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 support vector machines (SVM) was established and studied.Firstly,color fundus images were segmented by IFCM,and candidate regions of EXs were obtained.Then,the SVM 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 126 images with variable color,brightness,and quality.70 of them were used to train the SVM and the remaining 56 to assess the performance of the method.Using a lesion based criterion,we achieved a mean sensitivity of 94.65％ and a mean positive predictive value of 97.25％.With an image-based criterion,our approach reached a 100％ mean sensitivity,96.43％ mean specificity and 98.21％ mean accuracy.Furthermore,the average time cost in processing an image is 4.56 s.The 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.
基金项目
Supported by the National High Technology Research and Development Program of China (863 Program)(2006AA020804)
Fundamental Research Funds for the Central Universities(NJ20120007)
Jiangsu Province Science and Technology Support Plan(BE2010652)
Program Sponsored for Scientific Innovation Research of College Graduate in Jangsu Province(CXLX11_0218)
Shanghai University Scientific Selection and Cultivation for Outstanding Young Teachers in Special Fund(ZZGCD15081)
NETL
NSTL
维普
万方数据