Automatic localization and segmentation of optic disk in color fundus image

ZOU Bei-ji; ZHANG Si-jian; ZHU Cheng-zhang

doi:10.3788/OPE.20152304.1187

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Automatic localization and segmentation of optic disk in color fundus image

更新时间：2020-08-13

- Automatic localization and segmentation of optic disk in color fundus image
- Optics and Precision Engineering Vol. 23, Issue 4, Pages: 1187-1195(2015)
- 作者机构：
  
  1. 中南大学信息科学与工程学院,湖南长沙,410083
  2. "移动医疗"教育部-中国移动联合实验室,湖南长沙,410083
  3. 湖南理工学院,湖南岳阳,414000
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152304.1187
  CLC： R770.4;TP391.7
- Received：25 November 2014，
  
  Revised：23 January 2015，
  
  Published：25 April 2015
- 稿件说明：
移动端阅览
邹北骥, 张思剑, 朱承璋. 彩色眼底图像视盘自动定位与分割[J]. 光学精密工程, 2015,23(4): 1187-1195

ZOU Bei-ji, ZHANG Si-jian, ZHU Cheng-zhang. Automatic localization and segmentation of optic disk in color fundus image[J]. Editorial Office of Optics and Precision Engineering, 2015,23(4): 1187-1195
邹北骥, 张思剑, 朱承璋. 彩色眼底图像视盘自动定位与分割[J]. 光学精密工程, 2015,23(4): 1187-1195 DOI： 10.3788/OPE.20152304.1187.

ZOU Bei-ji, ZHANG Si-jian, ZHU Cheng-zhang. Automatic localization and segmentation of optic disk in color fundus image[J]. Editorial Office of Optics and Precision Engineering, 2015,23(4): 1187-1195 DOI： 10.3788/OPE.20152304.1187.

摘要

针对彩色眼底图像视盘定位时图像边缘高亮环对定位准确率的影响

提出了一种有效的图像预处理方法。针对已有的视盘分割算法中存在的问题

提出了一种结合形态学、椭圆拟合及梯度矢量流(GVF) Snake模型的分割算法。提出的预处理方法首先利用最小二乘法拟合出眼底图像的边界

然后裁剪掉边界的一部分高亮像素点

最后进行视盘定位。视盘分割算法则首先进行血管擦除

然后用椭圆拟合提取初始轮廓

最后使用GVF Snake精确调整视盘边界。用提出的方法对Messidor眼底图像数据库1 200幅图像上进行了实验

结果显示:视盘定位准确率由原来没经过预处理的95.4%提升到了98.7%;视盘分割错误率与当前已知最好的算法相比由12.5%降低到了9.39%。结果表明:提出的眼底图像视盘自动定位与分割方法准确率高、实用性强

可以用于眼科疾病的计算机辅助诊断。

Abstract

An effective pre-processing method is proposed to overcome the influence of a bright ring caused by the edge of a color fundus image on optic disk localization. Then

a novel method integrating the morphology

ellipse fitting and a Gradient Vector Flow (GVF) Snake model is proposed to implement the segmentation of the optic disk. The proposed pre-processing method uses least square method to fit the edge of color fundus image

and then clips some bright pixels near the edge. Finally

it localizes the optic disk. Furthermore

the proposed segmentation algorithm segments the optic disk by 3 steps: vascular erase

ellipse fitting and a fine tune step using GVF Snake model. A test is performed with 1 200 color fundus images from Messidor color fundus image database. The test results indicate that the localization accuracy for the optic disk rises from 95.4% to 98.7% as comparing with the traditional method. Moreover

the optic disk segmentation error has dropped from 12.5% to 9.39% as comparing with the current known best algorithm. It concludes that the proposed method of automatic localization and segmentation of optic disk in color fundus images have strong practicability and high accuracy and are suitable for the computer-aided diagnosis of ocular diseases.

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Keywords

references

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