• Environmental Engineering Research
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• v.24 no.3
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• pp.397-403
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• 2019

Microcystis sp. is one of the most common harmful cyanobacteria that release toxic substances. Counting algal cells is often used for effective control of harmful algal blooms. However, Microcystis sp. is commonly observed as a colony, so counting individual cells is challenging, as it requires significant time and labor. It is urgent to develop an accurate, simple, and rapid method for counting algal cells for regulatory purposes, estimating the status of blooms, and practicing proper management of water resources. The flow cytometer and microscope (FlowCAM), which is a dynamic imaging particle analyzer, can provide a promising alternative for rapid and simple cell counting. However, there is no accurate method for counting individual cells within a Microcystis colony. Furthermore, cell counting based on two-dimensional images may yield inaccurate results and underestimate the number of algal cells in a colony. In this study, a three-dimensional cell counting approach using a novel model algorithm was developed for counting individual cells in a Microcystis colony using a FlowCAM. The developed model algorithm showed satisfactory performance for Microcystis sp. cell counting in water samples collected from two rivers, and can be used for algal management in fresh water systems.

• Journal of KIISE
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• v.42 no.10
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• pp.1239-1246
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• 2015

Cell counting is extensively used to analyze cell growth in biomedical research, and as a result automated cell counting methods have been developed to provide a more convenient and means to analyze cell growth. However, there are still many challenges to improving the accuracy of the cell counting for cells that proliferate abnormally, divide rapidly, and cluster easily, such as cancer cells. In this paper, we present an automated cell counting method for HeLa cells, which are used as reference for cancer research. We recognize and classify the morphological conditions of the cells by using a cell segmentation algorithm based on cell membrane extraction, and we then apply a cell back-tracking algorithm to improve the cell counting accuracy in cell clusters that have indistinct cell boundary lines. The experimental results indicate that our proposed segmentation method can identify each of the cells more accurately when compared to existing methods and, consequently, can improve the cell counting accuracy.

• Journal of Korea Multimedia Society
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• v.22 no.3
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• pp.335-348
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• 2019

Cell segmentation and counting represent one of the most important tasks required in order to provide an exhaustive understanding of biological images. Conventional features suffer the lack of spatial consistency by causing the joining of the cells and, thus, complicating the cell counting task. We propose, in this work, a cascade of networks that take as inputs different versions of the original image. After constructing a Gaussian pyramid representation of the microscopy data, the inputs of different size and spatial resolution are given to a cascade of deep convolutional autoencoders whose task is to reconstruct the segmentation mask. The coarse masks obtained from the different networks are summed up in order to provide the final mask. The principal and main contribution of this work is to propose a novel method for the cell counting. Unlike the majority of the methods that use the obtained segmentation mask as the prior information for counting, we propose to utilize the hidden latent representations, often called the high-level features, as the inputs of a neural network based regressor. While the segmentation part of our method performs as good as the conventional deep learning methods, the proposed cell counting approach outperforms the state-of-the-art methods.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.113-119
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• 2020

This study proposed the structure of the cluster's cell counting algorithm for cell analysis. The image required for cell count is taken under a microscope. At present, the cell counting algorithm is reported to have a problem of low accuracy of results due to uneven shape and size clusters. To solve these problems, the proposed algorithm has a feature of calculating the number of cells in a cluster by applying a radius change analysis to the existing distance conversion and watershed algorithm. Later, cell counting algorithms are expected to yield reliable results if applied to the required field.

• 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.

• 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.

• Restorative Dentistry and Endodontics
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• v.34 no.6
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• pp.491-499
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• 2009

This study examined the influence of the storage methods on the viability of oral epithelial cells using conventional cell freezing storage, slow freezing preservation, rapid freezing preservation, and slow freezing preservation with a pressure of 2 Mpa or 3 Mpa. The cell viability was evaluated by cell counting, WST-1 and the clonogenic capacity after 6 days of freezing storage. After 6 days, the frozen cells were thawed rapidly, and the cell counting. WST-1, and clonogenic capacity values were measured and compared. 1. The results from cell counting demonstrated that conventional cryopreservation, slow freezing under a 2 Mpa pressure and slow freezing under a 3 Mpa pressure showed significantly higher values than slow freezing preservation and rapid freezing preservation (p < 0.05). 2. The results from the optical density by WST-1 demonstrated that slow freezing under a 2 Mpa pressure showed significantly higher values than slow freezing preservation and rapid freezing preservation (p<0.05). 3. The clonogenic capacity demonstrated that slow freezing under a 2 Mpa pressure showed significantly higher values than slow freezing preservation and rapid freezing preservation (p < 0.05).

• 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%.

• 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.

• ALGAE
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• v.23 no.4
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• pp.257-268
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• 2008

Species composition, standing crop and dominant species of phytoplankton were investigated at 6 sites of Galgok stream and Incheon river which run into Gomso Bay, Jeollanam-do in April, August and November 2007. Total of 210 taxa were found which were classified as 6 classes, 12 orders, 25 families, 66 genera, 177 species, 27 varieties, 5 forms and 1 unidentified species. These river and stream had lower concentration of T-N and T-P compared to that of others; however, the site 2 of Galgok stream in summer was hypertrophic in T-P and also near hypertrophic in TN, and the site 1 in Incheon river during fall appeared to be hypertrophic in T-N, and the site 3 in summer showed near hypertrophic level in T-P. Determining the trophic status of the water quality based on chlorophyll a (chl-a), the site 3 of Galgok stream in spring and the site 3 of Incheon river in fall were oligotrophic; moreover, 6 sites including the site 2 and 3 of Galgok stream in summer were mesotrophic, and 9 sites including all sites of Galgok stream in fall turned out to be eutrophic. Particularly, the site 1 of Galgok stream in summer was hypertrophic, having 58.19 mg chl-a m$^{-3}$. There was a conspicuous difference between two values of standing crops based on chl-a and cell counting. This discrepancy may have occurred because of abundant cyanophytes and exclusion of picoplankton cells in cell counting. In the study, 5 cyanophytes, Synechocystis aquatilis, Microcystis aeruginosa, M. flos-aquae, Oscillatoria angustissima, O. limnetica and 2 diatoms, Thalassiosira bramaputrae and Navicula viridula var. rostellata were abundant. Based on the T-N, T-P values, standing crops and cell counting in Galgok stream and Incheon river were between mesotrophic and eutrophic conditions.