• Current Optics and Photonics
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• v.3 no.6
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• pp.597-604
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• 2019

This study was performed to evaluate objective visual quality and ocular scattering in myopic refractive-surgery patients, compared to emmetropes. Optical vision-quality parameters (modulation transfer function (MTF) cutoff and Strehl ratio) and objective scattering index (OSI) were measured using an optical quality analysis system (OQAS II) based on the double-pass technique. In all subjects, the higher the MTF cutoff and Strehl ratio, the lower the OSI and ocular higher-order aberrations (HOAs). The MTF cutoff and Strehl ratio for the laser-assisted subepithelial keratectomy (LASEK) group were lower than those for the emmetropia group, while the OSI, ocular HOAs, and spherical aberration (SA) for the LASEK group were higher than those for emmetropia group. Ocular scattering would be one of the important factors in regard to visual quality. Therefore, the quality of the retinal image in the LASEK patients has been shown to reduce the quality of vision more than in the emmetropes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2143-2149
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• 2007

Visual Cortex Stimulator is among artificial retina prosthesis for blind man, is the method that stimulate the brain cell directly without processing the information from retina to visual cortex. In this paper, we propose image construction and recognition model that is similar to human visual processing by recognizing the feature data with orientation information, that is, the characteristics of visual cortex. Back propagation algorithm based on Delta-bar delta is used to recognize after extracting image feature by Kirsh edge detector. Various numerical patterns are used to analyze the performance of proposed method. In experiment, the proposed recognition model to extract image characteristics with the orientation of information from retinal cells to visual cortex makes a little difference in a recognition rate but shows that it is not sensitive in a variety of learning rates similar to human vision system.

• Journal of Biomedical Engineering Research
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• v.8 no.1
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• pp.81-86
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• 1987

An image feature extraction method for the low contrast fluoresceln angiogram in dlabetes was studied. To obtain effective image segmentation, an adaptive local difference image is generated and relaxation process are applied to this difference Image. By the use of distance transformed data with segmented image, shape and location of feature regions were obtained. It was shown that the location and shape descriptions of Impaired blood vessel networks and retinal regions are can he utilized for the diagnosis of diabetes and other disease.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.315-319
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• 2008

This paper describes a method for recognizing and measuring the motion of each individual leukocyte in microvessel from a sequence of images. A spatiotemporal image is generated whose spatial axes are parallel and vertical to vessel region contours. In order to enhance and extract only leukocyte traces with a turned velocity range even under noisy background, we use a combination of a filtering process using Gabor filters with sharp orientation selectivity and a subsequent 3D spatiotemporal grouping process. The proposed method is shown to be effective by experiments using image sequences of two kinds of microcirculation, rat mesentery microvessels and human retinal capillaries.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.124-126
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• 2021

In this study, fundus images were analyzed using an image processing algorithm for diagnosis of diabetic retinopathy, and specific areas such as hard exudate and retinal hemorrhage, which are characteristic of diabetic retinopathy disease using the bit plane technique, were extracted. We propose a system capable of automatic diagnosis by quantifying the characteristics of diabetic retinopathy based on the analyzed fundus image.

• Current Optics and Photonics
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• v.3 no.1
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• pp.72-77
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• 2019

This study was performed to analyze the optical aberrations of the cornea induced by myopic refractive surgery. Corneal total higher-order aberrations, spherical aberration and coma for 4-mm and 6-mm pupils were measured using a wave-front analyzer. The amount of aberrations of the oblate corneal optics by the achieved correction was found to be larger than for the prolate corneal shape with complete eye, in an emmetropia control group. The change in corneal shape acts as an optical factor that degrades the quality of the retinal image; it seems to be one of the important factors related to quality of vision.

• Journal of Biomedical Engineering Research
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• v.34 no.3
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• pp.135-140
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• 2013

We have developed an ultra wide-field of view Optical Coherence Tomography(OCT) which has capability to 2D and 3D views of cross-sectional structure of in vivo human retina. Conventional OCT has a limitation in visualizing the entire retina due to a reduced field of view. We designed an optical setup to significantly improve the lateral scanning range to be more than 20 mm. The entire human retinal structure in 2D and 3D was reported in this paper with the developed OCT system. Also, we empirically searched an optimized image size for real time visualization by analyzing variation of the frame rate with different lateral scan points. The size was concluded to be $1024{\times}2000{\times}300$ pixels which took 9 seconds for visualization.

• Journal of information and communication convergence engineering
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• v.2 no.2
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• pp.132-137
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• 2004

Most of the current computer vision theories are based on hypotheses that are difficult to apply to the real world, and they simply imitate a coarse form of the human visual system. As a result, they have not been showing satisfying results. In the human visual system, there is a mechanism that processes information due to memory degradation with time and limited storage space. Starting from research on the human visual system, this study analyzes a mechanism that processes input information when information is transferred from the retina to ganglion cells. In this study, a model for the characteristics of ganglion cells in the retina is proposed after considering the structure of the retina and the efficiency of storage space. The MNIST database of handwritten letters is used as data for this research, and ART2 and SOM as recognizers. The results of this study show that the proposed recognition model is not much different from the general recognition model in terms of recognition rate, but the efficiency of storage space can be improved by constructing a mechanism that processes input information.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.77-82
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• 2019

Diabetic retinopathy is a complicated form of diabetes due to circulatory disorder in the peripheral blood vessels of the retina. We segment the microvessel for diagnosing diabetic retinophathy. The conventional methods using filter and features can segment the thick blood vessels, but it has relatively weak for segmenting fine blood vessels. In pre-processing step, noise reduction filter and histogram equalization are applied to suppress the noise and enhance the image contrast. Then, deep learning technique is used for pixel-by-pixel segmentation. The accuracy of conventional methods is between 90% to 94%, while the proposed method has improved as 95% accuracy. There is a problem of segmentation error around the optic disc and exudate due to the network depth. However the accuracy can be improved by modifying the network architecture in the future.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.19-25
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• 2024

The worldwide aging population trend is causing an increase in the incidence of major retinal diseases that can lead to blindness, including glaucoma, cataract, and macular degeneration. In the field of ophthalmology, there is a focused interest in diagnosing diseases that are difficult to prevent in order to reduce the rate of blindness. This study proposes a deep learning approach to accurately diagnose ocular diseases in fundus photographs using less data than traditional methods. For this, Convolutional Neural Network (CNN) models capable of effective learning with limited data were selected to classify Conventional Fundus Images (CFI) from various ocular disease patients. The chosen CNN models demonstrated exceptional performance, achieving high Accuracy, Precision, Recall, and F1-score values. This approach reduces manual analysis by ophthalmologists, shortens consultation times, and provides consistent diagnostic results, making it an efficient and accurate diagnostic tool in the medical field.