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http://hdl.handle.net/2183/36838

Automatic Detection of Blood Vessels in Retinal OCT Images

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Moura_Joaquimde_2017_Automatic_Detection_of_Blood_Vessels_in_Retinal_OCT_Images.pdf (2.47 MB)

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URI: http://hdl.handle.net/2183/36838
DOI: 10.1007/978-3-319-59773-7_1

Publication date

2017-06

Authors

Rouco, José
Penedo, Manuel

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Bibliographic citation

Moura, J. de, Novo, J., Rouco, J., Penedo, M.G., Ortega, M. (2017). Automatic Detection of Blood Vessels in Retinal OCT Images. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science, vol 10338. Springer, Cham. https://doi.org/10.1007/978-3-319-59773-7_1

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Academic degree

Abstract

[Abstract]: The eye is a non-invasive window where clinicians can observe and study in vivo the retinal vasculature, allowing the early detection of different relevant pathologies. In this paper, we present a complete methodology for the automatic vascular detection in retinal OCT images. To achieve this, we analyse the intensity profiles between representative layers of the retina, layers that are previously segmented. Then, we propose the use of two threshold-based strategies for vessel detection, a fixed and an adaptive approach. Both methods have been tested and validated with 128 OCT images, that include 560 vessels that were labelled by an ophthalmologist. The approaches provided satisfactory results, facilitating the doctors’ work and allowing better analysis and treatment of vascular diseases.

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7th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017 Coruña 19 June 2017 - 23 June 2017
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-319-59773-7_1

Keywords

Computer-aided Diagnosis Optical coherence tomography Retinal imaging Vessel detection

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https://doi.org/10.1007/978-3-319-59773-7_1

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© 2017 Springer International Publishing AG.

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Investigación (FIC)