Evidence map›Paper›PMID 33856597›Full record

ArticleInternational ophthalmology2021

Automatic segmentation of corneal dystrophy on photographic images based on texture analysis.

Jong In You, Jang Ryul Park, Seul Ki Bang, Kiyoung Kim, Wang-Yuhl Oh, Seung-Young Yu, Kyung Hyun Jin

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Article in International ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jong In YouDepartment of Ophthalmology, Kyung Hee University Medical Center, Kyung Hee University Hospital, Kyung Hee University, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 130-872, Korea.
Jang Ryul ParkDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
Seul Ki BangDepartment of Ophthalmology, Kyung Hee University Medical Center, Kyung Hee University Hospital, Kyung Hee University, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 130-872, Korea.
Kiyoung KimDepartment of Ophthalmology, Kyung Hee University Medical Center, Kyung Hee University Hospital, Kyung Hee University, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 130-872, Korea.
Wang-Yuhl OhDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
Seung-Young YuDepartment of Ophthalmology, Kyung Hee University Medical Center, Kyung Hee University Hospital, Kyung Hee University, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 130-872, Korea.
Kyung Hyun JinDepartment of Ophthalmology, Kyung Hee University Medical Center, Kyung Hee University Hospital, Kyung Hee University, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 130-872, Korea. khjinmd@khu.ac.kr.ORCID http://orcid.org/0000-0002-2675-0163

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop an automatic algorithm to analyze dystrophic lesions on photographic images of corneal dystrophy.

methodsThe dataset included 32 images of corneal dystrophy. The dystrophic area was manually segmented twice. Manually labeled dystrophy areas were compared with automatically segmented images. First, we manually removed the light reflex from the image of the cornea. Using an automatic approach, we extracted the brown color of the iris. Then, the program detected the circular region of the pupil and the corneal surface. A whitish dystrophy area was defined based on the image intensity on the iris and the pupil. The sliding square kernel was applied to clearly define the dystrophic region.

resultsFor the manual analysis and the twice automatic approach, the Dice similarity was 0.804 and 0.801, respectively. The Pearson correlation coefficient was 0.807 and 0.806, respectively. The total number of distinct dystrophic areas showed no significant difference between the manual and automatic approaches according to the Wilcoxon signed-rank test (p < 0.0001, both).

conclusionsWe proposed an automatic algorithm for detecting the dystrophy areas on photographic images with an accuracy of approximately 0.80. This system can be applied to detect and predict the progression of corneal dystrophy.

Indexed as

Corneal Dystrophies, HereditaryAlgorithmsCorneaHumansIrisPupilAutomatic detectionAvellino corneal dystrophyCorneal dystrophyHomozygous granular corneal dystrophy type II

Identifiers

PMID33856597

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