Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Robust and automatic cell detection and segmentation from microscopic images of non-setae phytoplankton species

Saliency-based marker-controlled watershed method was proposed to detect and segment phytoplankton cells from microscopic images of non-setae species. This method first improved IG saliency detection method by combining saturation feature with colour and luminance feature to detect cells from microscopic images uniformly and then produced effective internal and external markers by removing various specific noises in microscopic images for efficient performance of watershed segmentation automatically. The authors built the first benchmark dataset for cell detection and segmentation, including 240 microscopic images across multiple phytoplankton species with pixel-wise cell regions labelled by a taxonomist, to evaluate their method. They compared their cell detection method with seven popular saliency detection methods and their cell segmentation method with six commonly used segmentation methods. The quantitative comparison validates that their method performs better on cell detection in terms of robustness and uniformity and cell segmentation in terms of accuracy and completeness. The qualitative results show that their improved saliency detection method can detect and highlight all cells, and the following marker selection scheme can remove the corner noise caused by illumination, the small noise caused by specks, and debris, as well as deal with blurred edges.

References

    1. 1)
      • 6. du Buf, H., Bayer, M.M.: ‘Automatic diatom identification’ (World Scientific, 2002).
    2. 2)
      • 13. Zheng, H., Zhao, H., Sun, X., et al: ‘Automatic setae segmentation from Chaetoceros microscopic images’, Microsc. Res. Techniq., 2014, 77, (9), pp. 684690.
    3. 3)
      • 36. Ridler, T.W., Calvard, S.: ‘Picture thresholding using an iterative selection method’, IEEE Trans. Syst. Man Cybern., 1978, 8, (8), pp. 630632.
    4. 4)
      • 34. Cheng, M.-M., Mitra, N.J., Huang, X., et al: ‘Global contrast based salient region detection’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (3), pp. 569582.
    5. 5)
      • 40. Hartigan, J.A., Wong, M.A.: ‘Algorithm AS 136: a k-means clustering algorithm’, J. R. Stat. Soc. Ser. C Appl. Stat., 1979, 28, (1), pp. 100108.
    6. 6)
      • 23. Verma, S., Khare, D., Gupta, R., et al: ‘Analysis of image segmentation algorithms using MATLAB’. Proc. Third Int. Conf. Trends in Information, Telecommunication and Computing, Bangalore, India, 3–4 August 2012, 2013, pp. 163172.
    7. 7)
      • 12. Jalba, A.C., Wilkinson, M.H.F., Roerdink, J.B.T.M.: ‘Automatic segmentation of diatom images for classification’, Microsc. Res. Tech., 2004, 65, pp. 7285.
    8. 8)
      • 9. MacLeod, N., Benfield, M., Culverhouse, P.: ‘Time to automate identification’, Nature, 2010, 467, (7312), pp. 154155.
    9. 9)
      • 3. Reynaud, E.G.: ‘Imaging marine life: macrophotography and microscopy approaches for marine Biology’ (Wiley-Blackwell, 2013).
    10. 10)
      • 35. Canny, J.: ‘A computational approach to edge detection’, IEEE Trans. Pattern Anal. Mach. Intell., 1986, 8, (6), pp. 679698.
    11. 11)
      • 39. Kittler, J., Illingworth, J.: ‘Minimum error thresholding’, Pattern Recogn., 1986, 19, (1), pp. 4147.
    12. 12)
      • 27. Li, Y., Hou, X., Koch, C., et al: ‘The secrets of salient object segmentation’. Proc. IEEE Conf. Computer Vision and Pattern Recognition IEEE, Columbus, OH, USA, 24–27 June 2014, pp. 280287.
    13. 13)
      • 24. Verikas, A., Gelzinis, A., Bacauskiene, M., et al: ‘Phase congruency-based detection of circular objects applied to analysis of phytoplankton images’, Pattern Recogn., 2012, 45, (4), pp. 16591670.
    14. 14)
      • 1. Olson, R.J., Sosik, H.M.: ‘A submersible imaging-in-flow instrument to analyze nano-and-microplankton: imaging flowCytobot’, Limnol. Oceanogr.: Methods, 2007, 5, pp. 195203.
    15. 15)
      • 32. Hou, X., Harel, J., Koch, C.: ‘Image signature: highlighting sparse salient regions’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (1), pp. 194201.
    16. 16)
      • 5. Jaffe, J.S.: ‘Underwater optical imaging: the past, the present, and the prospects’, IEEE J. Oceanic Eng., 2015, 40, (3), pp. 683700.
    17. 17)
      • 41. Dubuisson, M.-P., Jain, A.K.: ‘A modified Hausdorff distance for object matching’. Proc. 12th IAPR Int. Conf. Pattern Recognition IEEE, Jerusalem, Israel, 9–13 October 1994, pp. 566568.
    18. 18)
      • 10. Santhi, N., Pradeepa, C., Subashini, P., et al: ‘Automatic identification of algal community from microscopic images’, Bioinf. Biol. Insights, 2013, 7, pp. 327334.
    19. 19)
      • 22. Kloster, M., Kauer, G., Beszteri, B.: ‘SHERPA: an image segmentation and outline feature extraction tool for diatoms and other objects’, BMC Bioinformatics, 2014, 15, (1), p. 218.
    20. 20)
      • 43. Jaffe, J.S., Roberts, P.L.D., Ratelle, D., et al: ‘Scripps plankton camera system’,(2015). Available at http://spc.ucsd.edu/.
    21. 21)
      • 25. Fischer, S., Shahbazkia, H.R., Bunke, H.: ‘Contour extraction’, In du Buf, H., Bayer, M.M. (Eds.): ‘Automatic diatom identification’ (World Scientific, Singapore, 2002), pp. 93107.
    22. 22)
      • 31. Guo, C., Ma, Q., Zhang, L.: ‘Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform’. Proc. IEEE Conf. Computer Vision and Pattern Recognition IEEE, Anchorage, AK, USA, 24–26 June 2008, pp. 18.
    23. 23)
      • 17. Bi, H., Guo, Z., Benfield, M.C., et al: ‘A semi-automated image analysis procedure for in situ plankton imaging systems’, PLoS One, 2015, 10, (5), p. e0127121.
    24. 24)
      • 19. Sosik, H.M., Olson, R.J.: ‘Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry’, Limnol. Oceanogr. Methods, 2007, 5, pp. 204216.
    25. 25)
      • 37. Otsu, N.: ‘A threshold selection method from gray-level histograms’, IEEE Trans. Syst. Man Cybern., 1979, 9, (1), pp. 6266.
    26. 26)
      • 2. Erickson, J.S., Hashemi, N., Sullivan, J.M., et al: ‘In situ phytoplankton analysis: theres plenty of room at the bottom’, Anal. Chem., 2011, 84, (2), pp. 839850.
    27. 27)
      • 28. Achanta, R., Hemami, S., Estrada, F., et al: ‘Frequency-tuned salient region detection’. Proc. IEEE Conf. Computer Vision and Pattern Recognition IEEE, Miami, FL, USA, 20–25 June 2009, pp. 15971604.
    28. 28)
      • 4. Watson, J.E., Zielinski, O.: ‘Subsea optics and imaging’ (Elsevier, Singapore, 2013).
    29. 29)
      • 14. Embleton, K.V., Gibson, C.E., Heaney, S.I.: ‘Automated counting of phytoplankton by pattern recognition: a comparison with a manual counting method’, J. Plankton. Res., 2003, 25, (6), pp. 669681.
    30. 30)
      • 20. Luo, Q., Gao, Y., Luo, J., et al: ‘Automatic identification of diatoms with circular shape using texture analysis’, J. Softw., 2011, 6, (3), pp. 428435.
    31. 31)
      • 18. Blaschko, M.B., Holness, G., Mattar, M.A., et al: ‘Automatic in situ identification of plankton’. Proc. Seventh IEEE Workshops on Application of Computer Vision IEEE, Breckenridge, CO, USA, 5–7 January 2005, vol. 1, pp. 7986.
    32. 32)
      • 38. Sauvola, J., Pietikäinen, M.: ‘Adaptive document image binarization’, Pattern Recogn., 2000, 33, (2), pp. 225236.
    33. 33)
      • 21. Mosleh, M.A.A., Manssor, H., Malek, S., et al: ‘A preliminary study on automated freshwater algae recognition and classification system’, BMC Bioinform., 2012, 13, (Suppl. 17), p. S25.
    34. 34)
      • 44. Alexe, B., Deselaers, T., Ferrari, V.: ‘What is an object?’. Proc. IEEE Conf. Computer Vision and Pattern Recognition: IEEE, San Francisco, USA, 13–18 June 2010, pp. 7380.
    35. 35)
      • 42. Davis, C.S., Thwaites, F.T., Gallager, S.M., et al: ‘A three-axis fast-tow digital video plankton recorder for rapid surveys of plankton taxa and hydrography’, Limnol. Oceanogr. Methods, 2005, 3, pp. 5974.
    36. 36)
      • 26. Dimitrovski, I., Kocev, D., Loskovska, S., et al: ‘Hierarchical classification of diatom images using ensembles of predictive clustering trees’, Ecol. Inform., 2012, 7, (1), pp. 1929.
    37. 37)
      • 33. Li, J., Levine, M.D., An, X., et al: ‘Visual saliency based on scale-space analysis in the frequency domain’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (4), pp. 9961010.
    38. 38)
      • 7. Culverhouse, P.F., Williams, R., Benfield, M., et al: ‘Automatic image analysis of plankton: future perspectives’, Mar. Ecol. Prog. Ser., 2006, 312, pp. 297309.
    39. 39)
      • 11. Verikas, A., Gelzinis, A., Bacauskiene, M., et al: ‘An integrated approach to analysis of phytoplankton images’, IEEE J. Oceanic Eng., 2015, 40(2), pp. 315326.
    40. 40)
      • 15. Davis, C.S., Hu, Q., Gallager, S.M., et al: ‘Real-time observation of taxa-specific plankton distributions: an optical sampling method’, Mar. Ecol. Prog. Ser., 2004, 284, pp. 7796.
    41. 41)
      • 8. Benfield, M.C., Grosjean, P., Culverhouse, P.F., et al: ‘RAPID: research on automated plankton identification’, Oceanography, 2007, 20, (2), pp. 172187.
    42. 42)
      • 16. Rodenacker, K., Hense, B., Jütting, U., et al: ‘Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation’, Microsc. Res. Techniq., 2006, 69, (9), pp. 708720.
    43. 43)
      • 29. Itti, L., Koch, C., Niebur, E.: ‘A model of saliency-based visual attention for rapid scene analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (11), pp. 12541259.
    44. 44)
      • 30. Hou, X., Zhang, L.: ‘Saliency detection: a spectral residual approach’. Proc. of IEEE Conf. on Computer Vision and Pattern Recognition IEEE, Minneapolis, Minnesota, USA, 18–23 June 2007, pp. 18.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2017.0127
Loading

Related content

content/journals/10.1049/iet-ipr.2017.0127
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address