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Automatic cell object extraction of red tide algae in microscopic images

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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.

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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.

  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.

  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.

  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.

  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.

  6. Canny J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8 (6): 679–698.

  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.

  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.

  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.

  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.

  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.

  12. Fischer S, Gilomen K, Bunke H. 2002. Identification of diatoms by grid graph matching. Lecture Notes in Computer Science, 2396: 94–103.

  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.

  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.

  15. He K M, Sun J, Tang X O. 2010. Guided image filtering. Lecture Notes in Computer Science, 6311: 1–14.

  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.

  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.

  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.

  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.

  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.

  21. Johnson J L, Padgett M L. 1999. PCNN models and applications. IEEE Transactions on Neural Networks, 10 (3): 480–498.

  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.

  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.

  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.

  25. Margolin R, Zelnik-Manor L, Tal A. 2013a. Saliency for image manipulation. The Visual Computer, 29 (5): 381–392.

  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.

  27. Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9 (1): 62–66.

  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.

  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.

  30. Rother C, Kolmogorov V, Blake A. 2004. “Grabcut”: interactive foreground extraction using iterated Graph Cuts. ACM Transactions on Graphics, 23 (3): 309–314.

  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.

  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.

  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.

  34. Vachier C, Meyer F. 2005. The viscous watershed transform. Journal of Mathematical Imaging and Vision, 22 (2-3): 251–267.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

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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.

Author information

Correspondence to Haiyong Zheng 郑海永.

Additional information

Supported by the National Natural Science Foundation of China (Nos. 61301240, 61271406)

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Yu, K., Ji, G. & Zheng, H. Automatic cell object extraction of red tide algae in microscopic images. Chin. J. Ocean. Limnol. 35, 275–293 (2017). https://doi.org/10.1007/s00343-016-5324-6

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Keywords

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