Advertisement

Tumor Boundary Delineation Using Abnormality Outlining Box Guided Modified GVF Snake Model

  • Srinivas ThirumalaEmail author
  • Srinivasa Rao Chanamallu
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 79)

Abstract

Segmentation is a million dollar task in order to highlight any region of interest (ROI) such as tumor, bleed, edema, or infarct in any medical image. Disease diagnosis, prognosis, surgery, and rehabilitation aspects are guided by segmentation results in oncology. The best ideology is to use high-end active contour models for tumor detection, location, and delineation. Traditional parametric active contour model like GVF (gradient vector flow) snake model suffers from more sensitive parameters, limited capture range, more sensitive to noise, and indentation while segmenting brain tumors in medical images. In order to address and solve these problems, a modified GVF is proposed. The modified snake model proposes a new design for the snake external force to resolve these problems by inducing a strong force near edges. This paper will address how an abnormality outlining box (AOB) guides modified GVF to get rid of demerit like lack of choice of precise initial contour. AOB-guided modified GVF is an integration of AOB and modified GVF external force. The experimental results prove the modified snake model captures the tumor efficiently and quickly even in the presence of any noise artifacts.

Keywords

Abnormality outlining box Brain tumor Convergence Deformable models Delineation GVF Modified GVF Histogram Medical image segmentation Parametric active contours Snakes 

Notes

Acknowledgements

Authors are thankful to the GSL Medical College and General Hospital, Rajahmundry, Andhra Pradesh, India for image resources in order to carry out this work.

References

  1. 1.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: An active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRefGoogle Scholar
  2. 2.
    Yu Zhong., Jain, A.K., Dubuisson-Jolly, M.-P.: Object tracking using deformable templates. IEEE Trans. Pattern Anal. Mach. Intell. 22(5), 544–549 (2000). https://doi.org/10.1109/34.857008   
  3. 3.
    Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recogn. 26(9), 1277–1294 (1993)CrossRefGoogle Scholar
  4. 4.
    Terzopoulos, D., Fleischer, K.: Deformable models. Vis. Comput. 4(6), 306–331 (1988)CrossRefGoogle Scholar
  5. 5.
    Kichenassamy, S., Kumar, A., Oliver, P., Tannenbaum, A., Yezzi, A.: Gradient vector flows and geometric active contours. In: Fifth International Conference on Computer Vision ICCV 95, pp. 810–815. IEEE Computer Society, Washington, DC, USA (1995)Google Scholar
  6. 6.
    Huang, S., Wang, B., Huang, X.: Using GVF snake to segment liver from CT images. In: 3rd IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors, pp. 145–148. MIT, Boston, USA (2006)Google Scholar
  7. 7.
    Terzopoulos, D.: On matching deformable models to images: Direct and Iterative Solutions. In: Topical Meeting on Machine Vision. Technical Digest Series, vol. 12, no. 9, pp. 164–167. Optical Society of America, Washington, DC, Incline Village, Nevada (1987)Google Scholar
  8. 8.
    Sum, K.W., Cheung, P.Y.S.: Boundary vector field for parametric active contours. Pattern Recogn. 40(6), 1635–1645 (2007)Google Scholar
  9. 9.
    Williams, D.J., Shah, M.: A fast algorithm for active contours and curvature estimation. CVGIP: Image Understanding 55(1), 14–26 (1992)Google Scholar
  10. 10.
    Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 3, 359–369 (1998)Google Scholar
  11. 11.
    Jehan-Besson, S., Barlaud, M., Aubert, G.: A 3-step Algorithm using region based active contours for video objects detection. EURASIP J. Appl. Sig. Process. 6, 572–581 (2002)zbMATHGoogle Scholar
  12. 12.
    Peterfreund, N.: The velocity snake: deformable Contour for tracking in spatio-velocity space. Comput. Vis. Image Underst. 73(3), 346–356 (1999)CrossRefGoogle Scholar
  13. 13.
    Yu, Z., Bajaj, C.: Image segmentation using gradient vector diffusion and region merging. In: 16th International Conference on Pattern Recognition, vol. 2, pp. 941–944. IEEE CS Press, Quebec, Canada (2002)Google Scholar
  14. 14.
    Ray, N., Acton, S.T., Atles, T., Delange, E.E., Brookeman, J.R.: Merging parametric active contours with in homogeneous image regions for MRI-Based Lung segmentation. IEEE Trans. Med. Imaging 22(2), 189–199 (2003)CrossRefGoogle Scholar
  15. 15.
    Gunn, S.R., Nixon, M.S.: Robust snake implementation: a dual active contour. IEEE Trans. Pattern Anal. Mach. Intell. 19(1), 63–67 (1997)Google Scholar
  16. 16.
    Zhu, S.C., Yulle, A.: Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18(9), 884–900 (1996)Google Scholar
  17. 17.
    Berger, M.O.: Snake growing. In: Berger, M.O. (ed.) Snake Growing. Computer Vision Proceedings. First European Conference on Computer Vision (ECCV’90). Lectures Notes in Computer Science. Springer, Berlin, pp. 570–572. Antibes, France (1990)Google Scholar
  18. 18.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 158–175 (1995)Google Scholar
  19. 19.
    Han, X., Xu C., Prince, J.: A topology preserving level set method for geometric deformable models. IEEE Trans. Pattern Anal. Mach. Intell. 25, 755 (2003)Google Scholar
  20. 20.
    Leung Lam, C., Yin Yuen, S.: A fast active cont. algorithm for object tracking in complex background. In: Mertzios, B.G., Liatsis, P. (eds.) Proceedings of IWISP 1996, pp. 165–168. Elsevier Science B.V., Amsterdam (1996)Google Scholar
  21. 21.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)CrossRefGoogle Scholar
  22. 22.
    Makhanov, S.S.: Active contours in medical image processing: Theory and Applications. In: 5th International Conference on Knowledge and Smart Technology (KST), pp. xviii–xx. IEEE, Chonburi (2013)Google Scholar
  23. 23.
    Nilanjan, R., Russell, G., Albert, M.: Using Symmetry to Detect Abnormalies in Brain MRI. Comput. Soc. India Commun. 31(19) (2008)Google Scholar
  24. 24.
    Chawla, M., Sharma, S., Sivaswamy, J., Kishore, L.T.: A method for automatic detection and classification of stroke from brain CT images. In: 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3581–3584. IEEE, Minneapolis, Minnesota, USA (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of ECEAditya College of EngineeringSurampalemIndia
  2. 2.Department of ECEUniversity College of Engineering Vizianagaram, JNTUKVizianagaramIndia

Personalised recommendations