IEEE J Biomed Health Inform - Retinal area detector from scanning laser ophthalmoscope (SLO) images for diagnosing retinal diseases.

Tópicos

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Resumo

Scanning laser ophthalmoscopes (SLOs) can be used for early detection of retinal diseases. With the advent of latest screening technology, the advantage of using SLO is its wide field of view, which can image a large part of the retina for better diagnosis of the retinal diseases. On the other hand, during the imaging process, artefacts such as eyelashes and eyelids are also imaged along with the retinal area. This brings a big challenge on how to exclude these artefacts. In this paper, we propose a novel approach to automatically extract out true retinal area from an SLO image based on image processing and machine learning approaches. To reduce the complexity of image processing tasks and provide a convenient primitive image pattern, we have grouped pixels into different regions based on the regional size and compactness, called superpixels. The framework then calculates image based features reflecting textural and structural information and classifies between retinal area and artefacts. The experimental evaluation results have shown good performance with an overall accuracy of 92%.

Resumo Limpo

scan laser ophthalmoscop slos can use earli detect retin diseas advent latest screen technolog advantag use slo wide field view can imag larg part retina better diagnosi retin diseas hand imag process artefact eyelash eyelid also imag along retin area bring big challeng exclud artefact paper propos novel approach automat extract true retin area slo imag base imag process machin learn approach reduc complex imag process task provid conveni primit imag pattern group pixel differ region base region size compact call superpixel framework calcul imag base featur reflect textur structur inform classifi retin area artefact experiment evalu result shown good perform overal accuraci

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