首都医科大学学报 ›› 2015, Vol. 36 ›› Issue (6): 848-852.doi: 10.3969/j.issn.1006-7795.2015.06.003

• 糖尿病基础与临床研究 • 上一篇    下一篇

基于计算机视觉的糖尿病视网膜病变自动筛查系统

朱江兵1, 柯鑫1, 刘畅2, 杨金奎2, 何建国1   

  1. 1. 北京大恒图像视觉有限公司, 北京 100085;
    2. 首都医科大学附属北京同仁医院内分泌科, 北京 100730
  • 收稿日期:2015-10-21 出版日期:2015-12-21 发布日期:2015-12-18
  • 通讯作者: 何建国 E-mail:hejg@daheng-image.com
  • 基金资助:
    国家十二五重大专项基金(2013BAH19F02)。

Automated screening for diabetic retinopathy based on computer vision

Zhu Jiangbing1, Ke Xin1, Liu Chang2, Yang Jinkui2, He Jianguo1   

  1. 1. Beijing Daheng Image Vision Cor., Ltd, Beijing 100085, China;
    2. Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
  • Received:2015-10-21 Online:2015-12-21 Published:2015-12-18
  • Supported by:
    This study was supported by Important National Science & Technology Specific Projects(2013BAH19F02).

摘要: 目的 本研究旨在利用计算机视觉相关技术自动识别眼底影像中糖尿病视网膜病变(diabetic retinopathy,DR,以下简称糖网)的病变特征,开发能够用于DR筛查的计算机自动筛查系统。方法 利用数学形态学和支撑向量机(support vector machine,SVM)分类技术设计出检测DR包括出血、渗出、微血管瘤等各类病变的算法,再根据DR的临床诊断标准,对眼底影像进行自动分级诊断,实现自动筛查。结果 利用建立完成的糖网自动筛查系统对国际Messidor数据库进行了筛查判断,以经过专家认证的诊断结果作为判定标准。在总共1 200张眼底图中,系统的判定灵敏度(sensitivity)为93.8%,特异度(specificity)为94.5%,检测时间为9.83 s。结论 基于计算机视觉算法开发的糖网自动筛查系统能准确、高效的完成眼科影像的糖网筛查工作,能大幅减少阅片医生的工作量和人为的主观性,具有很好的临床应用前景和社会效益。

关键词: 糖尿病视网膜病变, 计算机视觉, 眼底图像, 自动筛查

Abstract: Objective To automatically identify relevant diabetic retinopathy(DR) characteristic lesions in the eyes using computer vision technology, develop computer automated screening system which can be used to screen for DR. Methods Using mathematical morphology and support vector machine(SVM) classification technology, we designed algorithm to screen for DR, including detecting hemorrhage, exudation, microaneurysm and other types of lesions, Then automatic grading and diagnosis of fundus imaging based on clinical diagnostic criteria for DR, and accomplish automatic DR screening. Results The established completely automated screening system was used for international Messidor databases as screening judgment, comparing with the validation criteria for diagnosis of experts, in a total of 1 200 fundus images, the system sensitivity was determined to be 93.8%, the specificity was 94.5%, detection time was 9.83 s. Conclusion These results show that the completion of the development of computer vision algorithms based DR automated screening system can accurately and efficiently screen ophthalmic images, with better prospects for clinical application and social benefits.

Key words: diabetic retinopathy, computer vision, fundus imaging, automatic screening

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