Detecting Cataract Using Smartphones

Objective: Cataract, which is the clouding of the crystalline lens, is the most prevalent eye disease accounting for 51% of all eye diseases in the U.S. Cataract is a progressive disease, and its early detection is critical for preventing blindness. In this paper, an efficient approach to...

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Autores principales:	Behnam Askarian, Peter Ho, Jo Woon Chong
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Cataract image processing luminance-based method smartphone Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/8ec36fea2b8947b0ab3bfeae46570259
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id	oai:doaj.org-article:8ec36fea2b8947b0ab3bfeae46570259
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spelling	oai:doaj.org-article:8ec36fea2b8947b0ab3bfeae465702592021-11-18T00:00:35ZDetecting Cataract Using Smartphones2168-237210.1109/JTEHM.2021.3074597https://doaj.org/article/8ec36fea2b8947b0ab3bfeae465702592021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9409132/https://doaj.org/toc/2168-2372Objective: Cataract, which is the clouding of the crystalline lens, is the most prevalent eye disease accounting for 51% of all eye diseases in the U.S. Cataract is a progressive disease, and its early detection is critical for preventing blindness. In this paper, an efficient approach to identify cataract disease by adopting luminance features using a smartphone is proposed. Methods: Initially, eye images captured by a smartphone were cropped to extract the lens, and the images were preprocessed to remove irrelevant background and noise by utilizing median filter and watershed transformation. Then, a novel luminance transformation from pixel brightness algorithm was introduced to extract lens image features. The luminance and texture features of different types of cataract disease images could be obtained accurately in this stage. Finally, by adopting support vector machines (SVM) as the classification method, cataract eyes were identified. Results: From all the images that we fed into our system, our method could diagnose diseased eyes with 96.6% accuracy, 93.4% specificity, and 93.75% sensitivity. Conclusion: The proposed method provides an affordable, rapid, easy-to-use, and versatile method for detecting cataracts by using smartphones without the use of bulky and expensive imaging devices. This methodcan be used for bedside telemedicine applications or in remote areas that have medical shortages. Previous smartphone-based cataract detection methods include texture feature analysis with 95 % accuracy, Gray Level Co-occurrence Matrix (GLCM) method with 89% accuracy, red reflex measurement method, and RGB color feature extraction method using cascade classifier with 90% accuracy. The accuracy of cataract detection in these studies is subject to changes in smartphone models and/or environmental conditions. However, our novel luminance-based method copes with different smartphone camera sensors and chroma variations, while operating independently from sensors’ color characteristics and changes in distances and camera angle. Clinical and Translational Impact—This study is an early/pre-clinical research proposing a novel luminance-based method of detecting cataract using smartphones for remote/at-home monitoring and telemedicine application.Behnam AskarianPeter HoJo Woon ChongIEEEarticleCataractimage processingluminance-based methodsmartphoneComputer applications to medicine. Medical informaticsR858-859.7Medical technologyR855-855.5ENIEEE Journal of Translational Engineering in Health and Medicine, Vol 9, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cataract image processing luminance-based method smartphone Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5
spellingShingle	Cataract image processing luminance-based method smartphone Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5 Behnam Askarian Peter Ho Jo Woon Chong Detecting Cataract Using Smartphones
description	Objective: Cataract, which is the clouding of the crystalline lens, is the most prevalent eye disease accounting for 51% of all eye diseases in the U.S. Cataract is a progressive disease, and its early detection is critical for preventing blindness. In this paper, an efficient approach to identify cataract disease by adopting luminance features using a smartphone is proposed. Methods: Initially, eye images captured by a smartphone were cropped to extract the lens, and the images were preprocessed to remove irrelevant background and noise by utilizing median filter and watershed transformation. Then, a novel luminance transformation from pixel brightness algorithm was introduced to extract lens image features. The luminance and texture features of different types of cataract disease images could be obtained accurately in this stage. Finally, by adopting support vector machines (SVM) as the classification method, cataract eyes were identified. Results: From all the images that we fed into our system, our method could diagnose diseased eyes with 96.6% accuracy, 93.4% specificity, and 93.75% sensitivity. Conclusion: The proposed method provides an affordable, rapid, easy-to-use, and versatile method for detecting cataracts by using smartphones without the use of bulky and expensive imaging devices. This methodcan be used for bedside telemedicine applications or in remote areas that have medical shortages. Previous smartphone-based cataract detection methods include texture feature analysis with 95 % accuracy, Gray Level Co-occurrence Matrix (GLCM) method with 89% accuracy, red reflex measurement method, and RGB color feature extraction method using cascade classifier with 90% accuracy. The accuracy of cataract detection in these studies is subject to changes in smartphone models and/or environmental conditions. However, our novel luminance-based method copes with different smartphone camera sensors and chroma variations, while operating independently from sensors’ color characteristics and changes in distances and camera angle. Clinical and Translational Impact—This study is an early/pre-clinical research proposing a novel luminance-based method of detecting cataract using smartphones for remote/at-home monitoring and telemedicine application.
format	article
author	Behnam Askarian Peter Ho Jo Woon Chong
author_facet	Behnam Askarian Peter Ho Jo Woon Chong
author_sort	Behnam Askarian
title	Detecting Cataract Using Smartphones
title_short	Detecting Cataract Using Smartphones
title_full	Detecting Cataract Using Smartphones
title_fullStr	Detecting Cataract Using Smartphones
title_full_unstemmed	Detecting Cataract Using Smartphones
title_sort	detecting cataract using smartphones
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/8ec36fea2b8947b0ab3bfeae46570259
work_keys_str_mv	AT behnamaskarian detectingcataractusingsmartphones AT peterho detectingcataractusingsmartphones AT jowoonchong detectingcataractusingsmartphones
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Detecting Cataract Using Smartphones

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