Segmentation processes and pattern recognition in retina and brain imaging

• Autor(a) principal: Correia, Pedro Guimarães Sá
• Data de Publicação: 2011
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/15531
• topic: Olho; Olho - doenças de visão; Visão - meios de diagnóstico; Meios complementares de diagnóstico; Tomografia óptica; Tomografia - Coerência óptica

We purposed to establish an automatic correlation between retinal and cortical thickness. In 2009 Christine C. Boucard et al. [6] showed that acquired long-standing retinal pathologies, such as age-related macular degeneration and glaucoma, appear to be related to grey matter density reduction roughly in the defect projections at the visual cortex. As mentioned in *6+, “this indicates that long-term cortical deprivation, due to retinal lesions acquired later in life, is associated with retinotopic-specific neuronal degeneration of visual cortex”. Also on that year, Gupta et al. [14] confirmed the in vivo atrophy of LGN in glaucoma patients, “(…) consistent with ex vivo primate and human neuropathological studies (…)”. Optical coherence tomography (OCT) allows imaging the ocular fundus in vivo and is herewith applied as the technique of choice for measuring the retinal thickness. We resort to the high-definition spectral domain system from Zeiss (Cirrus HD-OCT, Carl Zeiss Meditec, Dublin, CA, USA) to compute maps of retinal thickness covering the central 20 degrees of the human macula. Similarly, we resort to the Siemens Trio 3T (Siemens AG, Healthcare Sector, Erlangen, Germany) to gather MRI (Magnetic Resonance Imaging) from the human cortex and the Brain Voyager QX® (Brain Innovation B.V., Maastricht, The Netherlands) software to derive cortical thickness from the human visual cortex. While with the former technique a fundus reference is made available, therefore the thickness map of the macula can be easily located within the ocular fundus. The latter does not provide per se a reference, thus requiring proper visual stimuli in order to establish to which location within the ocular fundus a given visual cortex location refers to. Each visual area contains a representation of the visual space and therefore a representation of each retinal point. In order to achieve the intended correlation we need to map the retina in each visual area, thus visual area segmentation is mandatory. An algorithm was developed and validated against manual segmentation, using a total of 6 data sets from healthy subjects. In order to correlate the retina to the brain it is mandatory to find a continuous transformation function that links each retinal point to the cortex and vice-versa. Interpolation method used is based on Delaunay triangulation