• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.99-111
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• 2009

The result of Image segmentation, an indispensable process in image processing, significantly affects the analysis of an image. Despite the significance of image segmentation, it produces some problems when the variation of pixel values is large, or the boundary between background and an object is not clear. Also, these problems occur frequently when many objects in an image are placed very close by. In this paper, when the shape of objects in an image is circular, we proposed an algorithm which segment an each object in an image using the geometric characteristic of circular shape. The proposed algorithm is composed of 4 steps. First is the boundary edge extraction of whole object. Second step is to find the candidate points for further segmentation using the boundary edge in the first step. Calculating the representative circles using the candidate points is the third step. Final step is to draw the line connecting the overlapped points produced by the several erosions and dilations of the representative circles. To verify the efficiency of the proposed algorithm, the algorithm is compared with the three well-known cell segmentation algorithms. Comparison is conducted by the number of segmented region and the correctness of the inner segment line. As the result, the proposed algorithm is better than the well-known algorithms in both the number of segmented region and the correctness of the inner segment line by 16.7% and 21.8%, respectively.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Annual Conference of KIPS
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• 2008.05a
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• pp.141-144
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• 2008

본 논문에서는 세포영상을 분할하고 분류하는 알고리즘을 제안한다. 우선, 배경으로부터 세포를 분할한 후, 학습데이터로부터 얻은 Compactness, Smoothness, Moments와 같은 형태학적 특징을 추출한다. 전경세포들이 분할된 후에, 보다 정밀한 세포분석을 위해서 군집세포(Overlapped Cell)와 독립세포(Isolated Cell)를 분류 할 수 있는 알고리즘의 개발이 필수적이다. 이를 위해서 본 논문에서는 베이지안 네트워크와 각 노드에 대한 3개의 확률밀도함수를 사용하여 각 세포 영역을 분류한다. 분류된 군집세포영역은 향후 정확한 세포 분석을 위해서 군집세포가 포함하는 독립세포의 수만큼 마커를 찾고, Watershed 알고리즘과 병합과정을 거쳐 하나의 독립세포를 분리하게 된다. 현미경으로부터 얻은 세포영상에 대한 실험 결과는 이전 논문들에서 제안한 방법들과 비교했을 때, 각 군집세포의 독립세포로의 분리 이전에 세포영역에 대한 분류과정을 먼저 수행하였기 때문에 분할 성능이 크게 향상되었음을 확인할 수 있다.

• Molecules and Cells
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• v.27 no.4
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• pp.391-396
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• 2009

Image-based, high-content screening assays demand solutions for image segmentation and cellular compartment encoding to track critical events - for example those reported by GFP fusions within mitosis, signalling pathways and protein translocations. To meet this need, a series of nuclear/cytoplasmic discriminating probes have been developed: DRAQ5$^{TM}$ and CyTRAK Orange$^{TM}$. These are spectrally compatible with GFP reporters offering new solutions in imaging and cytometry. At their most fundamental they provide a convenient fluorescent emission signature which is spectrally separated from the commonly used reporter proteins (e.g. eGFP, YFP, mRFP) and fluorescent tags such as Alexafluor 488, fluorescein and Cy2. Additionally, they do not excite in the UV and thus avoid the complications of compound UV-autofluorescence in drug discovery whilst limiting the impact of background sample autofluorescence. They provide a convenient means of stoichiometrically labelling cell nuclei in live cells without the aid of DMSO and can equally be used for fixed cells. Further developments have permitted the simultaneous and differential labelling of both nuclear and cytoplasmic compartments in live and fixed cells to clearly render the precise location of cell boundaries which may be beneficial for quantitative expression measurements, cell-cell interactions and most recently compound in vitro toxicology testing.

• BMB Reports
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• v.44 no.3
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• pp.199-204
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• 2011

Ephrin signaling is involved in various morphogenetic events, such as axon guidance, hindbrain segmentation, and angiogenesis. We conducted a yeast two-hybrid screen using the intracellular domain (ICD) of EphrinB1 to gain biochemical insight into the function of the EphrinB1 ICD. We identified the transcriptional co-repressor xTLE1/Groucho as an EphrinB1 interacting protein. Whole-mount in situ hybridization of Xenopus embryos confirmed the co-localization of EphrinB1 and a Xenopus counterpart to TLE1, xTLE4, during various stages of development. The EphrinB1/xTLE4 interaction was confirmed by co-immunoprecipitation experiments. Further characterization of the interaction revealed that the carboxy-terminal PDZ binding motif of EphrinB1 and the SP domain of xTLE4 are required for binding. Additionally, phosphorylation of EphrinB1 by a constitutively activated fibroblast growth factor receptor resulted in loss of the interaction, suggesting that the interaction is modulated by tyrosine phosphorylation of the EphrinB1 ICD.

• Imaging Science in Dentistry
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• v.52 no.1
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• pp.27-32
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• 2022

Purpose: The aim of this study was to compare mastoid air cell volumes in patients with or without a pneumatized articular tubercle (PAT) on cone-beam computed tomography (CBCT) images. Materials and Methods: The CBCT images of 224 patients were retrospectively analyzed for the presence of PAT. The Digital Imaging and Communications in Medicine data of 30 patients with PAT and 30 individuals without PAT were transferred to 3D Doctor Software. Mastoid air cell volumes were measured using semi-automatic segmentation on axial sections. Data were analyzed using SPSS version 20.0. Results: The patients with PAT and those without PAT had a mean mastoid volume of 6.31±2.86 cm³ and 3.25±1.99 cm³, respectively. There were statistically significant differences in mastoid air cell volumes between patients with and without PAT regardless of sex and mastoid air cell side (P<0.05). Conclusion: The detection of PAT on routine dental radiographic examinations might be a potential prognostic factor that could be used to detect extensive pneumatization in the temporal bone. Clinicians should be aware that there may be widespread pneumatization of mastoid air cells in patients in whom PAT is detected. Advanced imaging should be performed in these cases, and possible complications due to surgical interventions should be considered.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.267-272
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• 2016

Microscope cell image is an important indicator for obtaining the biological information in a bio-informatics fields. Since human observers have been examining the cell image with microscope, a lot of time and high concentration are required to analyze cell images. Furthermore, It is difficult for the human eye to quantify objectively features in cell images. In this study, we developed HCS algorithm for automatic analysis of cell image using an OpenCV library. HCS algorithm contains the cell image preprocessing, cell counting, cell cycle and mitotic index analysis algorithm. We used human cancer cell (MKN-28) obtained by the confocal laser microscope for image analysis. We compare the value of cell counting to imageJ and to a professional observer to evaluate our algorithm performance. The experimental results showed that the average accuracy of our algorithm is 99.7%.

• Journal of Biomedical Engineering Research
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• v.33 no.4
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• pp.169-176
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• 2012

Tumor cell morphology is closely related to its migratory behaviors. An active tumor cell has a highly irregular shape, whereas a spherical cell is inactive. Thus, quantitative analysis of cell features is crucial to determine tumor malignancy or to test the efficacy of anticancer treatment. We use 3D time-lapse phase-contrast microscopy to analyze single cell morphology because it enables to observe long-term activity of living cells without photobleaching and phototoxicity, which is common in other fluorescence-labeled microscopy. Despite this advantage, there are image-level drawbacks to phase-contrast microscopy, such as local light effect and contrast interference ring. Therefore, we first corrected for non-uniform illumination artifacts and then we use intensity distribution information to detect cell boundary. In phase contrast microscopy image, cell is normally appeared as dark region surrounded by bright halo ring. Due to halo artifact is minimal around the cell body and has non-symmetric diffusion pattern, we calculate cross sectional plane which intersects center of each cell and orthogonal to first principal axis. Then, we extract dark cell region by analyzing intensity profile curve considering local bright peak as halo area. Finally, we calculated the Fourier descriptor that morphological characteristics of cell to classify tumor cells into active and inactive groups. We validated classification accuracy by comparing our findings with manually obtained results.

• Animal cells and systems
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• v.1 no.2
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• pp.329-335
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• 1997

The final pattern of the skeletal muscle of a vertebrate depends on the position-specific behavior of the muscle precursor cells during early developmental process and the abdominal muscle is made of cells which migrate a relatively long distance from their original tissue, myotome of dorsal mesoderm. We report the spatia-temporal migration pattern of abdominal muscle in Xenopus laevis by in situ hybridization and immunohistological studies. Shortly after hatching tadpole stage (stage 31/32), a group of myotomal cells detaches from the lower tip of the second somite and migrates ventrally to the lower position of abdomen. At stage 34/35, a second cell group migrates away from the third somite. Total 7 myotomal cell groups migrate ventrally one by one from the second to eighth myotome along their own pathways through the cell free space located between epidermis and subepidermal layer of the abdomen. During migration, the sizes of the cell groups (abdominal muscle anlagens) are increased to several tens fold. Around stage 40 all the abdominal muscle anlagens reaches their final positions and are interconnected side by side rostrocaudally. They are also connected to other types of muscles, forming a large multisegmented abdominal muscle. Heat shock study suggests that the disruption of segmentation of somites does not block the detachment of abdominal muscle anlagen, though the treatment gave stage- and dosagedependent effects on the migration speed.

• Applied Microscopy
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• v.51
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• pp.4.1-4.12
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• 2021

Fluorescence in situ hybridization (FISH) is a technique to visualize specific DNA/RNA sequences within the cell nuclei and provide the presence, location and structural integrity of genes on chromosomes. A confocal Whole Slide Imaging (WSI) scanner technology has superior depth resolution compared to wide-field fluorescence imaging. Confocal WSI has the ability to perform serial optical sections with specimen imaging, which is critical for 3D tissue reconstruction for volumetric spatial analysis. The standard clinical manual scoring for FISH is labor-intensive, time-consuming and subjective. Application of multi-gene FISH analysis alongside 3D imaging, significantly increase the level of complexity required for an accurate 3D analysis. Therefore, the purpose of this study is to establish automated 3D FISH scoring for z-stack images from confocal WSI scanner. The algorithm and the application we developed, SHIMARIS PAFQ, successfully employs 3D calculations for clear individual cell nuclei segmentation, gene signals detection and distribution of break-apart probes signal patterns, including standard break-apart, and variant patterns due to truncation, and deletion, etc. The analysis was accurate and precise when compared with ground truth clinical manual counting and scoring reported in ten lymphoma and solid tumors cases. The algorithm and the application we developed, SHIMARIS PAFQ, is objective and more efficient than the conventional procedure. It enables the automated counting of more nuclei, precisely detecting additional abnormal signal variations in nuclei patterns and analyzes gigabyte multi-layer stacking imaging data of tissue samples from patients. Currently, we are developing a deep learning algorithm for automated tumor area detection to be integrated with SHIMARIS PAFQ.