Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance

Miguel Carrasco; Patricio A. Toledo; Velázquez, Ramiro; Gutiérrez, Sebastián

doi:10.3390/plants9111613

Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance

Journal

Plants

ISSN

2223-7747

Date Issued

2020

Author(s)

Miguel Carrasco

Patricio A. Toledo

Velázquez, Ramiro

Facultad de Ingeniería - CampAGS

Gutiérrez, Sebastián

Type

text::journal::journal article

DOI

10.3390/plants9111613

URL

https://scripta.up.edu.mx/handle/20.500.12552/3184

Abstract

The CO2 and water vapor exchange between leaf and atmosphere are relevant for plant physiology. This process is done through the stomata. These structures are fundamental in the study of plants since their properties are linked to the evolutionary process of the plant, as well as its environmental and phytohormonal conditions. Stomatal detection is a complex task due to the noise and morphology of the microscopic images. Although in recent years segmentation algorithms have been developed that automate this process, they all use techniques that explore chromatic characteristics. This research explores a unique feature in plants, which corresponds to the stomatal spatial distribution within the leaf structure. Unlike segmentation techniques based on deep learning tools, we emphasize the search for an optimal threshold level, so that a high percentage of stomata can be detected, independent of the size and shape of the stomata. This last feature has not been reported in the literature, except for those results of geometric structure formation in the salt formation and other biological formations.