Detection of Central Retinal Vein Occlusion using Guided Salient Features

Abstract

Central Retinal Vein Occlusion (CRVO) is one of the most common retinal vascular disorders in the world. Since it causes sudden permanent vision loss, it is crucial to detect and treat CRVO immediately to avoid further vision deterioration. But, manually diagnosing CRVO is a time-consuming task which requires constant attention of ophthalmologist. Although there are considerable number of research have been conducted in detecting Branch Retinal Occlusion, there are only few approaches have been proposed to identify CRVO automatically using fundus images. Multiple other approaches have been proposed to detect symptoms of CRVO such as hemorrhages, macular oedema separately. This paper has proposed guided salient feature-based approach to detect CRVO automatically. Overall system has achieved 89.04% accuracy with 72.5% precision and 70.73% recall. Furthermore, it has introduced novel approaches to detect retinal hemorrhages and optic disc. As it is a guided framework based on expert knowledge, this study eliminates the haziness presents in probabilistic feature selection approaches.