Advertisement

Chinese Journal of Oceanology and Limnology

, Volume 35, Issue 2, pp 275–293 | Cite as

Automatic cell object extraction of red tide algae in microscopic images

  • Kun Yu (于堃)
  • Guangrong Ji (姬光荣)
  • Haiyong Zheng (郑海永)Email author
Biology
  • 85 Downloads

Abstract

Extracting the cell objects of red tide algae is the most important step in the construction of an automatic microscopic image recognition system for harmful algal blooms. This paper describes a set of composite methods for the automatic segmentation of cells of red tide algae from microscopic images. Depending on the existence of setae, we classify the common marine red tide algae into non-setae algae species and Chaetoceros, and design segmentation strategies for these two categories according to their morphological characteristics. In view of the varied forms and fuzzy edges of non-setae algae, we propose a new multi-scale detection algorithm for algal cell regions based on border- correlation, and further combine this with morphological operations and an improved GrabCut algorithm to segment single-cell and multicell objects. In this process, similarity detection is introduced to eliminate the pseudo cellular regions. For Chaetoceros, owing to the weak grayscale information of their setae and the low contrast between the setae and background, we propose a cell extraction method based on a gray surface orientation angle model. This method constructs a gray surface vector model, and executes the gray mapping of the orientation angles. The obtained gray values are then reconstructed and linearly stretched. Finally, appropriate morphological processing is conducted to preserve the orientation information and tiny features of the setae. Experimental results demonstrate that the proposed methods can effectively remove noise and accurately extract both categories of algae cell objects possessing a complete shape, regular contour, and clear edge. Compared with other advanced segmentation techniques, our methods are more robust when considering images with different appearances and achieve more satisfactory segmentation effects.

Keywords

non-setae algae Chaetoceros cell extraction border-correlation non-interactive GrabCut 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

We would like to sincerely thank the Key Laboratory of Marine Environment and Ecology of the Ministry of Education at Ocean University, China, and the Research Center for Harmful Algae and Aquatic Environment at Jinan University, China, for offering us the samples and equipment needed to observe and capture the optical micrographs of red tide algae used in this study.

References

  1. Achanta R, Hemami S, Estrada F et al. 2009. Frequency-tuned salient region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Miami, FL, USA. p.1597–1604.Google Scholar
  2. Achanta R, Shaji A, Smith K et al. 2012. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (11): 2274–2281.CrossRefGoogle Scholar
  3. Achanta R, Süesstrunk S. 2010. Saliency detection using maximum symmetric surround. In: Proceedings of the 17th IEEE International Conference on Image Processing. IEEE, Hong Kong, China. p.2653–2656.Google Scholar
  4. Blaschko M B, Holness G, Mattar M A et al. 2005. Automatic in situ identification of plankton. In: Proceedings of the 7th IEEE Workshops on Applications of Computer Vision. IEEE, Breckenridge, USA. 1: 79–86.Google Scholar
  5. Boykov Y Y, Jolly M P. 2001. 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. IEEE, Vancouver, Canada, 1: 105–112.Google Scholar
  6. Canny J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8 (6): 679–698.CrossRefGoogle Scholar
  7. Cheng M M, Warrell J, Lin W Y et al. 2013. Efficient salient region detection with soft image abstraction. C IEEE International Conference on Computer Vision. IEEE, Sydney, Australia. p.1529–1536.Google Scholar
  8. Cheng M M, Zhang G X, Mitra N J et al. 2011. Global contrast based salient region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Colorado Springs, USA. p.409–416.Google Scholar
  9. Culverhouse P F, Williams R, Benfield M et al. 2006. Automatic image analysis of plankton: future perspectives. Marine Ecology Progress Series, 312: 297–309.CrossRefGoogle Scholar
  10. Dimitrovski I, Kocev D, Loskovska S et al. 2012. Hierarchical classification of diatom images using ensembles of predictive clustering trees. Ecological Informatics, 7 (1): 19–29.CrossRefGoogle Scholar
  11. Erickson J S, Hashemi N, Sullivan J M et al. 2012. In situ phytoplankton analysis: there’s plenty of room at the bottom. Analytical Chemistry, 84 (2): 839–850.CrossRefGoogle Scholar
  12. Fischer S, Gilomen K, Bunke H. 2002. Identification of diatoms by grid graph matching. Lecture Notes in Computer Science, 2396: 94–103.CrossRefGoogle Scholar
  13. Goferman S, Zelnik-Manor L, Tal A. 2010. Context-aware saliency detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, San Francisco, USA. p.2376–2383.Google Scholar
  14. Harel J, Koch C, Perona P. 2006. Graph-based visual saliency. In: Proceedings of Annual Conference on Neural Information Processing Systems. NIPS, Vancouver, Canada. p.545–552.Google Scholar
  15. He K M, Sun J, Tang X O. 2010. Guided image filtering. Lecture Notes in Computer Science, 6311: 1–14.CrossRefGoogle Scholar
  16. Hou X D, Zhang L Q. 2007. Saliency detection: a spectral residual approach. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Minneapolis, USA. p.1–8.Google Scholar
  17. Itti L, Koch C, Niebur E. 1998. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (11): 1254–1259.CrossRefGoogle Scholar
  18. Jalba A C, Wilkinson M H F, Roerdink J B T M. 2004. Automatic segmentation of diatom images for classification. Microscopy Research and Technique, 65 (1-2): 72–85.CrossRefGoogle Scholar
  19. Jiang B W, Zhang L H, Lu H C et al. 2013. Saliency detection via absorbing Markov chain. In: Proceedings of IEEE International Conference on Computer Vision. IEEE, Sydney, Australia. p.1665–1672.Google Scholar
  20. Jiang H Z, Wang J D, Yuan Z J et al. 2011. Automatic salient object segmentation based on context and shape prior. In: Proceedings of British Machine Vision Conference. BMVA Press, Dundee, Britain. p.110.1-110.12.Google Scholar
  21. Johnson J L, Padgett M L. 1999. PCNN models and applications. IEEE Transactions on Neural Networks, 10 (3): 480–498.CrossRefGoogle Scholar
  22. Li X H, Lu H C, Zhang L H et al. 2013. Saliency detection via dense and sparse reconstruction. In: Proceedings of IEEE International Conference on Computer Vision. IEEE, Sydney, Australia. p.2976–2983.Google Scholar
  23. Luo Q Q, Gao Y H, Luo J F et al. 2011. Automatic identification of diatoms with circular shape using texture analysis. Journal of Software, 6 (3): 428–435.CrossRefGoogle Scholar
  24. Margolin R, Tal A, Zelnik-Manor L. 2013b. What makes a patch distinct? In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Portland, USA. p.1139–1146.Google Scholar
  25. Margolin R, Zelnik-Manor L, Tal A. 2013a. Saliency for image manipulation. The Visual Computer, 29 (5): 381–392.CrossRefGoogle Scholar
  26. Margolin R, Zelnik-Manor L, Tal A. 2014. How to evaluate foreground maps? In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Columbus, USA. p.248–255.Google Scholar
  27. Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9 (1): 62–66.CrossRefGoogle Scholar
  28. Perazzi F, Krähenbühl P, Pritch Y et al. 2012. Saliency filters: contrast based filtering for salient region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Providence, USA. p.733–740.Google Scholar
  29. Rodenacker K, Hense B, Jüetting U et al. 2006. Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation. Microscopy Research and Technique, 69 (9): 708–720.CrossRefGoogle Scholar
  30. Rother C, Kolmogorov V, Blake A. 2004. “Grabcut”: interactive foreground extraction using iterated Graph Cuts. ACM Transactions on Graphics, 23 (3): 309–314.CrossRefGoogle Scholar
  31. Shen X H, Wu Y. 2012. A unified approach to salient object detection via low rank matrix recovery. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Providence, USA. p.853–860.Google Scholar
  32. Sosik H M, Olson R J. 2007. Automated taxonomic classification of phytoplankton sampled with imaging-inflow cytometry. Limnology and Oceanography: Methods, 5 (6): 204–216.CrossRefGoogle Scholar
  33. Tong N, Lu H C, Zhang L H et al. 2014. Saliency detection with multi-scale superpixels. IEEE Signal Processing Letters, 21 (9): 1035–1039.CrossRefGoogle Scholar
  34. Vachier C, Meyer F. 2005. The viscous watershed transform. Journal of Mathematical Imaging and Vision, 22 (2-3): 251–267.CrossRefGoogle Scholar
  35. Verikas A, Gelzinis A, Bacauskiene M et al. 2012. Phase congruency-based detection of circular objects applied to analysis of phytoplankton images. Pattern Recognition, 45 (4): 1659–1670.CrossRefGoogle Scholar
  36. Wei Y C, Wen F, Zhu W J et al. 2012. Geodesic saliency using background priors. Lecture Notes in Computer Science, 7574: 29–42.CrossRefGoogle Scholar
  37. Xie Y L, Lu H C. 2011. Visual saliency detection based on bayesian model. In: Proceedings of the 18th IEEE International Conference on Image Processing. IEEE, Brussels, Belgium. p.645–648.Google Scholar
  38. Yan Q, Xu L, Shi J P et al. 2013. Hierarchical saliency detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Portland, USA. p.1155–1162.Google Scholar
  39. Yang C, Zhang L H, Lu H C et al. 2013b. Saliency detection via graph-based manifold ranking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Portland, USA. p.3166–3173.Google Scholar
  40. Yang C, Zhang L H, Lu H C. 2013a. Graph-regularized saliency detection with convex-hull-based center prior. IEEE Signal Processing Letters, 20 (7): 637–640.CrossRefGoogle Scholar
  41. Zhai Y, Shah M. 2006. Visual attention detection in video sequences using spatiotemporal cues. Annual ACM International Conference on Multimedia, New York, USA. p.815–824.CrossRefGoogle Scholar

Copyright information

© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Kun Yu (于堃)
    • 1
  • Guangrong Ji (姬光荣)
    • 1
  • Haiyong Zheng (郑海永)
    • 1
    Email author
  1. 1.College of Information Science and EngineeringOcean University of ChinaQingdaoChina

Personalised recommendations