A Comparative Study on Automatic Detection Methods for Hard Exudates Based on Threshold
Segmentation and Pattern Classifier
GAO Weiwei1, ZUO Jing2
1. College of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China;
2. Department of Ophthalmology, Jiangsu Province Hospital of TCM, Nanjing Jiangsu 210029, China
Abstract：In order to establish an automatic screening system for diabetic retinopathy based on fundus image, a comparative study
of hard exudates detection methods based on threshold segmentation and pattern classifier was carried out. Firstly, an improved
Otsu segmentation algorithm based on minimum inner-cluster variance was used to roughly segment G channel of fundus images
in order to obtain candidate regions of hard exudate lesions. Then, feature sets of candidate regions were extracted and optimized.
Finally, RBF neural network classifier and SVM classifier were established respectively by using the optimized feature set and the
corresponding artificial judgment results, so as to realize the automatic recognition of hard exudates in fundus image. This method
and other hard exudates automatic detection methods were compared and analyzed. The results show that for the clinical application
of automatic screening for diabetic retinopathy, the automatic detection method of hard exudates based on threshold segmentation
and SVM classifier has better performance.
高玮玮1，左晶2. 基于阈值分割及模式分类器的硬性渗出自动检测方法对比研究[J]. 中国医疗设备, 2019, 34(11): 74-78.
GAO Weiwei1, ZUO Jing2. A Comparative Study on Automatic Detection Methods for Hard Exudates Based on Threshold
Segmentation and Pattern Classifier. China Medical Devices, 2019, 34(11): 74-78.
