access icon free Open snake model based on global guidance field for embryo vessel location

© The Institution of Engineering and Technology
Received 14/11/2016, Accepted 20/07/2017, Revised 14/06/2017, Published 01/08/2017

The development of vessels can provide important information about the growth status of animal embryos. It is, therefore, important to automatically locate the deformed vessel branches from the embryo images. However, very few vessel detectors can accurately locate all vessel branches when the captured images are low quality and the implied vessel shapes are complex. In this study, a new framework consisting of vessel region extraction and snake shape optimisation is proposed. The main contribution in this detector is a novel open snake model based on the global guidance field and deformation template initialisation. Experimental results on a specific application of an embryo vessel database [Database and source codes: https://github.com/wcxie/Egg-embryro-vessel-location/.] demonstrate that the proposed algorithm not only locates the vessel shape properly but also obtains the orientations of embryo vessel branches accurately. Comparison to traditional guidance fields and the active appearance model illustrates the effectiveness and competitiveness of the proposed model.

Inspec keywords: edge detection; biomedical MRI; object detection; optimisation; bioinformatics; blood vessels

Other keywords: vessel region extraction; deformation template initialisation; animal embryos growth status; embryo vessel database; embryo vessel location; open snake model; global guidance field; automatic deformed vessel branch localization; vessel development; snake shape optimisation

Subjects: Computer vision and image processing techniques; Image recognition; Biology and medical computing; Optimisation techniques; Biomedical magnetic resonance imaging and spectroscopy; Optimisation techniques

References

    1. 1)
      • 24. Rez, P., Gangnet, M., Blake, A.: ‘Poisson image editing’, ACM Trans. Graph., 2003, 22, (3), pp. 313–318.
    2. 2)
      • 19. Li, C., Liu, J., Fox, M.D.: ‘Segmentation of external force field for automatic initialization and splitting of snakes’, Pattern Recognit., 2005, 38, (11), pp. 1947–1960.
    3. 3)
      • 6. Li, C., Huang, R., Ding, Z., et al: ‘A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI’, IEEE Trans. Image Process., 2011, 20, (7), pp. 2007–2016.
    4. 4)
      • 10. Geng, W., Cosman, P., Palm, M., et al: ‘Caenorhabditis elegans egg-laying detection and behavior study using image analysis’, EURASIP J. Appl. Signal. Process., 2005, 14, pp. 2229–2240.
    5. 5)
      • 11. Geng, W., Cosman, P., Berry, C.C., et al: ‘Automatic tracking, feature extraction and classification of C. elegans phenotypes’, IEEE Trans. Biomed. Eng., 2004, 51, (10), pp. 1811–1820.
    6. 6)
      • 13. Tzimiropoulos, G., Pantic, M.: ‘Optimization problems for fast AAM fitting in-the-wild’. Proc. IEEE Int. Conf. Computer Vision (ICCV), Sydney, Australia, 2013, pp. 593–600.
    7. 7)
      • 26. Weng, Y., Xu, W., Wu, Y., et al: ‘2D shape deformation using nonlinear least squares optimization’, Vis. Comput., 2006, 22, (9–11), pp. 653–660.
    8. 8)
      • 7. Duan, J., Pan, Z., Yin, X., et al: ‘Some fast projection methods based on chan–vese model for image segmentation’, EURASIP J. Image Video Process., 2014, 2014, (1), p. 7.
    9. 9)
      • 20. Hou, Z., Han, C.: ‘Force field analysis snake: an improved parametric active contour model’, Pattern Recognit. Lett., 2005, 26, (5), pp. 513–526.
    10. 10)
      • 14. Kass, M., Witkin, A., Terzopoulos, D.: ‘Snakes: active contour models’, Int. J. Comput. Vis., 1988, 1, (4), pp. 321–331.
    11. 11)
      • 2. Bai, X., Latecki, L.J., Liu, W.: ‘Skeleton pruning by contour partitioning with discrete curve evolution’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (3), pp. 449–462.
    12. 12)
      • 23. Zhang, W., Pan, L., Tu, K., et al: ‘Comparison of spectral and image morphological analysis for egg early hatching property detection based on hyperspectral imaging’, PLoS One, 2014, 9, (2), p. e88659.
    13. 13)
      • 21. Li, K., Lu, Z., Liu, W., et al: ‘Cytoplasm and nucleus segmentation in cervical smear images using radiating GVF Snake’, Pattern Recognit., 2012, 45, (4), pp. 1255–1264.
    14. 14)
      • 8. Tan, L., Pan, Z., Liu, W., et al: ‘Image segmentation with depth information via simplified variational level set formulation’, J. Math. Imaging Vis., 2017, doi:10.1007/s10851-017-0735-3.
    15. 15)
      • 16. Wang, Y., Narayanaswamy, A., Tsai, C., et al: ‘A broadly applicable 3-D neuron tracing method based on open-curve snake’, Neuroinformatics, 2011, 9, (2-3), pp. 193–217.
    16. 16)
      • 1. Kirbas, C., Quek, F.: ‘A review of vessel extraction techniques and algorithms’, ACM Comput. Surv., 2004, 36, (2), pp. 81–121.
    17. 17)
      • 15. Li, H., Shen, T., Smith, M.B., et al: ‘Automated actin filament segmentation, tracking and tip elongation measurements based on open active contour models’. Proc. IEEE Int. Symp. Biomedical Imaging: From Nano to Macro, Boston, MA, USA, 2009, pp. 1302–1305.
    18. 18)
      • 17. Bresson, X., Esedoglu, S., Vandergheynst, P., et al: ‘Fast global minimization of the active contour/snake model’, J. Math. Imaging Vis., 2007, 28, (2), pp. 151–167.
    19. 19)
      • 12. Lin, Z., Davis, L.S.: ‘Shape-based human detection and segmentation via hierarchical part-template matching’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 32, (4), pp. 604–618.
    20. 20)
      • 9. Wang, Q., Boyer, K.L.: ‘The active geometric shape model: a new robust deformable shape model and its applications’, Comput. Vis. Image Underst., 2012, 116, (12), pp. 1178–1194.
    21. 21)
      • 18. Xu, C., Prince, J.L.: ‘Snakes, shapes, and gradient vector flow’, IEEE Trans. Image Process., 1998, 7, (3), pp. 359–369.
    22. 22)
      • 27. Zhou, H., Li, X., Schaefer, G., et al: ‘Mean shift based gradient vector flow for image segmentation’, Comput. Vis. Image Underst., 2013, 117, (9), pp. 1004–1016.
    23. 23)
      • 4. Al-Rawi, M., Qutaishat, M., Arrar, M.: ‘An improved matched filter for blood vessel detection of digital retinal images’, Comput. Biol. Med., 2007, 37, (2), pp. 262–267.
    24. 24)
      • 5. Zhao, Y.Q., Wang, X.H., Wang, X.F., et al: ‘Retinal vessels segmentation based on level set and region growing’, Pattern Recognit., 2014, 47, (7), pp. 2437–2446.
    25. 25)
      • 22. Meyer, F.G., Constable, R.T., Sinusas, A.J., et al: ‘Tracking myocardial deformation using phase contrast MR velocity fields: a stochastic approach’, IEEE Trans. Image Process., 1996, 15, (4), pp. 453–465.
    26. 26)
      • 3. Sezgin, M., Sankur, B.: ‘Survey over image thresholding techniques and quantitative performance evaluation’, J. Electron. Imaging, 2004, 13, (1), pp. 146–168.
    27. 27)
      • 25. Duan, J., Qiu, Z., Lu, W., et al: ‘An edge-weighted second order variational model for image decomposition.’, Digit. Signal Process., 2016, 49, pp. 162–181.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2016.0411
Loading

Related content

content/journals/10.1049/iet-cvi.2016.0411
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading