应用于红外眼科疾病检测的瞳孔定位算法

蔡怀宇; 史玉; 娄世良; 汪毅; 陈文光; 陈晓冬

doi:10.37188/CO.2020-0170

Volume 14 Issue 3

May 2021

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Chinese Optics > 2021 > 14(3): 605-614.

CAI Huai-yu, SHI Yu, LOU Shi-liang, WANG Yi, CHEN Wen-guang, CHEN Xiao-dong. Pupil location algorithm applied to infrared ophthalmic disease detection[J]. Chinese Optics, 2021, 14(3): 605-614. doi: 10.37188/CO.2020-0170

Citation:

CAI Huai-yu, SHI Yu, LOU Shi-liang, WANG Yi, CHEN Wen-guang, CHEN Xiao-dong. Pupil location algorithm applied to infrared ophthalmic disease detection[J]. Chinese Optics, 2021, 14(3): 605-614. doi: 10.37188/CO.2020-0170

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Pupil location algorithm applied to infrared ophthalmic disease detection

doi: 10.37188/CO.2020-0170

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)

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Corresponding author: yushi_821@tju.edu.cn
Received Date: 25 Sep 2020
Rev Recd Date: 09 Nov 2020

Available Online: 05 Feb 2021

Publish Date: 14 May 2021

Abstract

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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References(30)

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