Abstract
We developed an automatic system to identify and differentiate color fundus images containing no lesions, drusen or exudates. Drusen and exudates are lesions with a bright appearance, associated with age-related macular degeneration and diabetic retinopathy, respectively. The system consists of three lesion detectors operating at pixel-level, combining their outputs using spatial pooling and classification with a random forest classifier. System performance was compared with ratings of two independent human observers using human-expert annotations as reference. Kappa agreements of 0.89, 0.97 and 0.92 and accuracies of 0.93, 0.98 and 0.95 were obtained for the system and observers, respectively.
Highlights
Diabetic retinopathy (DR) is the leading cause of blindness worldwide in the working population with an estimated number of affected patients of 93 million in 2010 [1,2,3]
The estimated number of patients affected by Age-related macular degeneration (AMD) was 170 million in 2014 [4]
In our previous work we have shown that including contextual information is beneficial for individual lesion detection [22]
Summary
Diabetic retinopathy (DR) is the leading cause of blindness worldwide in the working population with an estimated number of affected patients of 93 million in 2010 [1,2,3]. Age-related macular degeneration (AMD) is another sight threatening disease and the most common cause of blindness in the elderly. The estimated number of patients affected by AMD was 170 million in 2014 [4] Both diseases progress without any visual complaints in early stages, while leading to visual impairment and, vision loss in advanced stages. With the rising prevalence of diabetes and the aging population, the incidence of DR and AMD is expected to increase rapidly in the near future [5, 6]
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