• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.39-46
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• 2015

This paper presents an automatic method to detect and count the cells from microorganism images based on mobile environments. Cell counting is an important process in the field of biological and pathological image analysis. In the past, cell counting is done manually, which is known as tedious and time consuming process. Moreover, the manual cell counting can lead inconsistent and imprecise results. Therefore, it is necessary to make an automatic method to detect and count cells from biological images to obtain accurate and consistent results. The proposed multi-step cell counting method automatically segments the cells from the image of cultivated microorganism and labels the cells by utilizing topological analysis of the segmented cells. To improve the accuracy of the cell counting, we adopt watershed algorithm in separating agglomerated cells from each other and morphological operation in enhancing the individual cell object from the image. The system is developed by considering the availability in mobile environments. Therefore, the cell images can be obtained by a mobile phone and the processed statistical data of microorganism can be delivered by mobile devices in ubiquitous smart space. From the experiments, by comparing the results between manual and the proposed automatic cell counting we can prove the efficiency of the developed system.

• Journal of Biomedical Engineering Research
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• v.5 no.1
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• pp.69-76
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• 1984

An automatic white blood cell differential counting system was developed, which consists of feature extractor, main control computer, auto focus and search part and data acquisition part. This system is used as a clinical instrument whose purpose is to classify white blood cell images. It may also be used for other binary image processing.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.3
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• pp.224-230
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• 2002

In this paper, a counting algorithm hybridized with an adaptive automatic thresholding method based on Otsu's method and the algorithm that elongates markers obtained by the well-known watershed algorithm is proposed to enhance the exactness of the microcell counting in microscopic images. The proposed counting algorithm can be stated as follows. The transformed full image captured by CCD camera set up at microscope is divided into cropped images of m$\times$n blocks with an appropriate size. The thresholding value of the cropped image is obtained by Otsu's method and the image is transformed into binary image. The microbial cell images below prespecified pixels are regarded as noise and are removed in tile binary image. The smoothing procedure is done by the area opening and the morphological filter. Watershed algorithm and the elongating marker algorithm are applied. By repeating the above stated procedure for m$\times$n blocks, the m$\times$n segmented images are obtained. A superposed image with the size of 640$\times$480 pixels as same as original image is obtained from the m$\times$n segmented block images. By labeling the superposed image, the counting result on the image of microbial cells is achieved. To prove the effectiveness of the proposed mettled in counting the microbial cell on the image, we used Acinetobacter sp., a kind of ammonia-oxidizing bacteria, and compared the proposed method with the global Otsu's method the traditional watershed algorithm based on global thresholding value and human visual method. The result counted by the proposed method shows more approximated result to the human visual counting method than the result counted by any other method.

• 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.

• 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 the Korean Society of Visualization
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• v.19 no.2
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• pp.41-47
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• 2021

River and stream are the important water supply source in our lives. Eutrophication causes excessive green algae growth including microcystis, which makes harmful to ecosystem and human health. Therefore, the water purification process to remove green algae is essential. In Korea, green algae alarm system exists depending on the concentration of green algae cells in river or stream. To maintain the growth amount under control, green algae monitoring system is being used. However, the unmanned, small and automatic monitoring system would be preferable. In this study, we developed the 3D printed device to measure the concentration of green algae cell using microfluidic droplet generator and deep learning. Deep learning network was trained by using transfer learning through pre-trained deep learning network. This newly developed microfluidic cell counter has sufficient accuracy to be possibly applicable to green algae alarm system.

• Proceedings of the Korean Society of Food Hygiene and Safety Conference
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• 1996.06a
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• pp.33-33
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• 1996

Interests in the field of rapid methods and automation in microbiology have been growing steadily on an international scale in recent years. International meetings concerned this problem have been held in elsewhere in the world countries since the past twenty years. But, unfortunately in the field of microbial examination in food hygiene, this problem have not yet been developed so much as in the field of clinical microbiology. Today, I would like to introduce you here present aspects of rapid and automation technologies, those which are manly carrying in milk and meats industries. My illustration will be given recent improved technologies using automatic apparatus and instruments along with process of microbial count procedure. Recent direct microbiological counting system (ChemeScan \ulcorner) as real time ultrasensitive analysis created by Cheminex Ltd., France is now most evolutional instrument to provide direct microbial counts, down to one cell, within 30 minutes. The results from these evaluations how a good correlation between the ChemScan system and the standard plate count method. This system will be successful application for not only in the field of pharmacology but also food microbiology. In addition, current identification of microbes by sophisticated instruments suitable for food microbiology, one of which Biology is manual system (BIOLOG\ulcorner), provides reference-level capability at a modes price. For the manual system, the color reactions in the microplate are read by eye and manually keyed into personal computer. Species identification appears on the computer screen within seconds, along with biotype patterns, a list of closely related species, and other useful statistics. In present this is useful application for microbial ecology and epidemiological survey. RiboPrinter system newly produced by DuPont is now focusing among microbiologists in the world, and is one of the biggest microbial characterization system using a DNA-based approach. The technology analyzer is bacterial culture for its genetic fingerprint or riboprint pattern. Finally Bio-cellTracer system for automatic measurement of fungal growth and Fukitori-Maseter, a Surface Hygiene Monitoring Kit by using swabe procedure in food processing environment are briefly illustrated in this presentation.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.4
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• pp.399-405
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• 2018

The platelet count in clinical laboratories is essential for the diagnosis and treatment of hemostasis abnormalities, and accurate platelet counting in the low count range is of prime importance for deciding if a platelet transfusion is needed and for monitoring after chemotherapy. Quality control is designed to reduce and correct any deficiencies in the internal analytical process of a clinical laboratory prior to the release of patient results. Fragmented erythrocytes are the major confusing factors for platelet counting because of their similar size to platelets. The authors found that the low range QC values were out of 2SD with a Sysmex automatic analyzer in internal quality control process. Thus far, there has been little discussion on the relationship between hemolysis and the platelet parameters. Therefore, this study focused on the performance of automated platelet counts, including the PLT-F, the PLT-I, and PLT-O methods at the low platelet range using the low level QC materials and compared the 5 platelet parameters with the hemolyzed samples. The results showed that the CV was the smallest with PLT-F and P-LCR increased from 18.4 to 31.9% in the hemolysis samples. These results indicate that a more accurate estimation of the platelet counts can be achieved using the PLT-F method than the PLT-I method at the low platelet range. The use of the PLT-F system improves the confidence of results in low platelets samples in a routine hematology laboratory. The results suggest that P-LCR is a new parameter in assessing samples when the specimen is suspected of hemolysis and deterioration. Nevertheless, further studies will be needed to establish the relationship with P-LCR and hemolysis using human blood specimens.