Robust hemorrhage detection in diabetic retinopathy image

Zhang, Dongbo; Shang, Xinyu; Yao, Yi; Wang, Yaonan

doi:10.1109/acpr.2011.6166529

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…To extract non-hemorrhage features, they introduced non-vessel inhibition operator using Gabor filter to detect vessel feature and applied a multi-thresholds scheme based on standard hysteresis thresholding methods to help separate connective elongated vessels from scattered residual edge. The background estimation used Mahalanobis distance along with a threshold technique to separate foreground from background (Zhang D et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

Improvement of Automatic Hemorrhages Detection Methods Using Shapes Recognition

Abbadi¹

2013

Journal of Computer Science

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Diabetic Retinopathy is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. The presence of hemorrhages in the retina is the earliest symptom of diabetic retinopathy. The number and shape of hemorrhages is used to indicate the severity of the disease. Early automated hemorrhage detection can help reduce the incidence of blindness. This paper introduced new method depending on the hemorrhage shape to detect the dot hemorrhage (DH), its number, and size at early stage, this can be achieved by reducing the retinal image details. Detection and recognize the DH by following three sequential steps, removing the fovea, removing the vasculature and recognize DH by determining the circularity for all the objects in the image, finally determine the shape factor which is related to DH recognition, this stage strengthens the recognition process. The proposed method recognizes and separates all the DH.

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Section: Related Workmentioning

confidence: 99%