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Feasibility study on automated recognition of allergenic pollen: grass, birch and mugwort

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Abstract

Quantification of airborne pollen is an important tool in scientific research and patient care in allergy. The currently available method relies on microscopic examination of pollen slides, performed by qualified researchers. Although highly reliable, the method is labor intensive and requires extensive training of the researchers involved. In an approach to develop alternative detection methods, we performed a feasibility study on the automated recognition of the allergenic relevant pollen, grass, birch, and mugwort, by utilizing digital image analysis and pattern recognition tools. Of a total of 254 pollen samples (including 79 of grass, 79 of birch and 96 of mugwort), 97.2% were recognized correctly. This encouraging result provides a promising prospect for future developments.

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Abbreviations

KNNC:

K nearest neighbor classifier

LNC:

Linear normal classifier

NMC:

Nearest mean classifier

QNC:

Quadratic normal classifier

References

  • Bonton, P., Boucher, A., Thonnat, M., Belmonte, J., Galan, C., Bonton, P., & Tomczak, R. (2001). Color image in 2D and 3D microscopy for the automation of pollen rate measurement. Image Analysis and Stereology, 20(Suppl 1), 527–532.

    Google Scholar 

  • Boucher, A., Hidalgo, P. J., Thonnat, M., Belmonte, J., Galan, C., Bonton, P., & Tomczak, R. (2002). Development of a semi-automatic system for pollen recognition. Aerobiologia, 18, 195–201.

    Article  Google Scholar 

  • Cernadas, E., Formella, A., & Rodríguez-Damiá, M. (2004). Pollen classification using brightness-based and shape-based descriptors. 17th International Conference on Pattern Recognition (ICPR ’04) (vol. 2, pp. 212–215). Cambridge, UK.

  • Costa, L. F., & Cesar, R. M. (2001a). Shape characterization: some general descriptors. In Shape analysis and classification (pp. 422–439). Washington, DC: CRC Press.

    Google Scholar 

  • Costa, L. F., & Cesar, R. M. (2001b). 2D Shape representation: Hough transforms. In Shape analysis and classification (pp. 376–400). Washington, DC: CRC Press.

    Google Scholar 

  • Dasarathy, B. V. (1991). Nearest neighbour norms: NN pattern classification techniques. Los Alamitos, CA: IEEE Computer Society Press.

    Google Scholar 

  • Egan, J. P. (1975). Signal detection theory and ROC analysis. Academic, New York, USA.

    Google Scholar 

  • Hough, P. C. V. (1962). Methods and means for recognizing complex patterns. U.S. Patent 3,069,654.

  • Illingworth, J., & Kittler, J. (1988). A survey of the Hough transform. Computer Vision, Graphics, and Image Processing, 44, 87–116.

    Article  Google Scholar 

  • Kittler, J. (1975). Mathematical methods of feature selection in pattern recognition. International Journal of Man–Machine Studies, 7, 609–637.

    Article  Google Scholar 

  • Lachenbruch, P. A., & Mickey, M. R. (1968). Estimation of error rates in discriminant analysis. Technometrics, 10, 1–11.

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  • McLachlan, G. J. (1992). Discriminant analysis and statistical pattern recognition. New York: Wiley.

    Book  Google Scholar 

  • Pratt, W. K. (1978). Image detection and registration: template matching. Digital image processing (pp. 651–653). New York, USA: Wiley.

    Google Scholar 

  • Ronneberger, O., Schultz, E., & Burkhardt, H. (2002). Automated pollen recognition using 3D volume images from fluorescence microscopy. Aerobiologia, 18, 107–115.

    Article  Google Scholar 

  • Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B, 36, 111–147.

    Google Scholar 

  • Webb, A. R. (2002a). Clustering: hierarchical methods. Statistical pattern recognition (pp. 362–370). England: Wiley.

    Google Scholar 

  • Webb, A. R. (2002b). Density estimation—parametric: normal-based models. Statistical pattern recognition (pp. 34–40). England: Wiley.

    Google Scholar 

  • Weber, R. W. (1998). Pollen identification. Annals of Allergy, Asthma & Immunology, 80, 141–145.

    Article  CAS  Google Scholar 

  • Young, I. T., Gerbrands, J. J., & Van Vliet, L. J. (1998a). Chapter 51.10: Techniques: shading correction. In V. K. Madisetti & D. B. Williams (Eds.), The digital signal processing handbook (pp. 51:63–51:80). USA: CRC Pr I LIc.

    Google Scholar 

  • Young, I. T., Gerbrands, J. J., & Van Vliet, L. J. (1998b). Chapter 51.10: Techniques: segmentation. In V. K. Madisetti & D. B. Williams (Eds.), The digital signal processing handbook (pp. 51:63–51:80). USA: CRC Pr I LIc.

    Google Scholar 

  • Young, I. T., Gerbrands, J. J., & Van Vliet, L. J. (1998c). Chapter 51.9: Algorithms. In V. K. Madisetti & D. B. Williams (Eds.), The digital signal processing handbook (pp 51:33–51:63). USA: CRC Pr I LIc.

    Google Scholar 

  • Zhang, Y., & Wang, R. (2004). A combined method for texture analysis and its application. The International Conference on Computational Science (ICCS), part I (pp. 413–416). Krakow, Poland.

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Author notes

  1. Chun Chen

    Present address: Signals & Systems group, University of Twente, Haaksbergerstraat 174-613, 7513EB, Enschede, The Netherlands

Authors and Affiliations

  1. Information and Communication Theory Group, Delft University of Technology, P.O. Box 5031, 2600 GA, Delft, The Netherlands

    Chun Chen, Emile A. Hendriks & Robert P. W. Duin

  2. Department of Pulmonology, Leiden University Medical Center, P.O. Box 9600, 2300 RC, Leiden, The Netherlands

    Pieter S. Hiemstra & Letty A. de Weger

  3. Department of Radiology, Division of Image Processing, Leiden University Medical Center, P.O. Box 9600, 2300 RC, Leiden, The Netherlands

    Johan H. C. Reiber & Berend C. Stoel

Authors
  1. Chun Chen
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  2. Emile A. Hendriks
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  3. Robert P. W. Duin
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  4. Johan H. C. Reiber
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  5. Pieter S. Hiemstra
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Correspondence to Berend C. Stoel.

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Chen, C., Hendriks, E.A., Duin, R.P.W. et al. Feasibility study on automated recognition of allergenic pollen: grass, birch and mugwort. Aerobiologia 22, 275–284 (2006). https://doi.org/10.1007/s10453-006-9040-0

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  • DOI: https://doi.org/10.1007/s10453-006-9040-0

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