• Title, Summary, Keyword: Cell counting method

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A novel method for cell counting of Microcystis colonies in water resources using a digital imaging flow cytometer and microscope

  • Park, Jungsu;Kim, Yongje;Kim, Minjae;Lee, Woo Hyoung
    • 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.

Pyramidal Deep Neural Networks for the Accurate Segmentation and Counting of Cells in Microscopy Data

  • Vununu, Caleb;Kang, Kyung-Won;Lee, Suk-Hwan;Kwon, Ki-Ryong
    • 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.

An Automatic Mobile Cell Counting System for the Analysis of Biological Image (생물학적 영상 분석을 위한 자동 모바일 셀 계수 시스템)

  • Seo, Jaejoon;Chun, Junchul;Lee, Jin-Sung
    • 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.

A Segmentation Method for Counting Microbial Cells in Microscopic Image

  • Kim, Hak-Kyeong;Lee, Sun-Hee;Lee, Myung-Suk;Kim, Sang-Bong
    • 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.

A New Cell Counting Method to Evaluate Anti-tumor Compound Activity

  • Wang, Xue-Jian;Zhang, Xiu-Rong;Zhang, Lei;Li, Qing-Hua;Wang, Lin;Shi, Li-Hong;Fang, Chun-Yan
    • 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.

Battery Cell SOC Estimation Using Neural Network (뉴럴 네트워크를 이용한 배터리 셀 SOC 추정)

  • Ryu, Kyung-Sang;Kim, Ho-Chan
    • Journal of IKEEE
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    • v.24 no.1
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    • pp.333-338
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    • 2020
  • This paper proposes a method of estimating the SOC(State of Charge) of a battery cell using a neural network algorithm. To this, we implement a battery SOC estimation simulator and derive input and output data for neural network learning through charge and discharge experiments at various temperatures. Finally, the performance of the battery SOC estimation is analyzed by comparing with the experimental value by Ah-counting using Matlab/Simulink program and confirmed that the error rate can be reduced to less than 3%.

A Study on the Modeling and Analysis of Cell Delay Variation Compensation using Variable Timestamp Method in the Satellite TDMA Transmission (위성 TDMA 전송에서 가변타임스탬프 방식의 셀 지연변이 보상의 모델과 해석)

  • 김정호;박진양
    • 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.

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Mechanical properties, Biodegradability and Biocompatibility of Coronary Bypass Artery with PCL Layer and PLGA/Chitosan Mats Using Electrospinning

  • Nguyen, Thi-Hiep;Min, Young-Ki;Yang, Hun-Mo;Song, Ho-Yeon;Lee, Byong-Taek
    • Proceedings of the Materials Research Society of Korea Conference
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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.

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Area Measurement of Organism Image using Super Sampling and Interpolation (수퍼 샘플링과 보간을 이용한 생물조직 영상의 면적 측정)

  • Choi, Sun-Wan;Yu, Suk-Hyun
    • Journal of Korea Multimedia Society
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    • v.17 no.10
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    • pp.1150-1159
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    • 2014
  • This paper proposes a method for extracting tissue cells from an organism image by an electron microscope and getting the whole cell number and the area from the cell. In general, the difference between the cell color and the background is used to extract tissue cell. However, there may be a problem when overlapped cells are seen as a single cell. To solve the problem, we split them by using cell size and curvature. This method has a 99% accuracy rate. To measure the cell area, we compute two areas, the inside and boundary of the cell. The inside is simply calculated by the number of pixels. The cell boundary is obtained by applying super sampling, linear interpolation, and cubic spline interpolation. It improves the error rate, 18%, 19%, and 120% respectively, in comparison to the counting method that counts a pixel area as 1.

Enzymatic Determination of Somatic Cells by Using Transparisation in Raw Milk

  • Lee, Bou-Oung;Xu, Wen-Ying;Chang, Oun-Ki;Jin, Tai-Hua
    • Food Science of Animal Resources
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    • v.24 no.4
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    • pp.411-415
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    • 2004
  • The transparisation technology for milk and milk products could be applied widely and very importantly to various determination because transparisation can economize the cost and increase with precision in the milk payment system. Component of butanone or Triton in transparisation solvent would inhibit the growth of bacteria and method. Enzymatic determination of leucocytes were proposed to evaluate milk quality as mastitis in the milk payment system, this can be easily applied to simplify automation of the determation with the lowest investment cost in milk pay system. The significance of this technique, it can be used in the quality control of raw milk and milk products, milk payment system, and programming of national dairy project. Transparisation technology is used in somatic cell counting by enzymic methods. The range of deviation for this method is 16% in 74 samples. But the deviation is increased to 20% when the Infoss method is used. It is affected by the percentage of epithelial cells and white blood cells in somatic cells from different animals and the stages of aging. NAgase activity has an obvious correlation with white-blood cells in milk. In the case of mastitis the white-blood cells is 90-95% in somatic cells in milk, it is showing greater precision in measuring the state of mastitis. In conclusion the enzymic method of somatic cell counting is a relatively simple and easy method of measurement and can be easily practiced. And the importance of this method is also worth utilizing for indirect counting of Somatic cells by use of synthetic substrates to NAgase. In the future, with the further development of the research in this field, it will b possible to automatize the measurement.