Retinal area detection by using laser ophthalmoscope(LO)images to diagnose retinal diseases

Scanning laser ophthalmoscope (SLOs) can be used for early detection of retinal diseases. 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 imagining, 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 super pixels. 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%. But to get more accuracy the study was extended by using texture analysis (surface quality)to detect more feature extraction. By this study we hope in Future we can measure the properties of images(contrast, homogeneity, entropy, correlation).