Skip to main content
SpringerLink
Log in

Extension of the virtual electric field model using bilateral-like filter for active contours

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The gradient vector flow (GVF) model has been proven as an effective external force for active-contour-based image segmentation. However, it suffers from high computation cost since there are two PDEs to be solved in an iterative manner. As a remedy, the virtual electric field (VEF) model is proposed, which can be implemented in real time using the fast Fourier transform. However, the VEF model cannot preserve weak edges since it employs linear kernels. In this work, we extend the VEF model by using bilateral-like filters, and a fast algorithm is also employed for the proposed model. The proposed model is referred to as bilateral-filter-based VEF (BVEF) model. Experimental results on synthetic and real images demonstrate that the BVEF snake possesses some desired properties of the GVF, CNGGVF and VEF snakes such as large capture range and concavity convergence, and the BVEF model can be implemented in near real time, and its computation cost is comparable to that of the VEF model and much shorter than that of the GVF and CNGGVF models; it also can preserve weak edges, thanks to the bilateral-like nonlinear kernels.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Ma, Z., Tavares, J.M.R.S., Natal Jorge, R.M.: A review on the current segmentation algorithms for medical images. In: 1st International Conference on Imaging Theory and Applications (IMAGAPP), pp. 135–140, Portugal (2009)

  2. Jodas, D.S., Pereira, A.S., Tavares, J.M.R.S.: A review of computational methods applied for identification and quantification of atherosclerotic plaques in images. Expert Syst. Appl. 46(15), 1–14 (2016)

    Article  Google Scholar 

  3. Ferreira, A., Gentil, F., Tavares, J.M.R.S.: Segmentation algorithms for ear image data towards biomechanical studies. Comput. Methods Biomech. Biomed. Eng. 17(8), 888–904 (2014)

    Article  Google Scholar 

  4. Ma, Z., Tavares, J.M.R.S., Natal Jorge, R.M.: A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput. Methods Biomech. Biomed. Eng. 13(2), 235–246 (2010)

    Article  Google Scholar 

  5. Oliveira, R.B., Filho, M.E., Ma, Z., Papa, J.P., Pereira, A.S., Tavares, J.M.R.S.: Computational methods for the image segmentation of pigmented skin lesions: a review. Comput. Methods Programs Biomed. 131, 127–141 (2016)

    Article  Google Scholar 

  6. Gonçalves, P.C.T., Tavares, J.M.R.S., Natal Jorge, R.M.: Segmentation and simulation of objects represented in images using physical principles. Comput. Model. Eng. Sci. 32(1), 45–55 (2008)

    MathSciNet  MATH  Google Scholar 

  7. Vasconcelos, M.J.M., Tavares, J.M.R.S.: Methods to automatically build point distribution models for objects like hand palms and faces represented in images. Comput. Model. Eng. Sci. 36(3), 213–241 (2008)

    Google Scholar 

  8. Kass, M., Witkin, A., Terzopoulos, D.: Snake: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)

    Article  MATH  Google Scholar 

  9. Uhlmann, V., Fageot, J., Unser, M.: Hermite snakes with control of tangents. IEEE Trans. Image Process. 25(6), 2803–2816 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  10. Sha, Y., Yao, L., Molloy, D.: A dynamic-shape-prior guided snake model with application in visually tracking dense cell populations. IEEE Trans. Image Process. 28(3), 1513–1527 (2019)

    Article  MathSciNet  Google Scholar 

  11. Zhou, S., Li, B., Wang, Y., et al.: The line- and block-like structures extraction via ingenious snake. Pattern Recognit. Lett. 112, 324–331 (2018)

    Article  Google Scholar 

  12. Zhao, S., Li, G., Zhang, W., Gu, J.: Automatical intima-media border segmentation on ultrasound image sequences using a Kalman filter snake. IEEE Access (2018). https://doi.org/10.1109/access.2018.2856244

    Article  Google Scholar 

  13. Moallem, P., Tahvilian, H., Monadjemi, S.A.: Parametric active contour model using Gabor balloon energy for texture segmentation. Signal Image Video Process. 10(2), 351–358 (2016)

    Article  Google Scholar 

  14. Subudhi, P., Mukhopadhyay, S.: A novel texture segmentation method based on co-occurrence energy-driven parametric active contour model. SIViP 12(4), 669–676 (2018)

    Article  Google Scholar 

  15. Manno-Kovacs, A.: Direction selective contour detection for salient objects. IEEE Trans. Circuits Syst. Video Technol. (2018). https://doi.org/10.1109/tcsvt.2018.2804438

    Article  Google Scholar 

  16. Awate, S.P., Tasdizen, T., Whitaker, R.T.: Unsupervised texture segmentation with nonparametric neighborhood statistics. In: ECCV (2), pp. 494–507 (2006)

  17. Delgado-Gonzalo, R., Uhlmann, V., Schmitter, D., Unser, M.: Snakes on a plane: a perfect snap for bioimage analysis. IEEE Signal Process. Mag. 32(1), 41–48 (2015)

    Article  Google Scholar 

  18. Xu, C., Prince, J.: Snakes, shapes and gradient vector flow. IEEE Trans. Image Process. 17(3), 359–369 (1998)

    MathSciNet  MATH  Google Scholar 

  19. Wu, Y., Wang, Y., Jia, Y.: Adaptive diffusion flow active contours for image segmentation. Comput. Vis. Image Underst. 117(10), 1421–1435 (2013)

    Article  Google Scholar 

  20. Jaouen, V., González, P., Stute, S.: Variational segmentation of vector-valued images with gradient vector flow. IEEE Trans. Image Process. 23(11), 4773–4785 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhao, F., Zhao, J., Zhao, W., et al.: Guide filter-based gradient vector flow module for infrared image segmentation. Appl. Opt. 54(33), 9809–9817 (2015)

    Article  Google Scholar 

  22. Zhu, S., Bu, X., Zhou, Q.: A novel edge preserving active contour model using guided filter and harmonic surface function for infrared image segmentation. IEEE Access 6(99), 5493–5510 (2018)

    Article  Google Scholar 

  23. Ning, J., Wu, C., Liu, S., Yang, S.: NGVF: an improved external force field for active contour model. Pattern Recognit. Lett. 28(1), 58–63 (2007)

    Article  Google Scholar 

  24. Wang, Y., Liu, L., Zhang, H., et al.: Image segmentation using active contours with normally biased GVF external force. IEEE Signal Process. Lett. 17(10), 875–878 (2010)

    Article  Google Scholar 

  25. Li, C., Liu, J., Fox, M.D.: Segmentation of external force field for automatic initialization and splitting of snakes. Pattern Recognit. 38(11), 1947–1960 (2005)

    Article  Google Scholar 

  26. Qin, L., Zhu, C., Zhao, Y., et al.: Generalized gradient vector flow for snakes: new observations, analysis, and improvement. IEEE Trans. Circuits Syst. Video Technol. 23(5), 883–897 (2013)

    Article  Google Scholar 

  27. Cheng, J., Foo, S.W.: Dynamic directional gradient vector flow for snakes. IEEE Trans. Image Process. 15(6), 1563–1571 (2006)

    Article  Google Scholar 

  28. Ray, N., Acton, S.T.: Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours. IEEE Trans. Med. Imaging 23(12), 1466–1478 (2004)

    Article  Google Scholar 

  29. Ray, N., Acton, S.T., Altes, T., et al.: Merging parametric active contours within homogeneous image regions for MRI-based lung segmentation. IEEE TMI 22(2), 189–199 (2003)

    Google Scholar 

  30. Ray, N., Acton, S.T., Ley, K.: Tracking leukocytes in vivo with shape and size constrained active contours. IEEE TMI 21(10), 1222–1235 (2002)

    Google Scholar 

  31. Battiato, S., Farinella, G.M., Puglisi, G.: Saliency-based selection of gradient vector flow paths for content aware image resizing. IEEE Trans. Image Process. 23(5), 2081–2095 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  32. Abdullah, M., Dlay, S., Woo, W., Chambers, J.: Robust Iris segmentation method based on a new active contour force with a noncircular normalization. IEEE Trans. Syst. Man Cybern. Syst. (2017). https://doi.org/10.1109/tsmc.2016.2562500

    Article  Google Scholar 

  33. Jaouen, V., Bert, J., Boussion, N., et al.: Image enhancement with PDEs and nonconservative advection flow fields. EEE Trans. Image Process. (2018). https://doi.org/10.1109/tip.2018.2881838

    Article  MATH  Google Scholar 

  34. Miri, M.S., Robles, V.A., Abràmoff, M.D., et al.: Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes. Comput. Med. Imaging Graph. 55, 87–94 (2017)

    Article  Google Scholar 

  35. Kirimasthong, K., Rodtook, A., Chaumrattanakul, U., Makhanov, S.S.: Phase portrait analysis for automatic initialization of multiple snakes for segmentation of the ultrasound images of breast cancer. Pattern Anal. Appl. 20(1), 239–251 (2017)

    Article  MathSciNet  Google Scholar 

  36. Kirimasthong, K., Rodtook, A., Lohitvisate, W., Makhanov, S.S.: Automatic initialization of active contours in ultrasound images of breast cancer. Pattern Anal. Appl. 21(2), 491–500 (2018)

    Article  MathSciNet  Google Scholar 

  37. Rodtook, A., Kirimasthong, K., Lohitvisate, W.: Automatic initialization of active contours and level set method in ultrasound images of breast abnormalities. Pattern Recognit. (2018). https://doi.org/10.1016/j.patcog.2018.01.032

    Article  Google Scholar 

  38. Bakir, H., Charfi, M., Zrida, J.: Automatic active contour segmentation approach via vector field convolution. SIViP 10(1), 9–18 (2016)

    Article  Google Scholar 

  39. Salimi, A., Pourmina, M.A., Moin, M.-S.: Fully automatic prostate segmentation in MR images using a new hybrid active contour-based approach. Signal Image Video Process. 12(8), 1629–1637 (2018)

    Article  Google Scholar 

  40. Carré, P., Denis, P., Fernandez-Maloigne, C.: Spatial color image processing using Clifford algebras: application to color active contour. SIViP 8(7), 1357–1372 (2014)

    Article  Google Scholar 

  41. Ren, D., Zuo, W., Zhao, X.: Fast gradient vector flow computation based on augmented Lagrangian method. Pattern Recognit. Lett. 34(2), 219–225 (2013)

    Article  Google Scholar 

  42. Park, H., Chung, M.: External force of snake: virtual electric field. IEE Electron. Lett. 38(24), 1500–1502 (2002)

    Article  Google Scholar 

  43. Wang, Y., Zhu, C., Zhang, J.: Convolutional virtual electric field for image segmentation using active contours. PLoS ONE 9(10), e110032 (2014). https://doi.org/10.1371/journal.pone.0110032

    Article  Google Scholar 

  44. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the International Conference on Computer Vision, pp. 839–846 (1998)

  45. Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph. 21(3), 257–266 (2002)

    Article  Google Scholar 

  46. Gunturk, B.K.: Fast bilateral filter with arbitrary range and domain kernels. IEEE Trans. Image Process. 20(9), 2690–2696 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  47. Ghosh, S., Chaudhury, K.N.: On fast bilateral filtering using Fourier kernels. IEEE Signal Process. Lett. 23(5), 570–573 (2016)

    Article  Google Scholar 

  48. Chaudhury, K.N., Sage, D., Unser, M.: Fast O(1) bilateral filtering using trigonometric range kernels. IEEE Trans. Image Process. 20(12), 3376–3382 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  49. Chaudhury, K.N., Dabhade, S.: Fast and provably accurate bilateral filtering. IEEE Trans. Image Process. 25(6), 2519–2528 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  50. Chaudhury, K.N.: Acceleration of the shiftable algorithm for bilateral filtering and nonlocal means. IEEE Trans. Image Process. 22(4), 1291–1300 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  51. Porikli, F.: Constant time O(1) bilateral filtering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

  52. Yang, Q., Tan, K.H., Ahuja, N.: Real-time O(1) bilateral filtering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 557–564 (2009)

  53. Yang, Q.: Hardware-efficient bilateral filtering for stereo matching. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 1026–1032 (2014)

    Article  Google Scholar 

  54. Wang, Y., Jia, Y.: External force for snake: gradient vector convolution. PRICAI2008 LNAI 5351, 466–472 (2008)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Science Foundation of China (No. 61471349), the Basic Discipline Layout Project of Shenzhen City (No. JCYJ20150731154850923), Shenzhen Engineering Laboratory for Key Technologies on Intervention Diagnosis and Treatment Integration, the Key program from NSF of Hebei Province (No. F2016202144), the general program from NSF of Tianjin (No. 16JCYBJC15600) and the youth fund from the Department of Education of Hebei Province (No. QN2016217).

Author information

Author notes

  1. Shoujun Zhou and Yao Lu have contributed equally to this work.

Authors and Affiliations

  1. Research Center for Medical Robotics and Minimally Invasive Surgical Device, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Shoujun Zhou

  2. School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China

    Shoujun Zhou, Nana Li & Yuanquan Wang

  3. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510275, China

    Yao Lu

Authors
  1. Shoujun Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nana Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuanquan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuanquan Wang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, S., Lu, Y., Li, N. et al. Extension of the virtual electric field model using bilateral-like filter for active contours. SIViP 13, 1131–1139 (2019). https://doi.org/10.1007/s11760-019-01456-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01456-x

Keywords

Search

Navigation