Detection of retinal area from scanning laser ophthalmoscope images (SLO) using deep neural network Online publication date: Sat, 04-Mar-2017
by V. Ashok; G. Murugesan
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 23, No. 2/3/4, 2017
Abstract: Earlier detection and treatment of the retinal disease are crucial to avoid preventable vision loss. Scanning laser ophthalmoscopes (SLOs) can be used for early detection of retinal diseases and the SLO can image a large part of the retinal image to diagnose it in a better way. Eyelashes and eyelids are also imaged with the retinal image during the image process used to exclude the artefacts of the retinal image. The proposed SLO approach automatically extracts both the true retinal area and artefacts of the image based on image processing and machine learning approach. Superpixel and Deep Neural Network (DNN) are used to reduce the complexity of image processing tasks, the result is being provided with a primitive image pattern. The framework performs the calculations of textural and structural-based information of features and this approach results in effective analysis of retinal area and the artefacts.
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