Abstract
This work presents a novel approach for automatic detection of the epiretinal membrane in Optical Coherence Tomography (OCT) images. A tool able to detect this pathology is very valued since it can prevent further ocular damage by doing an early detection. This approach is based in the location of the inner limiting membrane (ILM) layers of the retina. Then, the detected locations are classified using a local-feature based vector in order to determine presence of the membrane. Different tests are run and compared to establish the appropriateness of the approach as well as its practical validity.
This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union throug the PI14/02161 and the DTS15/00153 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project.
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Baamonde, S., de Moura, J., Novo, J., Ortega, M. (2017). Automatic Detection of Epiretinal Membrane in OCT Images by Means of Local Luminosity Patterns. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_20
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DOI: https://doi.org/10.1007/978-3-319-59153-7_20
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