• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.176-181
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• 2023

During cancer treatment, the patient's response to drugs appears differently at the cellular level. In this paper, an image-based cell phenotypic feature quantification and key feature selection method are presented to predict the response of patient-derived cancer cells to a specific drug. In order to analyze the viability characteristics of cancer cells, high-definition microscope images in which cell nuclei are fluorescently stained are used, and individual-level cell analysis is performed. To this end, first, image stitching is performed for analysis of the same environment in units of the well plates, and uneven brightness due to the effects of illumination is adjusted based on the histogram. In order to automatically segment only the cell nucleus region, which is the region of interest, from the improved image, a superpixel-based segmentation technique is applied using the fluorescence expression level and morphological information. After extracting 242 types of features from the image through the segmented cell region information, only the features related to cell viability are selected through the ReliefF algorithm. The proposed method can be applied to cell image-based phenotypic screening to determine a patient's response to a drug.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.227-235
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• 2000

Agricultural products are easily deformable its shape because of some external forces. However, these force behavior is difficult to measure quantitatively. Until now, many researches on the mechanical property was performed with various methods such as material testing, chemical analysis and non-destructive methods. In order to investigate force behavior on the cellular unit of agricultural products, electro-microscope based 3D image processing method will contribute to analysis of plant cells behavior. Before image measurement of plant cells, plant sample was cut off cross-sectioned area in a size of almost 300-400 ${\mu}$ m units using the micron thickness device, and some of preprocessing procedure was performed with fixing and dyeing. However, the wall structure of plant cell is closely neighbor each other, it is necessary to separate its boundary pixel. Therefore, image merging and shrinking algorithm was adopted to avoid disconnection. After then, boundary pixel was traced through thinning algorithm. Each image from the electro-microscope has a information of x,y position and its height along the z axis cross sectioned image plane. 3D image was constructed using the continuous image combination. Major feature was acquired from a fault image and measured area, thickness of cell wall, shape and unit cell volume. The shape of plant cell was consist of multiple facet shape. Through this measured information, it is possible to construct for structure shape of unit plant cell. This micro unit image processing techniques will contribute to the filed of agricultural mechanical property and will use to construct unit cell model of each agricultural products and information of boundary will use for finite element analysis on unit cell image.

• 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 Korea Multimedia Society
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• v.18 no.10
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• pp.1180-1188
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• 2015

In this paper, a parallel cell contour line extraction algorithm using CUDA, which has no inner contour lines, is proposed. The contour of a cell is very important in a cell image analysis. It could be obtained by a conventional serial contour tracing algorithm or parallel morphology operation. However, the cell image has various damages in acquisition or dyeing process. They could be turn into several inner contours, which make a cell image analysis difficult. The proposed algorithm introduces a min-max coordinates table into each CUDA thread block, and removes the inner contour in parallel. It is 4.1 to 7.6 times faster than a conventional serial contour tracing algorithm.

• Korean Chemical Engineering Research
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• v.61 no.2
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• pp.247-257
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• 2023

Precise counting of cell number stands in important position within clinical and research laboratories. Conventional methods such as hemocytometer, migration/invasion assay, or automated cell counters have limited in analytical time, cost, and accuracy., which needs an alternative way with time-efficient in-situ approach to broaden the application avenue. Here, we present simple coding-based cell counting method using image analysis tool, freely available image software (ImageJ). Firstly, we encapsulated RFP-expressing bacteria in a droplet using microfluidic device and automatically performed fluorescence image-based analysis for the quantification of cell numbers. Also, time-lapse images were captured for tracking the change of cell numbers in a droplet containing different concentrations of antibiotics. This study confirms that our approach is approximately 15 times faster and provides more accurate number of cells in a droplet than the external analysis program method. We envision that it can be used to the development of high-throughput image-based cell counting analysis.

• Journal of Biosystems Engineering
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• v.22 no.4
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• pp.433-438
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• 1997

The freshness of apple was evaluated by characterizing the surface texture of flesh cells. The freshness index which was related to the amount of wrinkles on the cell surface was defined to quantify the freshness. Four parameters relevant to the amount of the cell wrinkles were selected and measured using image analysis including wrinkle extraction procedure and Fast Fourier Transform of a wrinkle-extracted image. Out of 4 parameters, three parameters had highly significant correlations with the time elapsed after harvest. But it was concluded that two parameters out of such 3 parameters could be used for description of freshness index.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.55-63
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• 2013

Purpose: The present study aimed at image processing methods for automatic cell pattern recognition and morphological analysis for tissue engineering applications. The primary aim was to ascertain the novel algorithm of adaptive brightness correction from microscopic images for use as a potential image analysis. Methods: General microscopic image of cells has a minor problem which the central area is brighter than edge-area because of the light source. This may affect serious problems to threshold process for cell-number counting or cell pattern recognition. In order to compensate the problem, we processed to find the central point of brightness and give less weight-value as the distance to centroid. Results: The results presented that microscopic images through the brightness correction were performed clearer than those without brightness compensation. And the classification of mixed cells was performed as well, which is expected to be completed with pattern recognition later. Beside each detection ratio of hBMSCs and HeLa cells was 95% and 92%, respectively. Conclusions: Using this novel algorithm of adaptive brightness correction could control the easier approach to cell pattern recognition and counting cell numbers.

• Korean Journal of Clinical Laboratory Science
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• v.38 no.3
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• pp.203-207
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• 2006

The purpose of this study was to develop discriminant analysis models for predicting cervical normal/dysplasia case diagnoses using cytometric features derived from the digital image analysis of cell monolayers. The database consisted of 19 cases diagnosed either as normal (n=5), moderate dysplasia (n=7), severe dysplasia (n=7) on monolayer preparations. We studied the nuclear and cytoplasmic characteristics of cells in the normal, moderate dysplasia and severe dysplasia on cervical samples. The morphometric parameters selected for the analysis were nuclear/cytoplasmic ratio and the nuclear variations measured by image analysis on normal and precancerous lesions of cervical smears; several shape factors; area; perimeter; maximal, minimal and equivalent circle diameters. The results showed that the dysplasia samples exhibited changes in both cellular and nuclear form and size but lacked substantial differences in the tumor grades. The coefficient of nuclear variation is as follows to normal cell $21.8{\pm}3.2%$, moderate dysplasia $33.5{\pm}6.1%$, severe dysplasia $27.7{\pm}5.8$ of cervical smears.

• The Journal of Bigdata
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• v.4 no.1
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• pp.53-61
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• 2019

The extracellular matrix, which provides the structural and biochemical support of surrounding cells, is a cell physiological modulator that controls cell division and differentiation. In the bio sector, the company produces Scapold, a three-dimensional support for tissue engineering, and cultivates stem cells in the produced Scapold to be transplanted into animals to assess tissue regeneration. This depends on components such as collagen in the tissue. Therefore, it is very important to identify the inclusion rate and distribution of components in the tissue, and the data are obtained by analyzing the color of the dyed tissue image. The process from image collection to analysis is costly, and the data collected and analyzed are managed in different formats by different research institutions. Therefore, data integration management and analysis results search are not being performed. In this paper, we establish a database that can manage relevant bigdata in an integrated manner, and propose a bio-image integrated management and retrieval system that can be searched based on color, an important analytical measure in this field of study.

• Journal of Hydrogen and New Energy
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• v.28 no.5
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• pp.521-530
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• 2017

Improving the microstructure of NiO/YSZ is one of several approaches used to enhance the electrical and mechanical properties of an anode support in Solid Oxide Fuel Cells (SOFCs). The aim of the work reported in this paper was to predict the relationship between these microstructural changes and the resulting properties. To this end, modification of the anode microstructure was carried out using different sizes of Poly (Methyl Methacrylate) (PMMA) beads as a pore former. The electrical conductivity and mechanical strength of these samples were measured using four-probe DC, and three-point bend-test methods, respectively. Thermal etching followed by high resolution SEM imaging was performed for sintered samples to distinguish between the three phases (NiO, YSZ, and pores). Recently developed image analysis techniques were modified and used to calculate the porosity and the contiguity of different phases of the anode support. Image analysis results were verified by comparison with the porosity values determined from mercury porosimetry measurements. Contiguity of the three phases was then compared with data from electrical and mechanical measurements. A linear relationship was obtained between the contiguity data determined from image analysis, and the electrical and mechanical properties found experimentally. Based upon these relationships we can predict the electrical and mechanical properties of SOFC support from the SEM images.