A novel method for detection of hard exudates from fundus images based on RBF and improved FCM
Conference: BIBE 2019 - The Third International Conference on Biological Information and Biomedical Engineering
06/20/2019 - 06/22/2019 at Hangzhou, China
Proceedings: BIBE 2019
Pages: 6Language: englishTyp: PDF
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Authors:
Gao, Weiwei (College of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai, China)
Shen, Jianxin (College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Zuo, Jing (Department of Ophthalmology, Jiangsu Province Hospital of TCM, Nanjing, China)
Abstract:
Diabetic retinopathy (DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates (EXs), in retinal images can contribute to the diagnosis and screening of the disease. To achieve this goal, an automatically detecting approach based on improved FCM (IFCM) as well as RBF was established and studied. Firstly, color fundus images were segmented by IFCM, and candidate regions of EXs were obtained. Then, the RBF classifier is confirmed with the optimal subset of features and judgments of these candidate regions, as a result hard exudates are detected from fundus images. Our database was composed of 120 images with variable color, brightness, and quality. 55 of them were used to train the RBF and the remaining 65 to assess the performance of the method. Using a lesion based criterion, we achieved a mean sensitivity of 95.05% and a mean positive predictive value of 95.37%. With an image-based criterion, our approach reached a 100% mean sensitivity, 90.9% mean specificity and 96.0% mean accuracy. Furthermore, the average time cost in processing an image is 3.7 seconds. These results suggest that the proposed method can efficiently detect EXs from color fundus images and it could be a diagnostic aid for ophthalmologists in the screening for DR.