Optical coherence tomography: automatic retina classification through support vector machines

• Autor(a) principal: Bernardes, Rui
• Data de Publicação: 2012
• Outros Autores: Serranho, Pedro, Santos, Torcato, Gonçalves, Valter, Cunha-Vaz, José
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.2/2765
• topic: Optical coherence tomography; Support vector machines; Supervised classification; Retina; Diabetic retinopathy; Ageing

Optical coherence tomography (OCT) is becoming one of the most important imaging modalities in ophthalmology due to its non-invasiveness and by allowing the visualisation the human retina structure in detail. It was recently proposed that OCT data embeds functional information from the human retina. Specifically, it was proposed that blood–retinal barrier status information is present within OCT data from the human retina. Besides this ability, the authors present data supporting the idea of having the OCT data encoding the ageing of the retina in addition to the disease (diabetes) condition from the healthy status. The methodology followed makes use of a supervised classification procedure, the support vector machine (SVM) classifier – based solely on the statistics of the distribution of OCT data from the human retina (i.e. OCT data between the inner limiting membrane and the retinal pigment epithelium). Results achieved suggest that information on both the healthy status of the blood–retinal barrier and on the ageing process co-exist encoded within the optical properties of the human retina.