Cargando…
An automatic screening method for strabismus detection based on image processing
PURPOSE: This study aims to provide an automatic strabismus screening method for people who live in remote areas with poor medical accessibility. MATERIALS AND METHODS: The proposed method first utilizes a pretrained convolutional neural network-based face-detection model and a detector for 68 facia...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330949/ https://www.ncbi.nlm.nih.gov/pubmed/34343204 http://dx.doi.org/10.1371/journal.pone.0255643 |
_version_ | 1783732831295373312 |
---|---|
author | Huang, Xilang Lee, Sang Joon Kim, Chang Zoo Choi, Seon Han |
author_facet | Huang, Xilang Lee, Sang Joon Kim, Chang Zoo Choi, Seon Han |
author_sort | Huang, Xilang |
collection | PubMed |
description | PURPOSE: This study aims to provide an automatic strabismus screening method for people who live in remote areas with poor medical accessibility. MATERIALS AND METHODS: The proposed method first utilizes a pretrained convolutional neural network-based face-detection model and a detector for 68 facial landmarks to extract the eye region for a frontal facial image. Second, Otsu’s binarization and the HSV color model are applied to the image to eliminate the influence of eyelashes and canthi. Then, the method samples all of the pixel points on the limbus and applies the least square method to obtain the coordinate of the pupil center. Lastly, we calculated the distances from the pupil center to the medial and lateral canthus to measure the deviation of the positional similarity of two eyes for strabismus screening. RESULT: We used a total of 60 frontal facial images (30 strabismus images, 30 normal images) to validate the proposed method. The average value of the iris positional similarity of normal images was smaller than one of the strabismus images via the method (p-value<0.001). The sample mean and sample standard deviation of the positional similarity of the normal and strabismus images were 1.073 ± 0.014 and 0.039, as well as 1.924 ± 0.169 and 0.472, respectively. CONCLUSION: The experimental results of 60 images show that the proposed method is a promising automatic strabismus screening method for people living in remote areas with poor medical accessibility. |
format | Online Article Text |
id | pubmed-8330949 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-83309492021-08-04 An automatic screening method for strabismus detection based on image processing Huang, Xilang Lee, Sang Joon Kim, Chang Zoo Choi, Seon Han PLoS One Research Article PURPOSE: This study aims to provide an automatic strabismus screening method for people who live in remote areas with poor medical accessibility. MATERIALS AND METHODS: The proposed method first utilizes a pretrained convolutional neural network-based face-detection model and a detector for 68 facial landmarks to extract the eye region for a frontal facial image. Second, Otsu’s binarization and the HSV color model are applied to the image to eliminate the influence of eyelashes and canthi. Then, the method samples all of the pixel points on the limbus and applies the least square method to obtain the coordinate of the pupil center. Lastly, we calculated the distances from the pupil center to the medial and lateral canthus to measure the deviation of the positional similarity of two eyes for strabismus screening. RESULT: We used a total of 60 frontal facial images (30 strabismus images, 30 normal images) to validate the proposed method. The average value of the iris positional similarity of normal images was smaller than one of the strabismus images via the method (p-value<0.001). The sample mean and sample standard deviation of the positional similarity of the normal and strabismus images were 1.073 ± 0.014 and 0.039, as well as 1.924 ± 0.169 and 0.472, respectively. CONCLUSION: The experimental results of 60 images show that the proposed method is a promising automatic strabismus screening method for people living in remote areas with poor medical accessibility. Public Library of Science 2021-08-03 /pmc/articles/PMC8330949/ /pubmed/34343204 http://dx.doi.org/10.1371/journal.pone.0255643 Text en © 2021 Huang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Huang, Xilang Lee, Sang Joon Kim, Chang Zoo Choi, Seon Han An automatic screening method for strabismus detection based on image processing |
title | An automatic screening method for strabismus detection based on image processing |
title_full | An automatic screening method for strabismus detection based on image processing |
title_fullStr | An automatic screening method for strabismus detection based on image processing |
title_full_unstemmed | An automatic screening method for strabismus detection based on image processing |
title_short | An automatic screening method for strabismus detection based on image processing |
title_sort | automatic screening method for strabismus detection based on image processing |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330949/ https://www.ncbi.nlm.nih.gov/pubmed/34343204 http://dx.doi.org/10.1371/journal.pone.0255643 |
work_keys_str_mv | AT huangxilang anautomaticscreeningmethodforstrabismusdetectionbasedonimageprocessing AT leesangjoon anautomaticscreeningmethodforstrabismusdetectionbasedonimageprocessing AT kimchangzoo anautomaticscreeningmethodforstrabismusdetectionbasedonimageprocessing AT choiseonhan anautomaticscreeningmethodforstrabismusdetectionbasedonimageprocessing AT huangxilang automaticscreeningmethodforstrabismusdetectionbasedonimageprocessing AT leesangjoon automaticscreeningmethodforstrabismusdetectionbasedonimageprocessing AT kimchangzoo automaticscreeningmethodforstrabismusdetectionbasedonimageprocessing AT choiseonhan automaticscreeningmethodforstrabismusdetectionbasedonimageprocessing |