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document
Automatic detection of red lesions in digital color fundus photographs . The robust detection of red lesions in digital color fundus photographs is a critical step in the development of automated screening systems for diabetic retinopathy . In this paper , a novel red lesion detection method is presented based on a hybrid approach , combining prior works by Spencer et al . ( 1996 ) and Frame et al . ( 1998 ) with two important new contributions . The first contribution is a new red lesion candidate detection system based on pixel classification . Using this technique , vasculature and red lesions are separated from the background of the image . After removal of the connected vasculature the remaining objects are considered possible red lesions . Second , an extensive number of new features are added to those proposed by Spencer-Frame . The detected candidate objects are classified using all features and a k-nearest neighbor classifier . An extensive evaluation was performed on a test set composed of images representative of those normally found in a screening set . When determining whether an image contains red lesions the system achieves a sensitivity of 100 % at a specificity of 87 % . The method is compared with several different automatic systems and is shown to outperform them all . Performance is close to that of a human expert examining the images for the presence of red lesions .
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Intervention_Other
detection of red lesions in digital color fundus photographs
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Intervention_Other
novel red lesion detection method
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Intervention_Other
red lesion candidate detection system
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Outcome_Other
red lesions
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Outcome_Other
vasculature and red lesions
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Outcome_Physical
red lesions
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Outcome_Other
sensitivity
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Outcome_Other
Performance
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Participant_Condition
diabetic