Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance
The CO<sub>2</sub> 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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/9/11/1613 |
| _version_ | 1797547290377846784 |
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| author | Miguel Carrasco Patricio A. Toledo Ramiro Velázquez Odemir M. Bruno |
| author_facet | Miguel Carrasco Patricio A. Toledo Ramiro Velázquez Odemir M. Bruno |
| author_sort | Miguel Carrasco |
| collection | DOAJ |
| description | The CO<sub>2</sub> 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. |
| first_indexed | 2024-03-10T14:42:14Z |
| format | Article |
| id | doaj.art-b2c113f634294644b1ccd0bffe8874b8 |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-03-10T14:42:14Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj.art-b2c113f634294644b1ccd0bffe8874b82023-11-20T21:44:24ZengMDPI AGPlants2223-77472020-11-01911161310.3390/plants9111613Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency DistanceMiguel Carrasco0Patricio A. Toledo1Ramiro Velázquez2Odemir M. Bruno3Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibañez, Av. Diagonal Las Torres, 2700 Santiago, ChileFacultad de Ingeniería y Ciencias, Universidad Adolfo Ibañez, Av. Diagonal Las Torres, 2700 Santiago, ChileFacultad de Ingeniería Josemaría Escrivá de Balaguer 101, Campus Aguascalientes, Universidad Panamericana, Aguascalientes 20290, MexicoScientific Computing Group, São Carlos Institute of Physics, University of São Paulo, P.O. Box 369, São Carlos, SP 13560-970, BrazilThe CO<sub>2</sub> 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.https://www.mdpi.com/2223-7747/9/11/1613stomatal segmentationimage segmentationDelaunay-Rayleigh frequency |
| spellingShingle | Miguel Carrasco Patricio A. Toledo Ramiro Velázquez Odemir M. Bruno Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance Plants stomatal segmentation image segmentation Delaunay-Rayleigh frequency |
| title | Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance |
| title_full | Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance |
| title_fullStr | Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance |
| title_full_unstemmed | Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance |
| title_short | Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance |
| title_sort | automatic stomatal segmentation based on delaunay rayleigh frequency distance |
| topic | stomatal segmentation image segmentation Delaunay-Rayleigh frequency |
| url | https://www.mdpi.com/2223-7747/9/11/1613 |
| work_keys_str_mv | AT miguelcarrasco automaticstomatalsegmentationbasedondelaunayrayleighfrequencydistance AT patricioatoledo automaticstomatalsegmentationbasedondelaunayrayleighfrequencydistance AT ramirovelazquez automaticstomatalsegmentationbasedondelaunayrayleighfrequencydistance AT odemirmbruno automaticstomatalsegmentationbasedondelaunayrayleighfrequencydistance |