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Erschienen in: Neural Computing and Applications 6/2021

19.06.2020 | Original Article

Simultaneous segmentation of the optic disc and fovea in retinal images using evolutionary algorithms

verfasst von: Enrique J. Carmona, José M. Molina-Casado

Erschienen in: Neural Computing and Applications | Ausgabe 6/2021

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Abstract

In this work, we present a new methodology to simultaneously segment anatomical structures in medical images. Additionally, this methodology is instantiated in a method that is used to simultaneously segment the optic disc (OD) and fovea in retinal images. The OD and fovea are important anatomical structures that must be previously identified in any image-based computer-aided diagnosis system dedicated to diagnosing retinal pathologies that cause vision problems. Basically, the simultaneous segmentation method uses an OD-fovea model and an evolutionary algorithm. On the one hand, the model is built using the intra-structure relational knowledge, associated with each structure, and the inter-structure relational knowledge existing between both and other retinal structures. On the other hand, the evolutionary algorithm (differential evolution) allows us to automatically adjust the instance parameters that best approximate the OD-fovea model in a given retinal image. The method is evaluated in the MESSIDOR public database. Compared with other recent segmentation methods in the related literature, competitive segmentation results are achieved. In particular, a sensitivity and specificity of 0.9072 and 0.9995 are respectively obtained for the OD. Considering a success when the distance between the detected and actual center is less than or equal to \(\eta\) times the OD radius, the success rates obtained for the fovea are 97.3% and 99.0% for \(\eta =1/2\) and \(\eta =1\), respectively. The segmentation average time per image is 29.35 s.
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Metadaten
Titel
Simultaneous segmentation of the optic disc and fovea in retinal images using evolutionary algorithms
verfasst von
Enrique J. Carmona
José M. Molina-Casado
Publikationsdatum
19.06.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05060-w

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