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http://hdl.handle.net/2183/37071 Automatic Detection of Epiretinal Membrane in OCT Images by Means of Local Luminosity Patterns
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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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[Absctract]: 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.
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The conference was held in Cadiz, Spain, June 14-16, 2017.
©2017 This version of the article has been accepted for publication, after peer review 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/10.1007/978-3-319-59153-7_20
©2017 This version of the article has been accepted for publication, after peer review 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/10.1007/978-3-319-59153-7_20