2001;20(12):1275–1284
  26. Huttenlocher DJ, Klanderman GA, Rucklidge WJ. Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Machine Intell. 1993;15(9):850–863
  27. Zhang YJ. Influence of segmentation over feature measurement. Pattern Recognition Lett. 1995;16(2):201–206
  28. Wong KG, Heng PA, Wong TT. Accelerating intelligent scissors using slimmed graphs. J Graphics Tools. 2000;5(2):1–13

PII: S0895-6111(09)00080-9

doi: 10.1016/j.compmedimag.2009.06.002

Computerized Medical Imaging and Graphics
Volume 33, Issue 8 , Pages 602-607 , December 2009