应用于红外眼科疾病检测的瞳孔定位算法 -

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应用于红外眼科疾病检测的瞳孔定位算法

doi:10.37188/CO.2020-0170

1.
天津大学精密仪器与光电子工程学院光电信息技术教育部重点实验室，天津 300072
2.
上海美沃精密仪器股份有限公司，上海 200237

基金项目:国家重点研发计划（No. 2017YFC0109901）；天津市自然科学基金项目（No. 15JCQNJC14200）

详细信息

作者简介:
蔡怀宇（1965—），女，湖南涟源人，博士，教授，硕士生导师，1991年、2000年于天津大学分别获得硕士、博士学位，主要从事信息光学、光电技术及仪器和图像处理等方面的研究。E-mai：hycai@tju.edu.cn
史　玉 (1997—)，女，山西吕梁人，天津大学精密仪器与光电工程技术学院硕士研究生，2019年于湖北大学获得学士学位，主要从事光学相干层析成像方面的研究。E-mail：yushi_821@tju.edu.cn

中图分类号:TP391；TN247
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- 被引次数:0
出版历程
- 收稿日期:2020-09-25
- 修回日期:2020-11-09
- 网络出版日期:2021-02-05
- 刊出日期:2021-05-14

Pupil location algorithm applied to infrared ophthalmic disease detection

1.
Key Laboratory of Opto-Electronics Information Technology of Ministry of Education, College of Precision Instrument and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
2.
Shanghai MediWorks Precision Instrument Incorporated Company, Shanghai 200237, China

Funds:Supported by National Key R&D Program of China (No. 2017YFC0109901); Natural Science Foundation Project of Tianjin (No. 15JCQNJC14200)

More Information

Corresponding author:yushi_821@tju.edu.cn

摘要

摘要:在眼科疾病检测中，为了对被检测者进行快速、准确、自动化的瞳孔定位，提出一种改进径向对称变换的瞳孔中心点定位算法。首先利用灰度积分投影法结合最大类间方差法，完成对人眼图像的粗分割，并根据多团块筛选条件提取出只包含瞳孔的感兴趣区域(Region Of Interest，ROI)。然后对ROI采用最小外接矩形结合灰度级形态学线性滤波方法，完成搜索半径范围的设置。最后，利用改进的径向对称变换算法进行瞳孔中心点定位。实验结果表明：本文算法的定位误差在8 pixel以内，平均定位时间为0.366 s，能够适应人眼图像中噪声干扰、采集不完整等大量非理性状态，满足多种红外眼科疾病检测设备对瞳孔定位算法的要求。
- 眼科检查/
- 瞳孔定位/
- 图像处理/
- 径向对称变换
Abstract:In order to quickly, accurately and automatically locate a pupil in ophthalmic disease detection, a location algorithm for a pupil’s center based on radial symmetry transformation was proposed. Firstly, the gray integral projection method combined with the maximum class square error method was used to complete the rough segmentation of human eye image, and a Region Of Interest (ROI) solely containing the pupil was extracted according to multi-lump screening conditions. Then the search radius range was set by using a minimum circumscribed rectangle on the ROI combined with gray-level morphological linear filtering. Finally, the pupil center was located using an improved radial symmetry transformation algorithm. The experimental results show that the location error of this algorithm is less than 8 pixels and the average processing time is 0.366 s. It can adapt to a large number of irrational states such as noise interference and an incomplete collection of human eye images and meets the pupil location performance requirements for many kinds of infrared ophthalmology disease detection equipment.
- ophthalmic examination/
- pupil localization/
- image processing/
- radial symmetry transformation

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图 1算法总流程图

Figure 1.Flow chart of the improved pupil location algorithm

下载: 全尺寸图片幻灯片

图 2人眼图像ROI分割示意图。（a）竖直投影曲线对照图；(b)水平投影曲线对照图；(c)粗分割图像；(d)二值化图像

Figure 2.Schematic diagram of ROI segmentation of a human eye image. (a) Vertical projection curve contrast diagram; (b) horizontal projection curve contrast diagram; (c) coarse segmentation image; (d) binary image

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图 3受睫毛遮挡的ROI提取示意图。(a)样本1图像；(b)样本1二值化图像；(c)样本1的ROI图像；(d)样本2图像；(e)样本2的二值化图像；(f)样本2的ROI图像,图像源于数据库CASIA-IrisV4

Figure 3.Schematic diagram of ROI extraction when pupil was obscured by eyelashes. (a) Image of sample 1; (b) binarization image of sample 1; (c) ROI image of the sample 1; (d) image of sample 2; (e) binarization image of sample 2; (f) ROI image of sample 2, images are derived from the CASIA-IrisV4 database

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图 4结合灰度级形态学滤波的ROI提取示意图。（a）线性结构元素；（b）样本1的ROI图像；（c）样本2的ROI图像

Figure 4.Schematic diagram of ROI extraction combined with grayscale morphological filtering. (a) Linear structural element; (b) ROI image of sample 1; (c) ROI image of sample 2

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图 5ROI的最小外接矩形示意图

Figure 5.Schematic diagrams of minimum circumscribed rectangles of the ROI

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图 6(a)~(d)为瞳孔定位结果图；(e)~(h)为人眼图像定位结果

Figure 6.(a)~(d) Pupil positioning; (e)~(h) localization of human eye

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图 7不完整瞳孔区域图像的定位结果图。(a)~(d)人眼图像； (e)~(h)定位结果

Figure 7.Incomplete pupil area image positioning. (a)~(d) Images of human eye; (e)~(h) localization of human eye

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图 83种定位算法结果对比

Figure 8.Comparison of localization results by three different algorithms

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图 93种算法在CASIA-IrisV4数据库的定位结果

Figure 9.Comparison of localization results by three different algorithms in CASIA-IrisV4 database

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表 13种算法的精确度和实时性比较

Table 1.Comparison of accuracy and real-time performance of three algorithms

定位方法	定位误差/pixel	定位时间/s
本文算法	6.318	0.366
传统径向对称变换算法	103.681	4.610
基于梯度均值的定位算法	7.242	10.923

下载: 导出CSV

表 23种算法适用性比较

Table 2.Comparison of applicability of three algorithms

定位方法	定位准确率/%	定位时间/s
本文方法	98	0.053
传统径向对称变换算法	81	1.093
基于梯度均值的定位算法	93	8.583

下载: 导出CSV

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