HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

An approach to automatic classification of Culicoides species by learning the wing morphology

Abstract : Fast and accurate identification of biting midges is crucial in the study of Culicoides-borne diseases. In this work, we propose a two-stage method for automatically analyzing Culicoides (Diptera: Ceratopogonidae) species. First, an image preprocessing task composed of median and Wiener filters followed by equalization and morphological operations is used to improve the quality of the wing image in order to allow an adequate segmentation of particles of interest. Then, the segmentation of the zones of interest inside the biting midge wing is made using the watershed transform. The proposed method is able to produce optimal feature vectors that help to identify Culicoides species. A database containing wing images of C. obsoletus, C. pusillus, C. foxi, and C. insignis species was used to test its performance. Feature relevance analysis indicated that the mean of hydraulic radius and eccentricity were relevant for the decision boundary between C. obsoletus and C. pusillus species. In contrast, the number of particles and the mean of the hydraulic radius was relevant for deciding between C. foxi and C. insignis species. Meanwhile, for distinguishing among the four species, the number of particles and zones, and the mean of circularity were the most relevant features. The linear discriminant analysis classifier was the best model for the three experimental classification scenarios previously described, achieving averaged areas under the receiver operating characteristic curve of 0.98, 0.90, and 0.96, respectively.
Document type :
Journal articles
Complete list of metadata

https://hal.univ-reims.fr/hal-03407000
Contributor : Bu De Reims Champagne-Ardenne Connect in order to contact the contributor
Submitted on : Thursday, October 28, 2021 - 11:10:51 AM
Last modification on : Tuesday, February 8, 2022 - 2:16:20 PM
Long-term archiving on: : Saturday, January 29, 2022 - 6:47:28 PM

File

2020_pone.0241798.pdf
Publisher files allowed on an open archive

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Collections

Citation

Pablo Venegas, Noel Pérez, Sonia Zapata, Juan Mosquera, Denis Augot, et al.. An approach to automatic classification of Culicoides species by learning the wing morphology. PLoS ONE, Public Library of Science, 2020, 15 (11), pp.e0241798. ⟨10.1371/journal.pone.0241798⟩. ⟨hal-03407000⟩

Share

Metrics

Record views

15

Files downloads

14