• 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 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.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.8
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• pp.3397-3401
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• 2014

Determining cell quantity is a common problem in cytology research and anti-tumor drug development. A simple and low-cost method was developed to determine monolayer and adherent-growth cell quantities. The cell nucleus is located in the cytoplasm, and is independent. Thus, the nucleus cannot make contact even if the cell density is heavy. This phenomenon is the foundation of accurate cell-nucleus recognition. The cell nucleus is easily recognizable in images after fluorescent staining because it is independent. A one-to-one relationship exists between the nucleus and the cell; therefore, this method can be used to determine the quantity of proliferating cells. Results indicated that the activity of the histone deacetylase inhibitor Z1 was effective after this method was used. The nude-mouse xenograft model also revealed the potent anti-tumor activity of Z1. This research presents a new anti-tumor-drug evaluation method.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.3
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• pp.383-391
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• 2016

Ballast water provides stability and manoeuvrability to a ship. Foreign harmful aquatic organisms, which were transferred by ballast water, cause disturbing ecosystem. In order to minimize transference of foreign harmful aquatic organisms, IMO(International Maritime Organization) adopted the International Convention for the Control and Management of Ship's Ballast Water and Sediments in 2004. If the convention take effect, a port authority might need to check that ballast water is properly disposed of. In this paper, we propose a method of counting harmful aquatic organisms in ballast water thorough image processing. We extracted three samples from the ballast water that had been collected at Busan port in Korea. Then we made three grey-scale images from each sample as experimental data. We made a comparison between the proposed method and CellProfiler which is a well known cell-counting program based on image processing. Setting of CellProfiler is empirically chosen from the result of cell count by an expert. After finding a proper threshold for each image at which the result is similar to that of CellProfiler, we used the average value as the final threshold. Our experimental results showed that the proposed method is simple but about ten times faster than CellProfiler without loss of the output quality.

• Journal of the Korea Computer Industry Society
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• v.2 no.11
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• pp.1395-1406
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• 2001

In order to cover a widespread service range, terrestrial/satellite-mixed network is being combined with terrestrial ATM network. This dissertation analyzes and investigates several previously existent CDV compensation methods in order to compensate CDV arising from interfacing satellite TDMA and ATM. Specifically to supplement the problems of timestamp and cell number counting methods, new Variable Timestamp method for CDV compensation is proposed. To evaluate the proposed method, MMPP(Markov Modulated Poisson Process), which can express VBR service very well, is selected as a cell input traffic model of terrestrial transmitting earth station. After several simulation, it is also confirmed that CDV compensation capability for VBR services is very superior to the cell number counting method. In this case, as the timestamp number Nts increases, CDV compensation capability increases, and the CDV distribution length is reduced.

• Proceedings of the Materials Research Society of Korea Conference
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• 2009.05a
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• pp.45.2-45.2
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• 2009

A coronary graft fabricated from PLGA poly (lactic-co-glycolic acid) and chitosan electros puns deposited on poly caprolactone (PCL) electro spun tube. Mechanical properties of tube were evaluated through extruder machine depending on thickness of vessel wall. Biocompatible properties were evaluated by SEM morphology, amount of cell counting and MTT assay method for depending on culture days (1, 3, 5 days). MTT assay, counting cell and SEM morphology showed that cells were fast growth and immigration after 5 days. Biodegradability was monitored through loss weigh method for incubator days.