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Detection of Pollen Grains in Digital Microscopy Images by Means of Modified Histogram Thresholding

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11114))

Abstract

The paper describes and investigates the application of the algorithm for the detection and extraction of pollen contour shapes in digital microscopic images. This is the first step in the process of identification of pollen grains in order to obtain a method for automatic or semi-automatic analysis of air samples. The final approach is supposed to support this process by recognizing pollen types in digital microscopic images. The applied segmentation approach is based on the Modified Histogram Thresholding, previously employed in the extraction of red blood cells for the automatic diagnosis of certain diseases based on the erythrocyte shapes.

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References

  1. Frejlichowski, D.: Pre-processing, extraction and recognition of binary erythrocyte shapes for computer-assisted diagnosis based on MGG images. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6374, pp. 368–375. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15910-7_42

    Chapter  Google Scholar 

  2. Chen, C., et al.: Feasibility study on automated recognition of allergenic pollen: grass, birch and mugwort. Aerobiologia 22(4), 275–284 (2006)

    Article  Google Scholar 

  3. Damian, M., Cernadas, E., Formilla, A., Otero, P.M.: Pollen classification of three types of plants of the family Urticaceae. In: Proceedings of 12th Portuguese conference on pattern recognition, Aveiro (2004)

    Google Scholar 

  4. Kumar, S., Ong, S.H., Ranganath, S., Chew, F.T., Ong, T.C.: Segmentation of microscope cell images via adaptive eigenfilters. In: Proceedings of International Conference on Image Processing, ICIP 2004, vol. 1, pp. 135–138 (2004)

    Google Scholar 

  5. Boucher, A.: Development of a semi-automatic system for pollen recognition. Aerobiologia 18(3–4), 195–201 (2002)

    Article  Google Scholar 

  6. Ronneberger, O., Wang, Q., Burkhardt, H.: Fast and robust segmentation of spherical particles in volumetric data sets from brightfield microscopy. In: Proceedings of 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2008, pp. 372–375 (2008)

    Google Scholar 

  7. Ronneberger, O., Wang, Q., Burkhardt, H.: 3D invariants with high robustness to local deformations for automated pollen recognition. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 425–435. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74936-3_43

    Chapter  Google Scholar 

  8. Travieso, C.M., Briceno, J.C., Ticay-Rivas, J.R., Alonso, J.B.: Pollen classification based on contour features. In: Proceedings of 15th International Conference on Intelligent Engineering Systems, Poprad, Slovakia, pp. 17–21 (2011)

    Google Scholar 

  9. Bonton, P., et al.: Colour image in 2D and 3D microscopy for the automation of pollen rate measurement. Image Anal. Ster. 21(Suppl. 1), 25–30 (2001)

    MATH  Google Scholar 

  10. Holt, K., Allen, G., Hodgson, R., Marsland, S., Flenley, J.: Progress towards an automated trainable pollen location and classifier system for use in the palynology laboratory. Rev. Palaeobot. Palynol. 167(3–4), 175–183 (2011)

    Article  Google Scholar 

  11. Li, P., Flenley, J.R.: Pollen texture identification using neural networks. Grana 38(1), 59–64 (1999)

    Article  Google Scholar 

  12. Lagerstrom, R., et al.: Pollen image classification using the classifynder system: algorithm comparison and a case study on New Zealand honey. In: Sun, C., Bednarz, T., Pham, T.D., Vallotton, P., Wang, D. (eds.) Signal and Image Analysis for Biomedical and Life Sciences. AEMB, vol. 823, pp. 207–226. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-10984-8_12

    Chapter  Google Scholar 

  13. Ronneberger, O., Burkhardt, H., Schultz, E.: General-purpose object recognition in 3D volume data sets using gray-scale invariants — classification of airborne pollen-grains recorded with a confocal laser scanning microscope. In: Proceedings of the IEEE 16th International Conference on Pattern Recognition, vol. 2, pp. 290–295 (2002)

    Google Scholar 

  14. Nguyen, N.R., Donalson-Matasci, M., Shin, M.C.: Improving pollen classification with less training effort. In: Proceedings of IEEE Workshop on Applications of Computer Vision (WACV), pp. 421–426 (2013)

    Google Scholar 

  15. Arias, D.G., Cirne, M.V.M., Chire, J.E., Pedrini, H.: Classification of pollen grain images based on an ensemble of classifiers. In: Proceedings of 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 234–240 (2017)

    Google Scholar 

  16. Frejlichowski, D.: Identification of erythrocyte types in greyscale MGG images for computer-assisted diagnosis. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 636–643. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21257-4_79

    Chapter  MATH  Google Scholar 

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Correspondence to Dariusz Frejlichowski .

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Frejlichowski, D. (2018). Detection of Pollen Grains in Digital Microscopy Images by Means of Modified Histogram Thresholding. In: Chmielewski, L., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A., Petkov, N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science(), vol 11114. Springer, Cham. https://doi.org/10.1007/978-3-030-00692-1_27

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  • DOI: https://doi.org/10.1007/978-3-030-00692-1_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00691-4

  • Online ISBN: 978-3-030-00692-1

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