• Journal of the Korea Society for Simulation
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• v.17 no.4
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• pp.167-174
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• 2008

The introduction of confocal microscopy makes it possible to observe the structural change of live neuronal cell. Neuro-degenerative disease, such as Alzheimer;s and Parkinson’s diseases are especially related to the morphological change of dendrite spine. That’s the reason for the study of segmentation and extraction from confocal microscope image. The difficulty comes from uneven intensity distribution and blurred boundary. Therefore, the image processing technique which can overcome these problems and extract the structural information should be suggested. In this paper, we propose robust structural information extracting technique with confocal microscopy images of dendrite in brain neurons. First, we apply the nonlinear diffusion filtering that enhance the boundary recognition. Second, we segment region of interest using iterative threshold selection. Third, we perform skeletonization based on Fast Marching Method that extracts centerline and boundary for analysing segmented structure. The result of the proposed method has been less sensitive to noise and has not been affected by rough boundary condition. Using this method shows more accurate and objective results.

• Journal of the Optical Society of Korea
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• v.16 no.2
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• pp.115-120
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• 2012

In this paper, we present a method to automatically quantify the three-dimensional (3D) volume of red blood cells (RBCs) using off-axis digital holographic microscopy. The RBCs digital holograms are recorded via a CCD camera using an off-axis interferometry setup. The RBCs' phase image is reconstructed from the recorded off-axis digital hologram by a computational reconstruction algorithm. The watershed segmentation algorithm is applied to the reconstructed phase image to remove background parts and obtain clear targets in the phase image with many single RBCs. After segmenting the reconstructed RBCs' phase image, all single RBCs are extracted, and the 3D volume of each single RBC is then measured with the surface area and the phase values of the corresponding RBC. In order to demonstrate the feasibility of the proposed method to automatically calculate the 3D volume of RBC, two typical shapes of RBCs, i.e., stomatocyte/discocyte, are tested via experiments. Statistical distributions of 3D volume for each class of RBC are generated by using our algorithm. Statistical hypothesis testing is conducted to investigate the difference between the statistical distributions for the two typical shapes of RBCs. Our experimental results illustrate that our study opens the possibility of automated quantitative analysis of 3D volume in various types of RBCs.

• Journal of Korean Ophthalmic Optics Society
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• v.20 no.1
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• pp.51-60
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• 2015

Purpose: To determine the effect of marketed multipurpose contact lens solutions (MPSs) on human corneal epithelial cells (HCEpiCs) toxicity by using image analysis. Methods: HCEpiCs were exposed six MPSs (product A-F) at 0.05~50% for 2h, 12h, 24h, and 48h respectively. HCEpiCs were fixed and stained with Draq5 after exposure with MPSs, and the cell viability and apoptosis were evaluated by using confocal microscope and ImageXpress UltraTM. Results: Viabilities of HCEpiCs exposed to MPS A-F for a 2h were not affected, while reductions (52~75%) in cell viability over a 12h exposure of MPS B, MPS C, MPS D and MPS F, and significant more reductions (29~73%) over a 24h and 48h-exposure. Apoptosis of HCEpiC was not affect over a 12h MPS exposure, however was significantly increased (199~526%) over 24h and 48h MPS exposure. Among the products MPS D, E and F reduced viability of HCEpiCs and apoptosis increased more than MPS A (p<0.05). Conclusions: Lower concentration of MPSs have not an cytotoxic effect on HCEpiCs, however higher concentration of MPSs induce apoptosis and reduce viability of HCEpiCs. Therefore, it need to develop MPS having antimicrobial effectiveness with low cytotoxicity.

• Journal of the Korea Computer Graphics Society
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• v.22 no.3
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• pp.21-29
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• 2016

Cell segmentation is an important but time-consuming and laborious task in biological image analysis. An automated, robust, and fast method is required to overcome such burdensome processes. These needs are, however, challenging due to various cell shapes, intensity, and incomplete boundaries. A precise cell segmentation will allow to making a pathological diagnosis of tissue samples. A vast body of literature exists on cell segmentation in microscopy images [1]. The majority of existing work is based on input images and predefined feature models only - for example, using a deformable model to extract edge boundaries in the image. Only a handful of recent methods employ data-driven approaches, such as supervised learning. In this paper, we propose a novel data-driven cell segmentation algorithm for bright-field microscopy images. The proposed method minimizes an energy formula defined by two dictionaries - one is for input images and the other is for their manual segmentation results - and a common sparse code, which aims to find the pixel-level classification by deploying the learned dictionaries on new images. In contrast to deformable models, we do not need to know a prior knowledge of objects. We also employed convolutional sparse coding and Alternating Direction of Multiplier Method (ADMM) for fast dictionary learning and energy minimization. Unlike an existing method [1], our method trains both dictionaries concurrently, and is implemented using the GPU device for faster performance.

• Journal of information and communication convergence engineering
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• v.4 no.1
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• pp.36-40
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• 2006

A new method of digital image analysis technique for medical images of cancer cell is presented. This paper deals with the cancer cell discrimination. The object images were the Thyroid Gland cell images that were diagnosed as normal and abnormal. This paper proposes a new discrimination method based on fuzzy logic algorithm. The focus of this paper is an automatic discrimination of cells into normal and abnormal of medical images by dominant feature parameters method with fuzzy algorithm. As a consequence of using fuzzy logic algorithm, the nucleus were successfully diagnosed as normal and abnormal. As for the experimental result, average recognition rate of 64.66% was obtained by applying single parameter of 16 feature parameters at a time. The discrimination rate of 93.08% was obtained by proposed method.

• Korean Journal of Clinical Laboratory Science
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• v.37 no.1
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• pp.41-46
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• 2005

It is known that mast cells (MCs) are increased in H. pylori-infected gastritis and its increase is mediated by stem cell factor (c-kit ligand). To determine the mechanism of mast cell recruitment and activation by stem cell factor, weinvestigated the expression of stem cell factor receptor (c-kit) in H. pylori-positive and -negative gastric mucosa. Biopsy specimens from 16 H. pylori-negative and 20 positive subjects were examined. H. pylori infection in gastric mucosa was examined by the Warthin-Starry method. MC and c-kit were identified by immunohistochemisty, using a monoclonal antihuman MC tryptase antibody and a polyclonal anti-human c-kit antibody. Densities of MC and c-kit positive cell were measured by a computerized image analysis system. MCs were detected in the lamina propria of both H. pylori-positive and -negative gastric mucosa. Densities of MC and c-kit positive cell were significantly greater in H. pylori-positive than -negative subjects. c-kit was located on the surface of MCs. These results indicate that stem cell factors may be one of the factors involved in mast cell increase and that they activate mast cells by binding with c-kit.

• Journal of the Korean Wood Science and Technology
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• v.29 no.2
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• pp.84-90
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• 2001

To investigate morphological characteristics of zephyr produced from two bamboo species, Phyllostachys nigra var. henonis and Gigantochloa apus, basic anatomic properties were examined by scanning electron microscopy and image analysis. According to SEM observation, zephyr from Phyllostachys nigra var. henonis was not of uniform in shape and showed macro crack between vascular bundle sheaths. This may be attributes to the sclerenchymatous fibers connected closely, thus resulting in difficult separation of intercellular layer. Zephyr from Gigantochloa apus was of uniform in shape, which may be caused by easy separation of intercellular layer of sclerenchymatous fibers having thin cell wall and large cell lumen. By image analysis in cross section of two species, the ratio of vascular bundle sheaths and cell wall ratio of sclerenchymatous fibers were examined. The ratio of vascular bundle sheaths in Phyllostachys nigra var. henonis was lower than that in Gigantochloa apus. However, cell wall ratio of sclerenchymatous fibers in Phyllostachys nigra var. henonis was higher than that in Gigantochloa apus.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.308-309
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• 2009

Human vision information processing has many characteristics when image information is transmitted from retina to visual cortex. Among them, we analyze the sensibility of the orientation on an image and compare the recognition rates by the response_weight of the vertical, horizontal and diagonal orientation. In statistics analysis, we show that a particular simple cell responds best to a bar with a vertical orientation. After then, we will apply the characteristics to Human visual system.

• ETRI Journal
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• v.41 no.1
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• pp.73-83
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• 2019

This paper describes in detail the processing path leading to successful phase images stitching in digital holographic microscope for the extension of the field of view. It applies FIJI Grid/Collection Stitching Plugin, which is a general tool for images stitching, non-specific for phase images. The FIJI plugin is extensively supported by aberration and phase offset correction. Comparative analysis of different aberration correction methods and data processing strategies is presented, together with the critical analysis of their applicability. The proposed processing path provides good background for statistical phase analysis of cell cultures and digital phase pathology.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.369-378
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• 2005

Document image is segmented and classified into text, picture, or table by a document layout analysis, and the words in table regions are significant for keyword spotting because they are more meaningful than the words in other regions. This paper proposes a method to extract words from table regions in document images. As word extraction from table regions is practically regarded extracting words from cell regions composing the table, it is necessary to extract the cell correctly. In the cell extraction module, table frame is extracted first by analyzing connected components, and then the intersection points are extracted from the table frame. We modify the false intersections using the correlation between the neighboring intersections, and extract the cells using the information of intersections. Text regions in the individual cells are located by using the connected components information that was obtained during the cell extraction module, and they are segmented into text lines by using projection profiles. Finally we divide the segmented lines into words using gap clustering and special symbol detection. The experiment performed on In table images that are extracted from Korean documents, and shows $99.16\%$ accuracy of word extraction.