Segmenting High-quality Digital Images of Stomata using the Wavelet Spot Detection and the Watershed Transform

Kauê T. N. Duarte, Marco A. G. de Carvalho, Paulo S. Martins

Abstract

Stomata are cells mostly found in plant leaves, stems and other organs. They are responsible for controlling the gas exchange process, i.e. the plant absorbs air and water vapor is released through transpiration. Therefore, stomata characteristics such as size and shape are important parameters to be taken into account. In this paper, we present a method (aiming at improved efficiency) to detect and count stomata based on the analysis of the multi-scale properties of the Wavelet, including a spot detection task working in the CIELab colorspace. We also segmented stomata images using the Watershed Transform, assigning each spot initially detected as a marker. Experiments with real and high-quality images were conducted and divided in two phases. In the first, the results were compared to both manual enumeration and another recent method existing in the literature, considering the same dataset. In the second, the segmented results were compared to a gold standard provided by a specialist using the F-Measure. The experimental results demonstrate that the proposed method results in better effectiveness for both stomata detection and segmentation.

References

  1. Arbelaez, P., Maire, M., Fowlkes, C., and Malik, J. (2011). Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5):898-916.
  2. Baeza-Yates, R. A. and Ribeiro-Neto, B. (1999). Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.
  3. Connolly, C. and Fleiss, T. (1997). A study of efficiency and accuracy in the transformation from rgb to cielab color space. IEEE Transactions on Image Processing, 6(7):1046-1048.
  4. Hamamatsu (2016). Hamamatsu Nanozoomer XR microscope. http://www.hamamatsu.com/eu/en/community/ nanozoomer/index.html. [Online; accessed 21- September-2016].
  5. Jian, S., Zhao, C., and Zhao, Y. (2011). Based on remote sensing processing technology estimating leaves stomatal density of populus euphratica. In Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International, pages 547-550.
  6. Laga, H., Shahinnia, F., and Fleury, D. (2014). Image-based plant stomata phenotyping. In Control Automation Robotics Vision (ICARCV), 2014 13th International Conference on, pages 217-222.
  7. Lojk, J., Sajn, L., Aibej, U., and Pavlin, M. (2014). Automatic cell counter for cell viability estimation. In Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention on, pages 239-244.
  8. Mallat, S. G. (1989). A theory for multi-resolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7):674-693.
  9. McGuinness, K. and O'Connor, N. E. (2010). A comparative evaluation of interactive segmentation algorithms. Pattern Recognition, 43(2):434-444.
  10. Meyer, F. (1994). Mathematical morphology and its applications to signal processing topographic distance and watershed lines. Signal Processing, 38(1):113 - 125.
  11. Oliveira, M. C. S., Silva, N. R., Casanova, D., Pinheiro, L. F. S., Kolb, R. M., and Bruno, O. M. (2014). Automatic counting of stomata in epidermis microscopic images. In X Workshop de Visa˜o Computacional.
  12. Olivo-Marin, J.-C. (2002). Extraction of spots in biological images using multiscale products. Pattern Recognition, 35(9):1989 - 1996.
  13. Stepka, K. (2013). Image Analysis: 18th Scandinavian Conference, SCIA 2013, Espoo, Finland, June 17-20, 2013. Proceedings, chapter Automated Cell Counting in Bürker Chamber, pages 236-245. Springer Berlin Heidelberg, Berlin, Heidelberg.
  14. Venkatalakshmi, B. and Thilagavathi, K. (2013). Automatic red blood cell counting using hough transform. In Information Communication Technologies (ICT), 2013 IEEE Conference on, pages 267-271.
  15. Vialet-Chabrand, S. and Brendel, O. (2014). Automatic measurement of stomatal density from microphotographs. Trees, 28(6):1859-1865.
  16. Willmer, C. and Fricker, M. (1996). Stomata. Topics in plant functional biology. Springer.
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Paper Citation


in Harvard Style

Duarte K., Carvalho M. and Martins P. (2017). Segmenting High-quality Digital Images of Stomata using the Wavelet Spot Detection and the Watershed Transform . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 540-547. DOI: 10.5220/0006168105400547


in Bibtex Style

@conference{visapp17,
author={Kauê T. N. Duarte and Marco A. G. de Carvalho and Paulo S. Martins},
title={Segmenting High-quality Digital Images of Stomata using the Wavelet Spot Detection and the Watershed Transform},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={540-547},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006168105400547},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Segmenting High-quality Digital Images of Stomata using the Wavelet Spot Detection and the Watershed Transform
SN - 978-989-758-225-7
AU - Duarte K.
AU - Carvalho M.
AU - Martins P.
PY - 2017
SP - 540
EP - 547
DO - 10.5220/0006168105400547