Segmentation of exudates in fundus images applying color mathematical morphology

Juan I. Pastore; Agustina Bouchet; Cristian Ordoñez; Marcel Brun; Virginia Ballarin

doi:10.1117/12.2581133

3 November 2020 Segmentation of exudates in fundus images applying color mathematical morphology

Juan I. Pastore, Agustina Bouchet, Cristian Ordoñez, Marcel Brun, Virginia Ballarin

Proceedings Volume 11583, 16th International Symposium on Medical Information Processing and Analysis; 115830I (2020) https://doi.org/10.1117/12.2581133
Event: The 16th International Symposium on Medical Information Processing and Analysis, 2020, Lima, Peru

Abstract

Diabetic retinopathy is the most common cause of blindness in population in developed countries. However, with early diagnosis of this asymptomatic disease, it could be prevented 80 percent of cases. Diabetic retinopathy is detected in fundus images, also called retinal digital angiography. In most cases, due to the concave geometry of the eye, these images have a high variability in local contrast and luminance. This lack of uniformity may mask signs of this disease as ocular hemorrhages, microaneurysms, hard exudates and cotton wool spots affecting the diagnostic quality. This paper presents an automatic method for the segmentation of hard exudates and cotton wool in fundus images based on Mathematical Morphology in color spaces. The aim of this development is to assist the expert in the diagnosis of disease. To validate the proposed method, the Diabetic Retinopathy Database and the Evaluation Protocol diaRetdB0 and diaRetdB1 were used. The proposed method archived values above 96.5%. This performance was compared with a technique developed by other authors, obtaining a difference of above 26.5% of true positives in favor of our method.

Citation Download Citation

Juan I. Pastore, Agustina Bouchet, Cristian Ordoñez, Marcel Brun, and Virginia Ballarin "Segmentation of exudates in fundus images applying color mathematical morphology", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 115830I (3 November 2020); https://doi.org/10.1117/12.2581133

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