Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods

By Akara Sopharak, Bunyarit Uyyanonvara, Sarah Ann Barman, T. Williamson. Comput. Medical Imaging Graph., 2008

484 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images (2017, methodology): “[4] used a closing operator and reconstruction operators together with thresholding to remove the optic disk and main vessels, then discriminate the exudate”
Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets (2011, methodology): “, 2011a), we have evaluated this exudate segmentation technique on the HEI-MED dataset and compared it to two other segmentation techniques (Sopharak et al., 2008; Sanchez et al., 2009).”
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, background): “For instance, consider mathematical morphology based techniques presented in 2002 ( Walter et al., 2002 ), 2008 ( Sopharak et al., 2008 ) and 2014 ( Zhang et al., 2014 ).”