Comparative Study On Segmentation Methods Of Fundus Images

被引：0
|
作者：
Cao, Juan [1 ]
Liu, JinJia [1 ]
机构：
[1] Chongqing Jiaotong Univ, Sch Informat, Chongqing 400074, Peoples R China
来源：
2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS | 2023年
基金：
中国国家自然科学基金;
关键词：
Image Segmentation Algorithm; Comparative Analysis; OPTIC DISC;
D O I：
10.1109/DDCLS58216.2023.10167377
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
As a traditional image segmentation method, image segmentation methods based on threshold, edge detection, and region have some differences in the effect of fundus image segmentation. In this paper, the watershed algorithm, Otsu, and Canny operator edge detection algorithms are selected for comparative study. By comparing the segmentation performance of two CHASEDB1 datasets and DRIVE datasets of fundus images, the experimental results showed that the Otsu had the highest segmentation accuracy and best segmentation effect on fundus images.
引用
收藏
页码：400 / 405
页数：6
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