• Journal of Multimedia Information System
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• 제5권2호
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• pp.79-82
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• 2018

Due to the handheld holding of smartphones and the presence of light leakage and non-balanced contrast, the detection of the retina area in smartphone-captured fundus images is more challenging than retinography-captured fundus images. This paper presents a computationally efficient image processing pipeline in order to detect and enhance the retina area in smartphone-captured fundus images. The developed pipeline consists of five image processing components, namely point spread function parameter estimation, deconvolution, contrast balancing, circular Hough transform, and retina area extraction. The results obtained indicate a typical fundus image captured by a smartphone through a D-EYE lens is processed in 1 second.

• International Journal of Advanced Culture Technology
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• 제7권2호
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• pp.227-237
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• 2019

In this paper, we have developed a Retina Healthcare Service System through which the patient himself/herself can manage his/her retina health. In the case of conventional portable ophthalmic cameras, patients cannot check their eye health on their own because most of them are used by doctor in environments where ophthalmography cannot be performed properly. This system consists of web, app and camera modules, and when a patient mounts a camera module for fundus photography on his / her smart phone and then photographs his / her fundus through the app, the image is transmitted to a server, and the transmitted image reads the fundus the patient's fundus image status in the fundus image reading model learned using deep learning. When the doctor expresses his/her opinions about the patient 's eye condition based on the reading result and the fundus photograph, the patient can check through the app and judge whether to receive ophthalmologic treatment.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.636-638
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• 2022

본 논문은 의료진단 검출 분야에서 혈관, 신경조직, 망막 손상 그리고 다양한 심혈관계 질환과 치매까지 진단하는 데 유용하게 사용하고 있는 안저 영상에 CNN(Convolution Neural Network) 알고리즘을 적용하고 녹내장 병변을 검출하기 위한 연구를 진행한다. 실험을 위하여 정상 안저 영상과 녹내장 병변이 있는 안저 영상으로 구성된 데이터 세트를 AlexNet으로 분류하고 그 성능을 확인하였다.

• Journal of Korean Medical Science
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• 제33권43호
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• pp.239.1-239.12
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• 2018

Background: We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system. Methods: A 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated. Results: The 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%-65.2%, and complete agreement rate of all-three raters was 5.7%-43.3%. As for diagnoses, agreement of at-least two raters was 35.6%-65.6%, and complete agreement rate was 11.0%-40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties. Conclusion: This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

• 한국광학회지
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• 제17권2호
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• pp.113-119
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• 2006

망막검사를 위한 검안경용 망막 카메라를 개발하기 위한 광학계 설계를 연구하였다. 이에 앞서 사람의 눈에 대한 광학계 제원은 김상기와 박성찬이 발표한 정밀모형안을 채택하였다. 이를 토대로 하여 광학계를 4개의 렌즈 군으로 구성하였고 제 1군으로 모든 시야각에 대한 광을 중심으로 모았다가 제2, 제3, 제4군에 의해 결상하도록 하였다. 결상된 망막의 상은 CCD 광검출기에 의해 모니터로 전시한다. 제 1군을 단일 렌즈로 하고 나머지 광학계를 triplet로 하여 최적화를 통해 설계하였다. 광선수차, spot diagram, 회절을 고려한 point spread function 및 MTF를 계산하여 분석한 결과, 좋은 성능을 가진 광학계 설계가 가능하였다. 본 연구 결과에서 얻은 광학계로서 양질의 망막 카메라를 개발할 순 있을 것으로 믿는다.