TY - GEN
T1 - Screening for refractive error with low-quality smartphone images
AU - Yang, Zhongqi
AU - Fu, Eugene Yujun
AU - Ngai, Grace
AU - Leong, Hong Va
AU - Do, Chi Wai
AU - Chan, Lily
N1 - Publisher Copyright:
© 2020 ACM.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/11/30
Y1 - 2020/11/30
N2 - Uncorrected refractive errors can lead to permanent debilitating eye conditions if not corrected in a timely manner. Contemporary diagnostic methods rely on the professional acumen of optometrists and the use of expensive devices, which may not be easily accessible to all. According to the optical principle of photorefraction, refractive error can be estimated based on a relative pupil and crescent size of an eye image taken by a camera from a specified working distance. A low-cost approach would be to leverage smartphones with cameras for this purpose. However, the poor image quality generated from basic smartphones poses a challenge for the current approach as they often fail to accurately distinguish the crescent from the iris. We propose a novel method to detect and accurately measure the iris and crescent from smartphone photos. Based on this method, we further propose a set of features for machine learning to build our refractive error estimation model. The performance of our models are evaluated in an in-depth experiment.
AB - Uncorrected refractive errors can lead to permanent debilitating eye conditions if not corrected in a timely manner. Contemporary diagnostic methods rely on the professional acumen of optometrists and the use of expensive devices, which may not be easily accessible to all. According to the optical principle of photorefraction, refractive error can be estimated based on a relative pupil and crescent size of an eye image taken by a camera from a specified working distance. A low-cost approach would be to leverage smartphones with cameras for this purpose. However, the poor image quality generated from basic smartphones poses a challenge for the current approach as they often fail to accurately distinguish the crescent from the iris. We propose a novel method to detect and accurately measure the iris and crescent from smartphone photos. Based on this method, we further propose a set of features for machine learning to build our refractive error estimation model. The performance of our models are evaluated in an in-depth experiment.
KW - compute-aided diagnosis
KW - heathcare
KW - machine learning
KW - photorefraction
KW - vision screening
UR - http://www.scopus.com/inward/record.url?scp=85100475795&partnerID=8YFLogxK
U2 - 10.1145/3428690.3429175
DO - 10.1145/3428690.3429175
M3 - Conference article published in proceeding or book
AN - SCOPUS:85100475795
T3 - ACM International Conference Proceeding Series
SP - 119
EP - 128
BT - MoMM '20: Proceedings of the 18th International Conference on Advances in Mobile Computing & Multimedia
A2 - Haghighi, Pari Delir
A2 - Salvadori, Ivan Luiz
A2 - Steinbauer, Matthias
A2 - Khalil, Ismail
A2 - Kotsis, Gabriele
PB - Association for Computing Machinery
T2 - 18th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2020, in conjunction with the 22nd International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2020
Y2 - 30 November 2020 through 2 December 2020
ER -