An Automated Method for Differential Blood Counting Using Microscope Color Image of Isolated WBC

2010 ◽  
Vol 1 (4) ◽  
pp. 35-48
Author(s):  
Anant R. Koppar ◽  
Venugopalachar Sridhar
Keyword(s):  
Blood Cells ◽  
Human Error ◽  
Color Image ◽  
White Blood Cells ◽  
Healthcare Delivery ◽  
Cost Effective ◽  
Turnaround Time ◽  
Differential Count ◽  
Flow Process ◽  
Automated Method

Healthcare Delivery Systems are becoming overloaded in developed and developing countries. It is imperative that more efficient and cost effective processes be employed by innovative applications of technology in the delivery system. One such process in Haematology that needs attention is “Generation of report on the Differential Count of Blood”. Most rural centers in India still employ traditional, manual processes to identify and count White Blood Cells under a microscope. This traditional method of manually counting the white blood cells is prone to human error and time consuming. Medical Imaging with innovative application of algorithms can be used for recognizing and analyzing the images from blood smears to provide an efficient alternative for differential counting and reporting. In this regard, the objective of this paper is to provide a simple and pragmatic software system built on innovative yet simple imaging algorithms for achieving better efficiency and accuracy of results. The resulting work-flow process has enabled truly practical tele-pathology by enabling e-collaboration between lesser skilled technicians and more skilled experts, which cuts down the total turnaround time for differential count reporting from days to minutes. The system can be extended to detect malarial parasites in blood and also cancerous cells.

An Automated Method for Differential Blood Counting Using Microscope Color Image of Isolated WBC

2012 ◽  
pp. 219-232
Author(s):  
Anant R. Koppar ◽  
Venugopalachar Sridhar
Keyword(s):  
Blood Cells ◽  
Human Error ◽  
Color Image ◽  
White Blood Cells ◽  
Healthcare Delivery ◽  
Cost Effective ◽  
Turnaround Time ◽  
Differential Count ◽  
Flow Process ◽  
Accuracy Of Results

Healthcare Delivery Systems are becoming overloaded in developed and developing countries. It is imperative that more efficient and cost effective processes be employed by innovative applications of technology in the delivery system. One such process in Haematology that needs attention is “Generation of report on the Differential Count of Blood”. Most rural centers in India still employ traditional, manual processes to identify and count White Blood Cells under a microscope. This traditional method of manually counting the white blood cells is prone to human error and time consuming. Medical Imaging with innovative application of algorithms can be used for recognizing and analyzing the images from blood smears to provide an efficient alternative for differential counting and reporting. In this regard, the objective of this paper is to provide a simple and pragmatic software system built on innovative yet simple imaging algorithms for achieving better efficiency and accuracy of results. The resulting work-flow process has enabled truly practical tele-pathology by enabling e-collaboration between lesser skilled technicians and more skilled experts, which cuts down the total turnaround time for differential count reporting from days to minutes. The system can be extended to detect malarial parasites in blood and also cancerous cells.

Malaria Parasite Detection

2012 ◽  
pp. 289-300
Author(s):  
Anant R. Koppar ◽  
Venugopalachar Sridhar
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Color Image ◽  
Healthcare Delivery ◽  
Diagnostic System ◽  
Cost Effective ◽  
Peripheral Blood Smear ◽  
Turnaround Time ◽  
Standard Test ◽  
Flow Process

Healthcare Delivery Systems are becoming overloaded in developing countries like India and China. It is imperative that more efficient and cost effective processes are employed. One such requirement is the automatic detection of malaria parasites in stained blood smears. Malaria is a mosquito-borne infectious disease. Each year Malaria kills between one and three million people. The most conventional and gold standard test for the confirmation of the Malarial diagnosis is the peripheral blood smear examination. The paper investigates and develops an automated malaria diagnostic system based on the color image processing using Hue, Saturation & Intensity (HSI) model. The algorithm is designed to identify only parasites inside red blood cells (erythrocytes) to avoid false positive results. The work-flow process has enabled practical tele-pathology by allowing e-collaboration between lesser skilled technicians in rural areas and experts in urban areas, cutting down the total turnaround time for diagnosing malaria from days to minutes.

Malaria Parasite Detection

2011 ◽  
Vol 2 (2) ◽  
pp. 68-81
Author(s):  
Anant R. Koppar ◽  
Venugopalachar Sridhar
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Color Image ◽  
Healthcare Delivery ◽  
Diagnostic System ◽  
Cost Effective ◽  
Peripheral Blood Smear ◽  
Turnaround Time ◽  
Standard Test ◽  
Flow Process

Healthcare Delivery Systems are becoming overloaded in developing countries like India and China. It is imperative that more efficient and cost effective processes are employed. One such requirement is the automatic detection of malaria parasites in stained blood smears. Malaria is a mosquito-borne infectious disease. Each year Malaria kills between one and three million people. The most conventional and gold standard test for the confirmation of the Malarial diagnosis is the peripheral blood smear examination. The paper investigates and develops an automated malaria diagnostic system based on the color image processing using Hue, Saturation & Intensity (HSI) model. The algorithm is designed to identify only parasites inside red blood cells (erythrocytes) to avoid false positive results. The work-flow process has enabled practical tele-pathology by allowing e-collaboration between lesser skilled technicians in rural areas and experts in urban areas, cutting down the total turnaround time for diagnosing malaria from days to minutes.

Comparison of five automated urine sediment analyzers with manual microscopy for accurate identification of urine sediment

2019 ◽  
Vol 57 (11) ◽  
pp. 1744-1753 ◽  
Author(s):  
Jooyoung Cho ◽  
Kyeong Jin Oh ◽  
Beom Chan Jeon ◽  
Sang-Guk Lee ◽  
Jeong-Ho Kim
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Turnaround Time ◽  
Accurate Identification ◽  
Urine Sediment ◽  
Detection Rates ◽  
Manual Processing ◽  
The Common ◽  
Distinct Features ◽  
Clinical Algorithms

Abstract Background While the introduction of automated urine analyzers is expected to reduce the labor involved, turnaround time and potential assay variations, microscopic examination remains the “gold standard” for the analysis of urine sediments. In this study, we evaluated the analytical and diagnostic performance of five recently introduced automated urine sediment analyzers. Methods A total of 1016 samples were examined using five automated urine sediment analyzers and manual microscopy. Concordance of results from each automated analyzer and manual microscopy were evaluated. In addition, image and microscopic review rates of each system were investigated. Results The proportional bias for red blood cells (RBCs), white blood cells (WBCs) and squamous epithelial cells in the automated urine sediment analyzers were within ±20% of values obtained using the manual microscope, except in the cases of RBCs and WBCs analyzed using URiSCAN PlusScope and Iris iQ200SPRINT, respectively. The sensitivities of Roche Cobas® u 701 and Siemens UAS800 for pathologic casts (73.6% and 81.1%, respectively) and crystals (62.2% and 49.5%, respectively) were high, along with high image review rates (24.6% and 25.2%, respectively). The detection rates for crystals, casts and review rates can be changed for the Sysmex UF-5000 platform according to cut-off thresholds. Conclusions Each automated urine sediment analyzer has certain distinct features, in addition to the common advantages of reducing the burden of manual processing. Therefore, laboratory physicians are encouraged to understand these features, and to utilize each system in appropriate ways, considering clinical algorithms and laboratory workflow.

scholarly journals High-resolution image analysis.

1986 ◽  
Vol 34 (1) ◽  
pp. 67-74 ◽  
Author(s):  
K Preston
Keyword(s):  
High Resolution ◽  
Blood Cells ◽  
Research Effort ◽  
White Blood Cells ◽  
Differential Count ◽  
Computer Assisted ◽  
Automated Microscopy ◽  
Human Lymph Node ◽  
Cellular Dna

In many departments of cytology, cytogenetics, hematology, and pathology, research projects using high-resolution computerized microscopy are now being mounted for computation of morphometric measurements on various structural components, as well as for determination of cellular DNA content. The majority of these measurements are made in a partially automated, computer-assisted mode, wherein there is strong interaction between the user and the computerized microscope. At the same time, full automation has been accomplished for both sample preparation and sample examination for clinical determination of the white blood cell differential count. At the time of writing, approximately 1,000 robot differential counting microscopes are in the field, analyzing images of human white blood cells, red blood cells, and platelets at the overall rate of about 100,000 slides per day. This mammoth through-put represents a major accomplishment in the application of machine vision to automated microscopy for hematology. In other areas of automated high-resolution microscopy, such as cytology and cytogenetics, no commercial instruments are available (although a few metaphase-finding machines are available and other new machines have been announced during the past year). This is a disappointing product, considering the nearly half century of research effort in these areas. This paper provides examples of the state of the art in automation of cell analysis for blood smears, cervical smears, and chromosome preparations. Also treated are new developments in multi-resolution automated microscopy, where images are now being generated and analyzed by a single machine over a range of 64:1 magnification and from 10,000 X 20,000 to 500 X 500 in total picture elements (pixels). Examples of images of human lymph node and liver tissue are presented. Semi-automated systems are not treated, although there is mention of recent research in the automation of tissue analysis.

scholarly journals Analysis of White Blood Cell Count and Its Differential Count in Gingival and Periodontal Conditions Associated with Bleeding Gingiva

2021 ◽  
pp. 911-917
Author(s):  
L. Sai Charan ◽  
Palati Sinduja ◽  
R. Priyadarshini
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Clinical Pathology ◽  
Differential Count ◽  
Hormonal Changes ◽  
Vitamin K Deficiency ◽  
Medical Problems ◽  
Gingival Bleeding ◽  
K Deficiency ◽  
Wbc Count

Background: Bleeding gingiva is caused primarily due to the accumulation of plaque and calculus which eventually leads to gingivitis or periodontitis. Other causes of bleeding gingiva can be due to improper flossing, over brushing of the teeth and gingiva, hormonal changes due to pregnancy, ill-fitting dentures and any other dental appliances impinging the gingiva. The bleeding gingiva can also indicate serious health problems like leukemia, scurvy, idiopathic thrombocytopenic purpura, vitamin k deficiency and any bleeding disorder. Persistent gingival bleeding is a sign of serious medical problems like leukemia and platelet disorders. Leukemia is a group of cancer where there is an increased number of immature or abnormal white blood cells. In this study, the WBC and their differential count is analyzed in patients with bleeding gingiva to check the possibilities for the patient to get cancer. Aim: To measure and observe the WBC count and its differentials by testing the blood from patients with bleeding gingiva. Materials and Methods: The study was conducted in the clinical pathology lab at Saveetha Dental College and Hospitals, Chennai. 100 subjects were subjected to the study. Subjects with chief complaint of bleeding gingiva, without systemic diseases like diabetes, hypertension, and patients with the age of above 10 were included in the study. Results and Conclusion: This study was conducted to analyze the WBC count and differential count among the patients with bleeding gingiva. No significant correlation was found between bleeding gingiva and white blood cells & their differential count in this study.

Differential Count of White Blood Cells

2014 ◽  
pp. 35-35
Author(s):  
M Chandrasekar ◽  
Nitesh Mishra
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Differential Count

scholarly journals Normal range of white blood cells and differential count of Sudanese in Khartoum state

2018 ◽  
Vol 5 (4) ◽  
pp. 784 ◽  
Author(s):  
Elmutaz H. Taha ◽  
Mohammed Elshiekh ◽  
Abdelrahim Alborai ◽  
Elnagi Y. Hajo ◽  
Abdelmohisen Hussein ◽  
...  
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Differential Count ◽  
Reference Ranges ◽  
African Countries ◽  
The Mean ◽  
Automated Hematology Analyzer ◽  
Wbc Count ◽  
Age Range

Background: The normal physiological range for white blood cells and differential count are essential for diagnosis, treatment, follow up and screening. This study aimed at establishing the reference ranges of WBCs and differential count in Sudanese people.Methods: The present study included 444 healthy adult Sudanese from both sexes with age range of 20 – 60 years. Blood samples were obtained from brachial veins and drawn in EDTA tubes. WBCs and differential count were analyzed using Sysmex KX-21 automated hematology analyzer. Full clinical examination was performed, weight and height were measured, and BMI was calculated.Results: The mean WBC count was 5.1±1.5×103/ µl with a range of 3.6 ×103/µl to 6.6 ×103/µl. The mean WBCs count for males and females were 4.969×103/µl and 5.138×103/µl respectively. Neutrophils count was 2.430×103/µl (47%) and mean for lymphocyte count was 2.116×103/µl (41.1%).Conclusions: WBCs count was directly proportional to BMI. The WBCs count of Sudanese people was lower than that of Caucasians and similar to reports from other African countries.

An Evaluation of the Performance of Sysmex XE-2100 in Enumerating Nucleated Red Cells in Peripheral Blood

2007 ◽  
Vol 131 (7) ◽  
pp. 1077-1083
Author(s):  
Gene Gulati ◽  
Eric Behling ◽  
William Kocher ◽  
Roland Schwarting
Keyword(s):  
Peripheral Blood ◽  
Blood Cells ◽  
Blood Smear ◽  
White Blood Cells ◽  
Relative Number ◽  
Nucleated Red Blood Cells ◽  
Automated Method ◽  
Blood Smear Examination ◽  
The Mean ◽  
Automated Methods

Abstract Context.—Automated methods of enumerating nucleated red blood cells (NRBCs) in blood are gaining acceptance in many laboratories. Objective.—To evaluate the performance of Sysmex XE-2100 in enumerating NRBCs in peripheral blood. Design.—Automated relative number of NRBCs per 100 white blood cells (NRBC%) results for a total of 460 specimens run on the XE-2100 were compared with manual NRBC% results obtained by performing a 100-cell differential on a Wright-stained smear prepared from each specimen. To assess within-day reproducibility, 64 specimens were rerun on the XE-2100, and a second 100-cell differential was performed on the original blood smear. Excel software was used for data analysis. Results.—Regression analysis of automated NRBC% versus manual NRBC% yielded a correlation coefficient of 0.9712. No NRBCs were seen in the blood smear on 35 (15.1%) of 232 specimens with automated NRBC% of 0.1 to 1.9. The XE-2100 generated an NRBC% of 0.0 on 5 (6.8%) of 74 specimens, revealing 1 NRBC per 100 or more white blood cells by blood smear examination. The mean percent difference between duplicate automated results was 16.7 compared with 78.1 for the duplicate manual results. There were 9 instances in which the XE-2100 either did not detect the presence of more than 8 NRBCs per 100 white blood cells or generated an automated NRBC% of 18.1 or 18.8 when the smear revealed none. All of these were, however, flagged for smear review. Conclusions.—Overall correlation between the automated and manual results was excellent. The automated method revealed better precision than the manual method. The number of specimens with false automated results was very small, and all were flagged for verification by a smear review.

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