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Automatic Detection of Microaneurysms and Hemorrhages in Color Eye Fundus Images


Affiliations
1 LAPIA - Laboratorio de Processamento de Imagem Aplicado, Brazil
2 GPSEI – Grupo de Pesquisa em Sistemas Eletronicos, Brazil
 

This paper presents an approach for automatic detection of microaneurysms and hemorrhages in fundus images. These lesions are considered the earliest signs of diabetic retinopathy. The diabetic retinopathy is a disease caused by diabetes and is considered as the major cause of blindness in working age population. The proposed method is based on mathematical morphology and consists in removing components of retinal anatomy to reach the lesions. This method consists of five stages: a) pre-processing; b) enhancement of low intensity structures; c) detection of blood vessels; d) elimination of blood vessels; e) elimination of the fovea. The accuracy of the method was tested on a public database of fundus images, where it achieved satisfactory results, comparable to other methods from the literature, reporting 87.69% and 92.44% of mean sensitivity and specificity, respectively.

Keywords

Image Processing, Mathematical Morphology, Fundus Images, Microaneurysms, Hemorrhages.
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  • Automatic Detection of Microaneurysms and Hemorrhages in Color Eye Fundus Images

Abstract Views: 350  |  PDF Views: 198

Authors

Sergio Bortolin
LAPIA - Laboratorio de Processamento de Imagem Aplicado, Brazil
Daniel Welfer
GPSEI – Grupo de Pesquisa em Sistemas Eletronicos, Brazil

Abstract


This paper presents an approach for automatic detection of microaneurysms and hemorrhages in fundus images. These lesions are considered the earliest signs of diabetic retinopathy. The diabetic retinopathy is a disease caused by diabetes and is considered as the major cause of blindness in working age population. The proposed method is based on mathematical morphology and consists in removing components of retinal anatomy to reach the lesions. This method consists of five stages: a) pre-processing; b) enhancement of low intensity structures; c) detection of blood vessels; d) elimination of blood vessels; e) elimination of the fovea. The accuracy of the method was tested on a public database of fundus images, where it achieved satisfactory results, comparable to other methods from the literature, reporting 87.69% and 92.44% of mean sensitivity and specificity, respectively.

Keywords


Image Processing, Mathematical Morphology, Fundus Images, Microaneurysms, Hemorrhages.