Segmenting High-quality Digital Images of Stomata using the Wavelet
Spot Detection and the Watershed Transform
by Duarte K. T. N., de Carvallo M. A.G., Martins P. S. (2017)
Kaue T. N. Duarte, Marco A. G. de Carvalho, Paulo S. Martins,
University of Campinas (UNICAMP), School of Technology, R. Paschoal Marmo, 1888, 13484 Limeira, Brazil
In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), pages 540-547 – ISBN: 978-989-758-225-7 – DOI: 10.5220/0006168105400547 –
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.