Relevant bibliographies by topics / Peripheral blood smear

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Peripheral blood smear'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Peripheral blood smear"

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Gopal, Shalini, Rashmi Naik, Ahmed Mujib B.R, and Arun Kumar N. "GENDER DETERMINATION USING PERIPHERAL BLOOD SMEAR." International Journal of Anatomy and Research 6, no. 2.1 (April 5, 2018): 5079–82. http://dx.doi.org/10.16965/ijar.2018.115.

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Lawrence, Christine, Sheldon T. Brown, and Lawrence F. Freundlich. "Peripheral blood smear bacillemia." American Journal of Medicine 85, no. 1 (July 1988): 111–13. http://dx.doi.org/10.1016/0002-9343(88)90514-1.

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Kapil, Menka, Rateesh Sareen, and GN Gupta. "Peripheral blood smear pathologist tool." Hematology & Transfusion International Journal 8, no. 1 (February 24, 2020): 10–11. http://dx.doi.org/10.15406/htij.2020.08.00214.

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Microscopic evaluation of a peripheral blood smear is one of the most valuable test for the diagnosis and differential diagnosis of disease inclusive of clinical history and physical examination. Despite advances in haematology automation and application of molecular techniquesits diagnostic relevance is enormous.
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Essgir, Prem Kumar, and Hemalatha Anantharamaiah. "A Study of Rapid Leishman Stain on Peripheral Blood Smear." Annals of Pathology and Laboratory Medicine 6, no. 4 (April 29, 2019): A201–204. http://dx.doi.org/10.21276/apalm.2323.

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Avci, E., B. Unver Koluman, R. Nar, H. Aybek, and S. Demİr. "Comparison of automatic peripheral blood smear to manual blood smear technique." Clinica Chimica Acta 493 (June 2019): S388—S389. http://dx.doi.org/10.1016/j.cca.2019.03.829.

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Tefferi, Ayalew, and Michelle A. Elliott. "Schistocytes on the Peripheral Blood Smear." Mayo Clinic Proceedings 79, no. 6 (June 2004): 809. http://dx.doi.org/10.4065/79.6.809.

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Edelman, M., and J. Mckitrick. "Histoplasma capsulatumin a Peripheral-Blood Smear." New England Journal of Medicine 342, no. 1 (January 6, 2000): 28. http://dx.doi.org/10.1056/nejm200001063420105.

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Beaudoin, Sandra, Anne Lanevschi, Marilyn Dunn, and Michel Desnoyers. "Peripheral Blood Smear from a Dog." Veterinary Clinical Pathology 31, no. 1 (March 2002): 33–35. http://dx.doi.org/10.1111/j.1939-165x.2002.tb00275.x.

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Mundi, Irneet, Ritu Pankaj, Rajeev Bedi, Anita Sharma, and Meenakshi Malhotra. "Peripheral blood smear: beyond routine morphology." Tropical Doctor 47, no. 4 (April 18, 2017): 382–84. http://dx.doi.org/10.1177/0049475517701877.

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Tefferi, Ayalew, and Michelle A. Elliott. "Schistocytes on the Peripheral Blood Smear." Mayo Clinic Proceedings 79, no. 6 (June 2004): 809. http://dx.doi.org/10.1016/s0025-6196(11)62635-9.

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Dissertations / Theses on the topic "Peripheral blood smear"

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Leclair, Susan J. "The Correlation Between the Levels of Education of Clinical Laboratory Personnel and the Accuracy of Peripheral Blood Smear Results." ScholarWorks, 2001. https://scholarworks.waldenu.edu/dissertations/1393.

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This study correlated the performance accuracy of peripheral blood differentials evaluated by clinical laboratory scientists and clinical laboratory technicians. Fifty-one senior-year students from four clinical laboratory science baccalaureate programs and 37 second-year students from five clinical laboratory technician programs were given 10 peripheral blood differentials to perform. Results were compared to the values assigned by the Rajamaki method of proficiency testing. There was a significant discrepancy in the levels of accuracy between the two cohorts, suggesting that the results of peripheral blood differentials performed by clinical laboratory technicians is suspect. Facilities wishing to maintain or improve the quality of laboratory services should consider allowing only baccalaureate level clinical laboratory scientists to perform peripheral blood differentials.
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Andersson, Vidar. "Evaluation of CellaVision DM1200 Vet and its ability to differentiate feline leukocytes compared to manual differential count and Advia 2120." Thesis, Uppsala universitet, Institutionen för kvinnors och barns hälsa, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295630.

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Leukocyte differential count in peripheral blood smear has, ever since the method was developed more than 100 years ago, been one of the most frequently used diagnostics tool in the routine hematology laboratory. The manual differential count of leukocytes using a microscope is still the standard method in most small and medium sized laboratories. Even though the method does not require any expensive instruments it comes at a high cost due to it being labor intensive and time consuming. In recent years the rapid technical advancements has led to the development of automatic or semi-automatic methods in which the leukocytes are differentiated. In this study a method comparison was made between manual leukocyte differential counts, CellaVision DM1200 Vet and Advia 2120 when analyzing 106 fresh, feline blood samples. The general agreement between results was good, especially for the most common leukocytes, such as neutrophils and lymphocytes. Results for eosinophils and monocytes had moderate agreement. The confidence intervals were generally wider when CellaVision DM1200 Vet was compared with Advia 2120, than when CellaVision DM1200 Vet was compared to the manual differential count. Despite the fact that Advia 2120 and CellaVision DM1200 Vet are both faster and often show comparable results to the manual differential count, the light microscopy will remain the gold standard for difficult samples, where there is suspicion of inflammation (band neutrophils), intracellular microorganisms, reactive lymphocytes or if the sample contains a high degree of smudge cells or artifacts.
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Pavlík, Jan. "Analýza cytologických snímků." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219507.

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This master’s thesis is focused on automating the process of differential leukocyte count in peripherial blood using image processing. It deals with the design of the processing of digital images - from scanning and image preprocessing, segmentation nucleus and cytoplasm, feature selection and classifier, including testing on a set of images that were scanned in the context of this work. This work introduces used segmentation methods and classification procedures which separate nucleus and the cytoplasm of leukocytes. A statistical analysis is performed on the basis of these structures. Following adequate statistical parameters, a set of features has been chosen. This data then go through a classification process realized by three artificial neural networks. Overall were classified 5 types of leukocytes: neutropfiles, lymphocytes, monocytes, eosinophiles and basophiles. The sensitivity and specificity of the classification made for 4 out of 5 leukocyte types (neutropfiles, lymphocytes, monocytes, eosinophiles) is higher than 90 %. Sensitivity of classiffication basophiles was evaluated at 75 % and specificity at 67 %. The total ability of classification has been tested on 111 leukocytes and was approximately 91% successful. All algorithms were created in the MATLAB program.
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Books on the topic "Peripheral blood smear"

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Albert, Tyler J., and Erik R. Swenson. The blood cells and blood count. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0265.

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Blood is a dynamic fluid consisting of cellular and plasma components undergoing constant regeneration and recycling. Like most physiological systems, the concentrations of these components are tightly regulated within narrow limits under normal conditions. In the critically-ill population, however, haematological abnormalities frequently occur and are largely due to non-haematological single- or multiple-organ pathology. Haematopoiesis originates from the pluripotent stem cell, which undergoes replication, proliferation, and differentiation, giving rise to cells of the erythroid, myeloid, and lymphoid series, as well as megakaryocytes, the precursors to platelets. The haemostatic system is responsible for maintaining blood fluidity and, at the same time, prevents blood loss by initiating rapid, localized, and appropriate blood clotting at sites of vascular damage. This system is complex, comprising both cellular and plasma elements, i.e. platelets, coagulation and fibrinolytic cascades, the natural intrinsic and extrinsic pathways of anticoagulation, and the vascular endothelium. A rapid, reliable, and inexpensive method of examining haematological disorders is the peripheral blood smear, which allows practitioners to assess the functional status of the bone marrow during cytopenic states. Red blood cells, which are primarily concerned with oxygen and carbon dioxide transport, have a normal lifespan of only 120 days and require constant erythropoiesis. White blood cells represent a summation of several circulating cell types, each deriving from the hematopoietic stem cell, together forming the critical components of both the innate and adaptive immune systems. Platelets are integral to haemostasis, and also aid our inflammatory and immune responses, help maintain vascular integrity, and contribute to wound healing.
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Cowell, Rick L., Ronald D. Tyler, Amy C. Valenciano, and Theresa E. Rizzi. Atlas of Canine and Feline Peripheral Blood Smears. Elsevier - Health Sciences Division, 2013.

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Atlas of Canine and Feline Peripheral Blood Smears. Elsevier, 2014. http://dx.doi.org/10.1016/c2009-0-33311-5.

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Book chapters on the topic "Peripheral blood smear"

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Sheeba, Feminna, Robinson Thamburaj, Joy John Mammen, and Atulya K. Nagar. "Detection of Plasmodium Falciparum in Peripheral Blood Smear Images." In Advances in Intelligent Systems and Computing, 289–98. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-1041-2_25.

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Mundhra, Dheeraj, Bharath Cheluvaraju, Jaiprasad Rampure, and Tathagato Rai Dastidar. "Analyzing Microscopic Images of Peripheral Blood Smear Using Deep Learning." In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 178–85. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67558-9_21.

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Al-qudah, Rabiah, and Ching Y. Suen. "A Survey on Peripheral Blood Smear Analysis Using Deep Learning." In Pattern Recognition and Artificial Intelligence, 725–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_63.

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Kulasekaran, S., Feminna Sheeba, Joy John Mammen, B. Saivigneshu, and S. Mohankumar. "Morphology Based Detection of Abnormal Red Blood Cells in Peripheral Blood Smear Images." In 7th WACBE World Congress on Bioengineering 2015, 57–60. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19452-3_16.

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Rufai, A. U., and J. O. Enikuomehin. "Deep Learning for Automatic Parasitemia Identification from Peripheral Blood Smear Images." In Sustainable Education and Development, 480–89. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68836-3_41.

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Sheeba, Feminna, Robinson Thamburaj, Joy John Mammen, Mohan Kumar, and Vansant Rangslang. "Convex Hull Based Detection of Overlapping Red Blood Cells in Peripheral Blood Smear Images." In 7th WACBE World Congress on Bioengineering 2015, 51–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19452-3_14.

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Sheeba, Feminna, Robinson Thamburaj, Joy John Mammen, and Atulya K. Nagar. "Splitting of Overlapping Cells in Peripheral Blood Smear Images by Concavity Analysis." In Lecture Notes in Computer Science, 238–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07148-0_21.

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Khan, Mohammad Badhruddouza, Tobibul Islam, Mohiuddin Ahmad, Rahat Shahrior, and Zannatun Naiem Riya. "A CNN Based Deep Learning Approach for Leukocytes Classification in Peripheral Blood from Microscopic Smear Blood Images." In Algorithms for Intelligent Systems, 67–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0586-4_6.

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Zhang, Songtao, Qingwen Ni, Bing Li, Shan Jiang, Wenyu Cai, Hang Chen, and Lin Luo. "Corruption-Robust Enhancement of Deep Neural Networks for Classification of Peripheral Blood Smear Images." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 372–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59722-1_36.

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Suganya Devi, K., G. Arutperumjothi, and P. Srinivasan. "Diagnosis Evaluation and Interpretation of Qualitative Abnormalities in Peripheral Blood Smear Images—A Review." In Health Informatics: A Computational Perspective in Healthcare, 341–65. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9735-0_17.

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Conference papers on the topic "Peripheral blood smear"

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Sheeba, Feminna, Mary Thomas T. Hannah, and Joy John Mammen. "Segmentation and reversible watermarking of peripheral blood smear images." In 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). IEEE, 2010. http://dx.doi.org/10.1109/bicta.2010.5645065.

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Makkapati, Vishnu V., and Raghuveer M. Rao. "Segmentation of malaria parasites in peripheral blood smear images." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4959845.

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Fitri, Zilvanhisna Emka, I. Ketut Eddy Purnama, Eko Pramunanto, and Mauridhi Hery Pumomo. "A comparison of platelets classification from digitalization microscopic peripheral blood smear." In 2017 International Seminar on Intelligent Technology and its Applications (ISITIA). IEEE, 2017. http://dx.doi.org/10.1109/isitia.2017.8124109.

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Biji, G., and S. Hariharan. "An efficient peripheral blood smear image analysis technique for Leukemia detection." In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2017. http://dx.doi.org/10.1109/i-smac.2017.8058350.

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Sheeba, Feminna, Robinson Thamburaj, Atulya K. Nagar, and Joy John Mammen. "Segmentation of Peripheral Blood Smear Images Using Tissue-Like P Systems." In 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). IEEE, 2011. http://dx.doi.org/10.1109/bic-ta.2011.72.

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Seyed Forootan, N., J. P. Dugan, and S. Raju. "Disseminated Histoplasmosis Visualized on Peripheral Blood Smear with Subsequent Mycotic Aneurysm." In American Thoracic Society 2021 International Conference, May 14-19, 2021 - San Diego, CA. American Thoracic Society, 2021. http://dx.doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3998.

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Mazalan, Siti Madihah, Nasrul Humaimi Mahmood, and Mohd Azhar Abdul Razak. "Automated Red Blood Cells Counting in Peripheral Blood Smear Image Using Circular Hough Transform." In 2013 1st International Conference on Artificial Intelligence, Modelling & Simulation (AIMS). IEEE, 2013. http://dx.doi.org/10.1109/aims.2013.59.

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Soltanzadeh, Ramin, and Hossein Rabbani. "Classification of three types of red blood cells in peripheral blood smear based on morphology." In 2010 10th International Conference on Signal Processing (ICSP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icosp.2010.5655754.

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Xiong, Wei, S. H. Ong, Christina Kang, Joo Hwee Lim, Jiang Liu, Daniel Racoceanu, and Kelvin Foong. "Cell Clumping Quantification and Automatic Area Classification in Peripheral Blood Smear Images." In 2009 2nd International Conference on Biomedical Engineering and Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bmei.2009.5301645.

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Hamghalam, Mohammad, and Ahmad Ayatollahi. "Automatic Counting of Leukocytes in Giemsa-Stained Images of Peripheral Blood Smear." In 2009 International Conference on Digital Image Processing, ICDIP. IEEE, 2009. http://dx.doi.org/10.1109/icdip.2009.9.

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