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Retinal Area Detector from Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases

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Retinal Area Detector from Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases

Early detection and treatment of retinal eye diseases is critical to avoid preventable vision loss. Digital retinal imaging is widely used to identify patients with retinal disease in primary care. With the advent of the latest screening technology, the advantage of using ultra wide field scanning-laser ophthalmoscope (SLO) is its wide field of view of the retina making it a valuable tool in the management of patients with retinal disease. One  consequence  of  the  wide field  imaging  process  is  that  artefacts  such  as eyelashes, eyelids and dust on optical surfaces are also imaged along with the retinal area.

To  the  best  of  our  knowledge,  there  is  no  existing  work  related  to  automated differentiation between the true retinal area and these artefacts in an SLO image. The purpose of performing this study is to develop a method that can exclude artefacts in SLO images so as to improve automatic detection of retina diseases. In this paper, we propose a novel approach to automatically extract true retinal area from an SLO image based on image processing and machine learning approaches.  To reduce the computational complexity of the image processing tasks and to provide a convenient primitive image pattern, we have grouped pixels into regions based on their regional size and compactness, called super pixels. Image based features representing textural and structural information are calculated and are used to classify the super pixels as retinal area and artefacts.  The  experimental  evaluation  results  have  shown  good performance  with  an  overall  accuracy  of  92% on both  healthy  and diseased  retinal scans  compared  to  clinical  annotations. The proposed approach enables effective analysis of retinal area and would have applications that include registering multi-view images into a montage and automated disease diagnosis.

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