Добірка наукової літератури з теми "Blood cell counting"
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Статті в журналах з теми "Blood cell counting"
Piacentini, Niccolò, Danilo Demarchi, Pierluigi Civera, and Marco Knaflitz. "Microsystems for Blood Cell Counting." Advances in Science and Technology 57 (September 2008): 55–60. http://dx.doi.org/10.4028/www.scientific.net/ast.57.55.
Повний текст джерелаSchmidt, R. M. "Automated differential blood cell counting systems." Clinical & Laboratory Haematology 1, no. 2 (June 28, 2008): 149–50. http://dx.doi.org/10.1111/j.1365-2257.1979.tb00463.x.
Повний текст джерелаSmith, Suzanne, Phophi Madzivhandila, René Sewart, Ureshnie Govender, Holger Becker, Pieter Roux, and Kevin Land. "Microfluidic Cartridges for Automated, Point-of-Care Blood Cell Counting." SLAS TECHNOLOGY: Translating Life Sciences Innovation 22, no. 2 (November 19, 2016): 176–85. http://dx.doi.org/10.1177/2211068216677820.
Повний текст джерелаLewis, S. M., J. M. England, and F. Kubota. "Coincidence correction in red blood cell counting." Physics in Medicine and Biology 34, no. 9 (September 1, 1989): 1239–46. http://dx.doi.org/10.1088/0031-9155/34/9/009.
Повний текст джерелаWalberg, James. "White blood cell counting techniques in birds." Seminars in Avian and Exotic Pet Medicine 10, no. 2 (April 2001): 72–76. http://dx.doi.org/10.1053/saep.2001.22051.
Повний текст джерелаFatichah, Chastine, Diana Purwitasari, Victor Hariadi, and Faried Effendy. "OVERLAPPING WHITE BLOOD CELL SEGMENTATION AND COUNTING ON MICROSCOPIC BLOOD CELL IMAGES." International Journal on Smart Sensing and Intelligent Systems 7, no. 3 (2014): 1271–86. http://dx.doi.org/10.21307/ijssis-2017-705.
Повний текст джерелаChaturvedi, Shruti. "Counting the cost of caplacizumab." Blood 137, no. 7 (February 18, 2021): 871–72. http://dx.doi.org/10.1182/blood.2020009250.
Повний текст джерелаJames L, Sherley, Daley Michael P, and Dutton Renly A. "Validation of Kinetic Stem Cell (KSC) counting algorithms for rapid quantification of human hematopoietic stem cells." Journal of Stem Cell Therapy and Transplantation 6, no. 1 (November 28, 2022): 029–37. http://dx.doi.org/10.29328/journal.jsctt.1001028.
Повний текст джерелаH. Al-khafaji, Kawther, and Athraa H. Al-khafaji. "Diagnoses of Blood Disorder in Different Animal Species Depending on Counting Methods in Blood Cell Images." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 660. http://dx.doi.org/10.14419/ijet.v7i4.36.24218.
Повний текст джерелаMATSUNO, K., and 美恵 森本. "Peripheral Blood Cell Counting by Automated Hematology Analyzer." JAPANES JOURNAL OF MEDICAL INSTRUMENTATION 69, no. 1 (January 1, 1999): 25–29. http://dx.doi.org/10.4286/ikakikaigaku.69.1_25.
Повний текст джерелаДисертації з теми "Blood cell counting"
Li, Nan. "Lab-on-a-chip systems for blood cell separation, counting, and characterization." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1872070051&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Повний текст джерелаTheera-Umpon, Nipon. "Morphological granulometric estimation with random primitives and applications to blood cell counting /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9974689.
Повний текст джерелаMauricio, Claudio Roberto Marquetto. "Contador de células vermelhas baseado em imagens para múltiplas espécies de animais silvestres e domésticos." Universidade Tecnológica Federal do Paraná, 2017. http://repositorio.utfpr.edu.br/jspui/handle/1/2314.
Повний текст джерелаA RBC count plays an important role in the diagnostic of wild and domestic animals. Despite the many technologies available in different automated hematology analyzers, when it comes to blood of wild animals it is still difficult to find an easy and affordable solution for multiple species. This study aims to develop an automatic red blood cell counter. Blood samples (1 ocelot - Leopardus pardalis, 1 monkey - Cebus apella, 1 coati - Nasua nasua, 62 dogs - Canis familiaris and 5 horses - Equus caballus) were analyzed using three methods: 1-manual count, 2automatic count by image and 3-semi-automatic count by image; blood from dogs and horses were also analyzed by a fourth method: 4-automatic count by impedance. The counts of methods 2 and 3 were produced by the proposed red blood cell counter. Results were compared using Pearson’s correlation and plots with different methods as the criterion standard. RBC counts of methods 1, 2 and 3 correlated very well with those on the method 4 (r ≥ 0.94). RBC counts produced by method 2 were highly correlated with method 3 (r = 0.998). The results indicate that the proposed method can be used as an automatic or semi-automatic counting method in clinics that are currently using the manual method for RBC assessment.
SILVA, NATAN V. da. "Produção e estudo de atividade antiangiogênica de proteínas de fusão endostatina-domínio BH3 das proteínas pró-apoptóticas PUMA e BIM." reponame:Repositório Institucional do IPEN, 2015. http://repositorio.ipen.br:8080/xmlui/handle/123456789/26940.
Повний текст джерелаMade available in DSpace on 2016-12-22T11:54:03Z (GMT). No. of bitstreams: 0
A endostatina (ES) é uma proteína inibidora da angiogênese, com ação específica sobre células endoteliais em proliferação, utilizada para tratamento de tumores sólidos. No entanto, o elevado efeito antitumoral da ES observado em animais não é reproduzido em humanos. Com o intuito de potencializar a eficácia terapêutica da ES, produzimos duas proteínas híbridas com dois domínios funcionais. O primeiro domínio é a ES, que apresenta especificidade por células endoteliais ativadas, dirigindo estas proteínas de fusão às células endoteliais em proliferação, promovendo sua internalização e seu efeito inibitório. Como segundo domínio funcional utilizamos os domínios BH3 próapoptóticos de duas proteínas BH3-only com o objetivo de promover a liberação de citocromo C e desencadear o processo de apoptose, aumentando a ação antiangiogênica da ES. Neste trabalho, foram desenhadas duas proteínas de fusão que contêm o domínio BH3 das potentes proteínas pró apoptóticas PUMA e BIM (ES-PUMA e ES-BIM), que deveriam apresentar efeito antiangiogênico potencializado em relação à ES selvagem. A inserção dos fragmentos de DNA codificantes para os domínios BH3 de PUMA e BIM no vetor contendo o gene da ES (pET-ES) foram realizadas por mutagênese sítiodirigida. Estas proteínas de fusão recombinantes foram expressas como corpos de inclusão em E.coli, renaturadas utilizando processo que utiliza alta pressão e purificadas em resina de afinidade por heparina. O tratamento de células endoteliais com as proteínas ES-PUMA e ES-BIM não levou à queda de viabilidade em ensaio de MTS ou de apoptose avaliado por citometria de fluxo, em comparação com os resultados obtidos pelo tratamento com ES.
Dissertação (Mestrado em Tecnologia Nuclear)
IPEN/D
Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP
Andrade, Hugo Miguel Felgueira de. "Image processing methodology for blood cell counting via mobile devices." Master's thesis, 2015. https://repositorio-aberto.up.pt/handle/10216/79416.
Повний текст джерелаAndrade, Hugo Miguel Felgueira de. "Image processing methodology for blood cell counting via mobile devices." Dissertação, 2015. https://repositorio-aberto.up.pt/handle/10216/79416.
Повний текст джерелаYang, Sheng, and 楊昇. "Developing a microfluidic device with hydrodynamic trap arrays for white blood cell counting in peritoneal dialysis solution." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/64yxz9.
Повний текст джерела國立臺灣大學
生醫電子與資訊學研究所
105
Peritoneal dialysis is a treatment for patients who suffer from severe chronic kidney disease. To prevent any infection during the treatment, it is important to monitor the population of white blood cell in peritoneal dialysis. At present, fluorescence-based flow cytometry and the automated hemocytometer are two prevailing methods to quantify the population of white blood cell. However, these techniques usually exist several limitations, such as (1) cannot deal with low white blood cell level (<300 cells/μL), (2) laborious assay preparation and manipulation steps. To address the above problems, we develop a microfluidic device with hydrodynamic trap arrays to capture white blood cells. The microfluidic microtrap array with multiple dimensions can trap general white blood cells and specific white blood cell subpopulation conjugated with 30μm polystyrene beads. This microfluidic platform enables simultaneously cell trapping, selection and can perform simple and real-time cell counting without complicated sample processing steps and equipment. To make the system more user-friendly and be suitable for point-of-care (POC) settings, we plan to make this microfluidic device compatible to existing smartphones and APP, which can perform image processing, analysis and data transmission. We believe this microfluidic platform for surveillance of white blood cell level will hold significant promise to provide the detailed infection status of patient for doctors to perform timely treatment.
Liu, Hung-Chieh, and 劉宏傑. "An Improved Method For Counting Red Blood Cells." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/70376862824802462104.
Повний текст джерела輔仁大學
資訊工程學系碩士班
103
Pulse coupled neural network (Pulse Coupled Neural Network, PCNN) is a new neural network evolved from traditional artificial neural network. and issued in1990 by Eckhornin accordance withthe sync pulse phenomenon of visual cortex of cats, monkeys and other animals.Related research and sustained developments have been widely used in image processing, target recognition, minimum path, and decision optimization. Image segmentation in the study of PCNN image processing technology, with similar neuronal input pulses occur simultaneously, for the image a little choppy and range obvious change inputs have a very effective remedy in order to retain a more complete picture regional information, which is the strength of image segmentation techniques, but when used PCNN image segmentation exist cycles of iterations can’t determine the problem.Through several experiments, PCNN image automatically wave propagation technology is found to be poor for segmentation of overlapped red blood cells and filter of small particles. In order to improve the above disadvantages, this paper proposes another way of thinking,PCNN edge detection technology can becombined with Hough circle detection.After conducting several experiments, results show that this method is superior to the original PCNN image segmentation.It can indeed achieve the separation of overlapped red blood cells and to filter out tiny particles.Soit is more in line with human eyes visually in counting the number of red blood cells.
Книги з теми "Blood cell counting"
McCann, Shaun R. The role of technology in haematology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198717607.003.0011.
Повний текст джерелаЧастини книг з теми "Blood cell counting"
Piacentini, Niccolò, Danilo Demarchi, Pierluigi Civera, and Marco Knaflitz. "Microsystems for Blood Cell Counting." In Advances in Science and Technology, 55–60. Stafa: Trans Tech Publications Ltd., 2008. http://dx.doi.org/10.4028/3-908158-14-1.55.
Повний текст джерелаChaudhary, Ayesha Hoor, Javeria Ikhlaq, Muhammad Aksam Iftikhar, and Maham Alvi. "Blood Cell Counting and Segmentation Using Image Processing Techniques." In Applications of Intelligent Technologies in Healthcare, 87–98. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96139-2_9.
Повний текст джерелаMohamed, Shahd T., Hala M. Ebeid, Aboul Ella Hassanien, and Mohamed F. Tolba. "Automatic White Blood Cell Counting Approach Based on Flower Pollination Optimization Multilevel Thresholoding Algorithm." In Advances in Intelligent Systems and Computing, 313–23. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99010-1_29.
Повний текст джерелаAit Mehdi, Mohamed, Khadidja Belattar, and Feriel Souami. "An Enhanced Blood Cell Counting System Using Swin Transformer with Dynamic Head and KNN Model." In Communications in Computer and Information Science, 95–106. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4484-2_8.
Повний текст джерелаHuang, Luojie, Gregory N. McKay, and Nicholas J. Durr. "A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 415–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87237-3_40.
Повний текст джерелаChatterjee, Joydeep, Semanti Chakraborty, and Kanik Palodhi. "A Novel Automated Blood Cell Counting Method Based on Deconvolution and Convolution and Its Application to Neural Networks." In Advances in Intelligent Systems and Computing, 67–78. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2930-6_6.
Повний текст джерелаMahanta, Lipi B., Kangkana Bora, Sourav Jyoti Kalita, and Priyangshu Yogi. "Automated Counting of Platelets and White Blood Cells from Blood Smear Images." In Lecture Notes in Computer Science, 13–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34872-4_2.
Повний текст джерелаDoering, Elena, Anna Pukropski, Ulf Krumnack, and Axel Schaffand. "Automatic Detection and Counting of Malaria Parasite-Infected Blood Cells." In Medical Imaging and Computer-Aided Diagnosis, 145–57. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5199-4_15.
Повний текст джерелаPalodhi, Kanik, Dhrubajyoti Dawn, and Amiya Halder. "Blood Cells Counting by Dynamic Area-Averaging Using Morphological Operations to SEM Images of Cancerous Blood Cells." In Advances in Intelligent Systems and Computing, 267–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28658-7_23.
Повний текст джерелаRathore, Saima, Aksam Iftikhar, Ahmad Ali, Mutawarra Hussain, and Abdul Jalil. "Capture Largest Included Circles: An Approach for Counting Red Blood Cells." In Communications in Computer and Information Science, 373–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28962-0_36.
Повний текст джерелаТези доповідей конференцій з теми "Blood cell counting"
Piacentini, Niccolo, Danilo Demarchi, Pierluigi Civera, and Marco Knaflitz. "MEMS-based blood cell counting system." In 2008 15th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2008). IEEE, 2008. http://dx.doi.org/10.1109/icecs.2008.4674825.
Повний текст джерелаNguyen, Viet Dung, Duy Hoang Ho, Viet Long Nguyen, and Ngoc Dung Bui. "Modified YOLOv5 for Blood Cell Counting." In 2022 RIVF International Conference on Computing and Communication Technologies (RIVF). IEEE, 2022. http://dx.doi.org/10.1109/rivf55975.2022.10013896.
Повний текст джерелаBerge, Heidi, Dale Taylor, Sriram Krishnan, and Tania S. Douglas. "Improved red blood cell counting in thin blood smears." In 2011 8th IEEE International Symposium on Biomedical Imaging (ISBI 2011). IEEE, 2011. http://dx.doi.org/10.1109/isbi.2011.5872388.
Повний текст джерелаTulsani, Hemant, Rashmi Gupta, and Rajiv Kapoor. "An improved methodology for blood cell counting." In 2013 International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT). IEEE, 2013. http://dx.doi.org/10.1109/mspct.2013.6782094.
Повний текст джерелаSu, Ting-Wei, Sungkyu Seo, Anthony Erlinger, and Aydogan Ozcan. "High-Throughput Cell Imaging, Counting and Characterization on a Chip." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-193255.
Повний текст джерелаVenkatalakshmi, B., and K. Thilagavathi. "Automatic red blood cell counting using hough transform." In 2013 IEEE Conference on Information & Communication Technologies (ICT). IEEE, 2013. http://dx.doi.org/10.1109/cict.2013.6558103.
Повний текст джерелаNarsale, Achal, Sakshi Nalwade, Medha Badgire, Sandhyarani Survase, and Chetan N. Aher. "Blood Cell Detection and Counting via Deep Learning." In 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC). IEEE, 2022. http://dx.doi.org/10.1109/assic55218.2022.10088344.
Повний текст джерелаMauricio, Claudio R. M., Fábio K. Schneider, and Leonilda Correia dos Santos. "Image-based red cell counting for wild animals blood." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5627383.
Повний текст джерелаSafuan, Syadia Nabilah Mohd, Razali Tomari, Wan Nurshazwani Wan Zakaria, and Nurmiza Othman. "White blood cell counting analysis of blood smear images using various segmentation strategies." In ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING: FROM THEORY TO APPLICATIONS: Proceedings of the International Conference on Electrical and Electronic Engineering (IC3E 2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5002036.
Повний текст джерелаMacawile, Merl James, Vonn Vincent Quinones, Alejandro Ballado, Jennifer Dela Cruz, and Meo Vincent Caya. "White blood cell classification and counting using convolutional neural network." In 2018 3rd International Conference on Control and Robotics Engineering (ICCRE). IEEE, 2018. http://dx.doi.org/10.1109/iccre.2018.8376476.
Повний текст джерела