• Journal of the Optical Society of Korea
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• v.10 no.4
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• pp.145-156
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• 2006

In the field of ophthalmology, many diagnostic instruments based on optical technology have been developed, such as refractometer, keratometer, corneal mapper, tonometer, fundus camera, slit lamp, laser scan ophthalmoscope and optical coherence tomography. Among them, the refractometer that is used for measuring the refractive power of the human eye has the long research history and various types have been developed. However the efforts to realize more accurate and precise measurement are still in progress. The wavefront analyzer commercialized in recent years is an excellent outcome of such efforts. In this paper, a brief account of the developmental history of various refractometers including the wavefront analyzer is summarized, and the underlying measurement principle is introduced in the view of optics. Finally, the technical issues that should be solved for getting better performance are discussed.

• Journal of the Optical Society of Korea
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• v.12 no.2
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• pp.98-102
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• 2008

Optical Coherence Tomography (OCT) is an important noninvasive medical imaging technique that can reveal subsurface structures of biological tissue. OCT has demonstrated a good correlation with histology in sufficient resolution to identify morphological changes in articular cartilage to differentiate normal through progressive stages of degenerative joint disease. Current OCT systems provide individual cross-sectional images that are representative of the tissue directly under the scanning beam, but they may not fully demonstrate the degree of degeneration occurring within a region of a joint surface. For a full understanding of the nature and degree of cartilage degeneration within a joint, multiple OCT images must be obtained and an overall assessment of the joint surmised from multiple individual images. This study presents frequency domain three-dimensional (3-D) OCT imaging of degenerative joint cartilage extracted from bovine knees. The 3-D OCT imaging of articular cartilage enables the assembly of 126 individual, adjacent, rapid scanned OCT images into a full 3-D image representation of the tissue scanned, or these may be viewed in a progression of successive individual two-dimensional (2-D) OCT images arranged in 3-D orientation. A fiber-based frequency domain OCT system that provides cross-sectional images was used to acquire 126 successive adjacent images for a sample volume of $6{\times}3.2{\times}2.5\;mm^3$. The axial resolution was $8\;{\mu}m$ in air. The 3-D OCT was able to demonstrate surface topography and subsurface disruption of articular cartilage consistent with the gross image as well as with histological cross-sections of the specimen. The 3-D OCT volumetric imaging of articular cartilage provides an enhanced appreciation and better understanding of regional degenerative joint disease than may be realized by individual 2-D OCT sectional images.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.9
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• pp.23-31
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• 2007

• Journal of the Korean Society for Precision Engineering
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• v.23 no.1 s.178
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• pp.41-47
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• 2006

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.04a
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• pp.122-122
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• 2022

• Journal of Dental Rehabilitation and Applied Science
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• v.32 no.1
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• pp.60-69
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• 2016

Purpose: The objective of this study was to compare the accuracy of digital models from 3 dimentional (3D) optical scanner and cone beam computed tomography (CBCT). Materials and Methods: We obtained digital models from 11 pairs of stone casts using a 3D optical scanner and a CBCT, and compared the accuracy of the models. Results: The error range of average positive distance was 0.059 - 0.117 mm and negative distance was 0.066 - 0.146 mm. Statistically (P < 0.05), average positive distance was larger than $70{\mu}m$ and shorter than $100{\mu}m$, and that of negative distance was larger than $100{\mu}m$ and shorter than $120{\mu}m$. Conclusion: We concluded that the accuracy of digital models generated from CBCT is not appropriate to make final prostheses. However, it may be acceptable for provisional restorations and orthodontic diagnoses with respect to the accuracy of the digitalization.

• Journal of Biomedical Engineering Research
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• v.33 no.2
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• pp.53-58
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• 2012

Intraoperative surgical microscope is an essential surgical equipment. However, it has a restriction to classify the retina layers because of the contrast differences. To solve this problem, operators use surgical instrument such as an intraocular mirror. In this case, it has to amputate the patient's eye. In this study, we developed a probe the intraoperative surgical optical coherence tomography. We expect that the developed OCT probe can overcome the limit of OCT and be applied as a real-time surgical tool. In this paper, we demonstrate applicability of the probe through rabbit's experimentation.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1291-1298
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• 2017

Optical coherence tomography (OCT) is the most common medical imaging device with the ability to image the retina in eyes at micrometer resolution and to visualize the pathological indicators of many retinal diseases such as Age-Related Macular Degeneration (AMD) and diabetic retinopathy. Accordingly, there have been research activities to analyze and process OCT images to identify those indicators and make medical decisions based on the findings. In this paper, we use a deep convolutional neural network for analysis of OCT volume data to distinguish AMD from normal patients. We propose a novel approach where images in each OCT volume are grouped together into sub-volumes and the network is trained by those sub-volumes instead of individual images. We conducted an experiment using public data set to evaluate the performance of the proposed approach. The experiment confirmed performance improvement of our approach over the traditional image-by-image training approach.

• Biomolecules & Therapeutics
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• v.26 no.1
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• pp.81-92
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• 2018

It is widely accepted that altered metabolism contributes to cancer growth and has been described as a hallmark of cancer. Our view and understanding of cancer metabolism has expanded at a rapid pace, however, there remains a need to study metabolic dependencies of human cancer in vivo. Recent studies have sought to utilize multi-modality imaging (MMI) techniques in order to build a more detailed and comprehensive understanding of cancer metabolism. MMI combines several in vivo techniques that can provide complementary information related to cancer metabolism. We describe several non-invasive imaging techniques that provide both anatomical and functional information related to tumor metabolism. These imaging modalities include: positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS) that uses hyperpolarized probes and optical imaging utilizing bioluminescence and quantification of light emitted. We describe how these imaging modalities can be combined with mass spectrometry and quantitative immunochemistry to obtain more complete picture of cancer metabolism. In vivo studies of tumor metabolism are emerging in the field and represent an important component to our understanding of how metabolism shapes and defines cancer initiation, progression and response to treatment. In this review we describe in vivo based studies of cancer metabolism that have taken advantage of MMI in both pre-clinical and clinical studies. MMI promises to advance our understanding of cancer metabolism in both basic research and clinical settings with the ultimate goal of improving detection, diagnosis and treatment of cancer patients.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.541-543
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• 2022

In recent years, retinal disorders have become a serious health concern. Retinal disorders develop slowly and without obvious signs. To avoid vision deterioration, early detection and treatment are critical. Optical coherence tomography (OCT) is a non-invasive and non-contact medical imaging technique used to acquire informative and high-resolution image of retinal area and underlying layers. Disease signs are difficult to detect because OCT images have many areas which are not related to any disease. In this paper, we present a deep learning-based method to perform multi-label classification on a long-tailed OCT dataset. Our method first extracts the region of interest and then performs the classification task. We achieve 98% accuracy, 92% sensitivity, and 99% specificity on our private OCT dataset. Using the heatmap generated from trained convolutional neural network, our method is more robust and explainable than previous approaches because it focuses on areas that contain disease signs.