<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Retinal images are nowadays widely used to diagnose many diseases, for example diabetic retinopathy. In our work, we propose the algorithm for the screening application, which identifies the patients with such severe diabetic complication as diabetic retinopathy is, in early phase. In the application we use the patient’s fundus photography without any additional examination by an ophtalmologist. After this screening identification, other examination methods should be considered and the patient’s follow-up by a doctor is necessary. Our application is composed of three principal modules including fundus image preprocessing, feature extraction and feature classification. Image preprocessing module has the role of luminance normalization, contrast enhancement and optical disk masking. Feature extraction module includes two stages: bright lesions candidates localization and candidates feature extraction. We selected 16 statistical and structural features. For feature classification, we use multilayer perceptron (MLP) with one hidden layer. We classify images into two classes. Feature classification efficiency is about 93 percent.</p></abstract>

Retinal images are nowadays widely used to diagnose many diseases, for example diabetic retinopathy. In our work, we propose the algorithm for the screening application, which identifies the patients with such severe diabetic complication as diabetic retinopathy is, in early phase. In the application we use the patient’s fundus photography without any additional examination by an ophtalmologist. After this screening identification, other examination methods should be considered and the patient’s follow-up by a doctor is necessary. Our application is composed of three principal modules including fundus image preprocessing, feature extraction and feature classification. Image preprocessing module has the role of luminance normalization, contrast enhancement and optical disk masking. Feature extraction module includes two stages: bright lesions candidates localization and candidates feature extraction. We selected 16 statistical and structural features. For feature classification, we use multilayer perceptron (MLP) with one hidden layer. We classify images into two classes. Feature classification efficiency is about 93 percent.

Diabetic Rethinopathy Screening by Bright Lesions Extraction from Fundus Images

Slovak University of Technology in Bratislava, Faculty of Electrical Engineering and Information Technology, Ilkovičova 3, 812 19 Bratislava, Slovakia

Journal of Electrical Engineering

{"article-title":"Diabetic Rethinopathy Screening by Bright Lesions Extraction from Fundus Images"}

Retinal images are nowadays widely used to diagnose many diseases, for example diabetic retinopathy. In our work, we propose the algorithm for the screening...

Diabetic Rethinopathy Screening by Bright Lesions Extraction from Fundus Images

Publié en ligne: 15 oct. 2013

Pages: 311 - 316

DOI: https://doi.org/10.2478/jee-2013-0045

Mots clés
diabetic retinopathy, fundus images, automatic diagnostic and screening, machine learning, neural networks

This content is open access.

Diabetic Rethinopathy Screening by Bright Lesions Extraction from Fundus Images

Publié en ligne: 15 oct. 2013

Pages: 311 - 316

DOI: https://doi.org/10.2478/jee-2013-0045

Mots clésdiabetic retinopathy, fundus images, automatic diagnostic and screening, machine learning, neural networks

This content is open access.

Mots clés
diabetic retinopathy, fundus images, automatic diagnostic and screening, machine learning, neural networks