Skip to main content
Springer Nature Link
Log in

A novel technique to extract accurate cell contours applied for segmentation of phytoplankton images

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Active contour model (ACM) is an image segmentation technique widely applied for object detection. Most of the research in ACM area is dedicated to the development of various energy functions based on physical intuition. Here, instead of constructing a new energy function, we manipulate values of ACM parameters to generate a multitude of potential contours, score them using a machine-learned ranking technique, and select the best contour for each object in question. Several learning-to-rank (L2R) methods are evaluated with a goal to choose the most accurate in assessing the quality of generated contours. Superiority of the proposed segmentation approach over the original boosted edge-based ACM and three ACM implementations using the level-set framework is demonstrated for the task of Prorocentrum minimum cells’ detection in phytoplankton images. Experiments show that diverse set of contour features with grading learned by a variant of multiple additive regression trees (\(\uplambda \)-MART) helped to extract precise contour for 87.6 % of cells tested.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  3. Li, C., Kao, C., Gore, J.C., Ding, Z.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17(10), 1940–1949 (2008)

    Article  MathSciNet  Google Scholar 

  4. Shi, Y., Karl, W.C.: A real-time algorithm for the approximation of level-set-based curve evolution. IEEE Trans. Image Process. 17(5), 645–656 (2008)

    Article  MathSciNet  Google Scholar 

  5. Bernard, O., Friboulet, D., Thevenaz, P., Unser, M.: Variational B-spline level-set: a linear filtering approach for fast deformable model evolution. IEEE Trans. Image Process. 18(6), 1179–1191 (2009)

    Article  MathSciNet  Google Scholar 

  6. Manfredi, C., Bocchi, L., Cantarella, G., Peretti, G.: Videokymographic image processing: objective parameters and user-friendly interface. Biomed. Signal Process. Control 7(2), 192–201 (2012)

    Article  Google Scholar 

  7. Truc, P.T.H., Kim, T.S., Lee, S., Lee, Y.K.: Homogeneity- and density distance-driven active contours for medical image segmentation. Comput. Biol. Med. 41(5), 292–301 (2011)

    Article  Google Scholar 

  8. Shang, Y., Yang, X., Zhu, L., Deklerck, R., Nyssen, E.: Region competition based active contour for medical object extraction. Comput. Med. Imaging Graph. 32(2), 109–117 (2008)

    Article  Google Scholar 

  9. Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N., Ahuja, C.K.: A novel content-based active contour model for brain tumor segmentation. Magn. Reson. Imaging 30, 694–715 (2012)

    Article  Google Scholar 

  10. Savelonas, M.A., Mylona, E.A., Maroulis, D.: Unsupervised 2D gel electrophoresis image segmentation based on active contours. Pattern Recognit. 45(2), 720–731 (2012)

    Article  Google Scholar 

  11. Veronese, E., Stramare, R., Campion, A., Raffeiner, B., Beltrame, V., Scagliori, E., Coran, A., Ciprian, L., Fiocco, U., Grisan, E.: Improved detection of synovial boundaries in ultrasound examination by using a cascade of active-contours. Med. Eng. Phys. 35(2), 188–194 (2012)

    Article  Google Scholar 

  12. Chakraborty, A., Staib, L., Duncan, J.: Deformable boundary finding in medical images by integrating gradient and region information. IEEE Trans. Med. Imaging 15(6), 859–870 (1996)

    Article  Google Scholar 

  13. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)

    Article  MATH  Google Scholar 

  14. Vasilevskiy, A., Siddiqi, K.: Flux-maximizing geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 24(12), 1565–1578 (2002)

    Article  Google Scholar 

  15. Ronfard, R.: Region-based strategies for active contour models. Int. J. Comput. Vis. 13(2), 229–251 (1994)

    Article  Google Scholar 

  16. Paragios, N., Deriche, R.: Geodesic active regions: a new framework to deal with frame partition problems in computer vision. Int. J. Comput. Vis. 46(3), 223–247 (2002)

    Article  MATH  Google Scholar 

  17. Zhang, K., Zhang, L., Song, H., Zhou, W.: Active contours with selective local or global segmentation: a new formulation and level set method. Image Vis. Comput. 28(4), 668–676 (2010)

    Article  Google Scholar 

  18. Yezzi, A., Tsai, A., Willsky, A.: A fully global approach to image segmentation via coupled curve evolution equations. J. Vis. Commun. Image Represent. 13, 195–216 (2002)

    Article  Google Scholar 

  19. Kim, J., Fisher, J.W., Yezzi, A., Çetin, M., Willsky, A.S.: A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Trans. Image Process. 14, 1486–1502 (2005)

    Article  MathSciNet  Google Scholar 

  20. Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int. J. Comput. Vis. 72(2), 195–215 (2007)

    Article  Google Scholar 

  21. Michailovich, O., Rathi, Y., Tannenbaum, A.: Image segmentation using active contours driven by the Bhattacharyya gradient flow. IEEE Trans. Image Process. 16(11), 2787–2801 (2007)

    Article  MathSciNet  Google Scholar 

  22. Sadeghi, F., Izadinia, H., Safabakhsh, R.: A new active contour model based on the conscience, archiving and mean-movement mechanisms and the SOM. Pattern Recognit. Lett. 32, 1622–1634 (2011)

    Article  Google Scholar 

  23. Venkatesh, Y.V., Raja, S.K., Ramya, N.: Multiple contour extraction from graylevel images using an artificial neural network. IEEE Trans. Image Process. 15, 892–899 (2006)

    Article  Google Scholar 

  24. Tao, W., Tai, X.C.: Multiple piecewise constant with geodesic active contours (MPC-GAC) framework for interactive image segmentation using graph cut optimization. Image Vis. Comput. 29, 499–508 (2011)

    Article  Google Scholar 

  25. Fang, W., Chan, K.L.: Incorporating shape prior into geodesic active contours for detecting partially occluded object. Pattern Recognit. 40(8), 2163–2172 (2007)

    Article  MATH  Google Scholar 

  26. Liu, W., Shang, Y., Yang, X., Deklerck, R., Cornelis, J.: A shape prior constraint for implicit active contours. Pattern Recognit. Lett. 32, 1937–1947 (2011)

    Article  Google Scholar 

  27. Yu, T., Luo, J., Ahuja, N.: Search strategies for shape regularized active contour. Comput. Vis. Image Underst. 113(10), 1053–1063 (2009)

    Article  Google Scholar 

  28. Skalski, A., Turcza, P., Zieliski, T., Krolczyk, J., Grodzicki, T.: Left ventricle USG image segmentation using active contour model. Proc. Comput. Sci. 1, 2723–2732 (2012)

    Article  Google Scholar 

  29. Lankton, S., Tannenbaum, A.: Localizing region-based active contours. IEEE Trans. Image Process. 17(11), 2029–2039 (2008)

    Article  MathSciNet  Google Scholar 

  30. Brox, T., Cremers, D.: On the statistical interpretation of the piecewise smooth Mumford–Shah functional. In: Sgallari, F., Murli, A., Paragios, N. (eds.) Scale Space and Variational Methods in Computer Vision. Volume 4485 of Lecture Notes in Computer Science, pp. 203–213. Springer, Berlin, Heidelberg (2007)

    Google Scholar 

  31. Wang, L., Li, C., Sun, Q., Xia, D., Kao, C.Y.: Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput. Med. Imaging Graph. 33, 520–531 (2009)

    Article  Google Scholar 

  32. Zhang, K., Song, H., Zhang, L.: Active contours driven by local image fitting energy. Pattern Recognit. 43(4), 1199–1206 (2010)

    Article  MATH  Google Scholar 

  33. Sum, K., Cheung, P.: Vessel extraction under non-uniform illumination: a level set approach. IEEE Trans. Biomed. Eng. 55(1), 358–360 (2008)

    Article  Google Scholar 

  34. Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J.P., Osher, S.: Fast global minimization of the active contour/snake model. J. Math. Imaging Vis. 28(2), 151–167 (2007)

    Article  MathSciNet  Google Scholar 

  35. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  36. Verikas, A., Gelzinis, A., Bacauskiene, M.: Phase congruency-based detection of circular objects applied to analysis of phytoplankton images. Pattern Recognit. 45(4), 1659–1670 (2012)

    Article  Google Scholar 

  37. Gelzinis, A., Vaiciukynas, E., Bacauskiene, M., Verikas, A., Sulcius, S., Paskauskas, R., Olenina, I.: Boosting performance of the edge-based active contour model applied to phytoplankton images. In: IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 273–277. IEEE, Budapest (2012)

  38. da Fontoura Costa, L., Cesar Jr, R.M.: Shape Classification and Analysis: Theory and Practice, 2nd edn. CRC Press, Inc., Boca Raton (2009)

  39. de Castro Mendes Gomes, G., de Oliveira, V.C., de Almeida, J.M., Goncalves, M.A.: Is learning to rank worth it? A statistical analysis of learning to rank methods in the LETOR benchmarks. J. Inf. Data Manag. 4(1), 57–66 (2013)

    Google Scholar 

  40. Chen, H., He, B., Luo, T., Li, B.: A ranked-based learning approach to automated essay scoring. In: The 2nd International Conference on Cloud and Green Computing (CGC), pp. 448–455 (2012)

  41. Li, B., Xiao, R., Li, Z., Cai, R., Lu, B.L., Zhang, L.: Rank-SIFT: learning to rank repeatable local interest points. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1737–1744 (2011)

  42. Canuto, S.D., Belem, F.M., Almeida, J.M., Goncalves, M.A.: A comparative study of learning-to-rank techniques for tag recommendation. J. Inf. Data Manag. 4(3), 453–468 (2013)

    Google Scholar 

  43. Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM), New York, NY, USA, pp. 621–630 (2009)

  44. Qin, T., Liu, T.Y.: Introducing LETOR 4.0 datasets. Computing Research Repository. Information Retrieval (2013). http://arxiv.org/abs/1306.2597v1. Accessed 20 April 2014

  45. Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  46. Storn, R.: On the usage of differential evolution for function optimization. In: Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 519–523 (1996)

  47. Krink, T., Fogel, B.F.G.B.: Noisy optimization problems—a particular challenge for differential evolution? Congr. Evolut. Comput. (CEC) 1, 332–339 (2004)

    Google Scholar 

  48. Dietenbeck, T., Alessandrini, M., Friboulet, D., Bernard, O.: CREASEG: a free software for the evaluation of image segmentation algorithms based on level-set. In: Proceedings of the 17th IEEE International Conference on Image Processing (ICIP), pp. 665–668. IEEE (2010)

  49. Sommer, C., Straehle, C.N., Kothe, U., Hamprecht, F.A.: Ilastik: Interactive learning and segmentation toolkit. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE, pp. 230–233 (2011)

  50. Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Tinevez, B.S.J.Y., White, D.J., Hartenstein, V., Eliceiri, K., Tomancak, P., Cardona, A.: Fiji: an open-source platform for biological-image analysis. Nat. Methods 9(7), 676–682 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Power Systems, Kaunas University of Technology, Studentu 50, 51368, Kaunas, Lithuania

    Adas Gelzinis, Antanas Verikas, Evaldas Vaiciukynas & Marija Bacauskiene

  2. Centre for Applied Intelligent Systems Research (CAISR), Halmstad University, Kristian IV:s väg 3, PO Box 823, 301 18, Halmstad, Sweden

    Antanas Verikas

  3. Department of Information Systems, Kaunas University of Technology, Studentu 50, 51368, Kaunas, Lithuania

    Evaldas Vaiciukynas

Authors
  1. Adas Gelzinis
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Antanas Verikas
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Evaldas Vaiciukynas
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Marija Bacauskiene
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Antanas Verikas.

Additional information

Microscopy images of Prorocentrum minimum cells were obtained by dr. Ricardas Paskauskas and dr. Sigitas Sulcius at Coastal Research and Planning Institute, Klaipeda University. Funding for this work was provided by a Grant (No. LEK-09/2012) from the Research Council of Lithuania under National Research Programme “Ecosystems in Lithuania: climate change and human impact”.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gelzinis, A., Verikas, A., Vaiciukynas, E. et al. A novel technique to extract accurate cell contours applied for segmentation of phytoplankton images. Machine Vision and Applications 26, 305–315 (2015). https://doi.org/10.1007/s00138-014-0643-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-014-0643-0

Keywords