Red blood cell and white blood cell classification using double thresholding and BLOB analysis

2016 ◽  
Author(s):  
Jullend Gatc ◽  
Febri Maspiyanti
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Red Blood Cell ◽  
Cell Classification ◽  
Blob Analysis

scholarly journals Reference intervals for hematology test parameters from apparently healthy individuals in southwest Ethiopia

2018 ◽  
Vol 6 ◽  
pp. 205031211880762 ◽  
Author(s):  
Lealem Gedefaw Bimerew ◽  
Tesfaye Demie ◽  
Kaleab Eskinder ◽  
Aklilu Getachew ◽  
Shiferaw Bekele ◽  
...  
Keyword(s):  
Platelet Count ◽  
Blood Cell ◽  
White Blood Cell ◽  
Red Blood Cell ◽  
Hematological Parameters ◽  
Age Groups ◽  
Reference Interval ◽  
Reference Intervals ◽  
Southwest Ethiopia ◽  
Apparently Healthy

Background: Clinical laboratory reference intervals are an important tool to identify abnormal laboratory test results. The generating of hematological parameters reference intervals for local population is very crucial to improve quality of health care, which otherwise may lead to unnecessary expenditure or denying care for the needy. There are no well-established reference intervals for hematological parameters in southwest Ethiopia. Objective: To generate hematological parameters reference intervals for apparently healthy individuals in southwest Ethiopia. Methods: A community-based cross-sectional study was conducted involving 883 individuals from March to May 2017. Four milliliter of blood sample was collected and transported to Jimma University Medical Center Laboratory for hematological analysis and screening tests. A hematological parameters were measured by Sysmex XS-500i hematology analyzer (Sysmex Corporation Kobe, Japan). The data were analyzed by SPSS version 20 statistical software. The non-parametric independent Kruskal–Wallis test and Wilcoxon rank-sum test (Mann–Whitney U test) were used to compare the parameters between age groups and genders. The 97.5 percentile and 2.5 percentile were the upper and lower reference limit for the population. Results: The reference interval of red blood cell, white blood cell, and platelet count in children were 4.99 × 1012/L (4.26–5.99 × 1012/L), 7.04 × 109/L (4.00–11.67 × 109/L), and 324.00 × 109/L (188.00–463.50 × 109/L), respectively. The reference interval of red blood cell, white blood cell, and platelet count in adults was 5.19 × 1012/L (4.08–6.33 × 1012/L), 6.35 × 109/L (3.28–11.22 × 109/L), and 282.00 × 109/L (172.50–415.25 × 109/L), respectively. The reference interval of red blood cell, white blood cell, and platelet count in geriatrics were 5.02 × 1012/L (4.21–5.87 × 1012/L), 6.21 × 109/L (3.33–10.03 × 109/L), and 265.50 × 109/L (165.53–418.80 × 109/L), respectively. Most of the hematological parameters showed significant differences across all age groups. Conclusion: Most of the hematological parameters in this study showed differences from similar studies done in the country. This study provided population-specific hematological reference interval for southwest Ethiopians. Reference intervals should also be established in the other regions of the country.

scholarly journals Red blood cell classification in lensless single random phase encoding using convolutional neural networks

2020 ◽  
Vol 28 (22) ◽  
pp. 33504 ◽  
Author(s):  
Timothy O’Connor ◽  
Christopher Hawxhurst ◽  
Leslie M. Shor ◽  
Bahram Javidi
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Convolutional Neural Networks ◽  
Red Blood Cell ◽  
Random Phase ◽  
Cell Classification ◽  
Phase Encoding ◽  
Random Phase Encoding

Donor white blood cell survival and cytokine profiles following red blood cell transfusion in Australian major trauma patients

2018 ◽  
Vol 103 ◽  
pp. 229-234 ◽  
Author(s):  
Rena Hirani ◽  
Melinda M. Dean ◽  
Zsolt J. Balogh ◽  
Natalie J. Lott ◽  
Julie Seggie ◽  
...  
Keyword(s):  
Blood Cell ◽  
Cell Survival ◽  
White Blood Cell ◽  
Red Blood Cell ◽  
Major Trauma ◽  
Red Blood Cell Transfusion ◽  
Trauma Patients ◽  
Cytokine Profiles

scholarly journals Red blood cell classification on thin blood smear images for malaria diagnosis

2020 ◽  
Vol 1444 ◽  
pp. 012036 ◽  
Author(s):  
Budi Sunarko ◽  
Djuniadi ◽  
Murk Bottema ◽  
Nur Iksan ◽  
Khakim A N Hudaya ◽  
...  
Keyword(s):  
Blood Cell ◽  
Red Blood Cell ◽  
Blood Smear ◽  
Malaria Diagnosis ◽  
Thin Blood Smear ◽  
Cell Classification

scholarly journals Application of the method of multiple thresholding to white blood cell classification

1988 ◽  
Vol 18 (2) ◽  
pp. 65-74 ◽  
Author(s):  
E.S. Gelsema ◽  
H.F. Bao ◽  
A.W.M. Smeulders ◽  
H.C. den Harink
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Cell Classification

scholarly journals A Neural-Network-Based Approach to White Blood Cell Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mu-Chun Su ◽  
Chun-Yen Cheng ◽  
Pa-Chun Wang
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Classification System ◽  
Blood Cells ◽  
White Blood Cells ◽  
Recognition Rate ◽  
Color Space ◽  
Segmentation Algorithm ◽  
Cell Classification ◽  
Morphological Process

This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.

Performance of automated digital cell imaging analyzer Sysmex DI-60

2017 ◽  
Vol 56 (1) ◽  
Author(s):  
Hyeong Nyeon Kim ◽  
Mina Hur ◽  
Hanah Kim ◽  
Seung Wan Kim ◽  
Hee-Won Moon ◽  
...  
Keyword(s):  
Platelet Count ◽  
Blood Cell ◽  
White Blood Cell ◽  
Red Blood Cell ◽  
Cell Imaging ◽  
Platelet Counts ◽  
Manual Count ◽  
Manual Counting ◽  
Highly Correlated

AbstractBackground:The Sysmex DI-60 system (DI-60, Sysmex, Kobe, Japan) is a new automated digital cell imaging analyzer. We explored the performance of DI-60 in comparison with Sysmex XN analyzer (XN, Sysmex) and manual count.Methods:In a total of 276 samples (176 abnormal and 100 normal samples), white blood cell (WBC) differentials, red blood cell (RBC) classification and platelet (PLT) estimation by DI-60 were compared with the results by XN and/or manual count. RBC morphology between pre-classification and verification was compared according to the ICSH grading criteria. The manual count was performed according to the Clinical and Laboratory Standards Institute guidelines (H20-A2).Results:The overall concordance between DI-60 and manual count for WBCs was 86.0%. The agreement between DI-60 pre-classification and verification was excellent (weighted κ=0.963) for WBC five-part differentials. The correlation with manual count was very strong for neutrophils (r=0.955), lymphocytes (r=0.871), immature granulocytes (r=0.820), and blasts (r=0.879). RBC grading showed notable differences between DI-60 and manual counting on the basis of the ICSH grading criteria. Platelet count by DI-60 highly correlated with that by XN (r=0.945). However, DI-60 underestimated platelet counts in samples with marked thrombocytosis.Conclusions:The performance of DI-60 for WBC differential, RBC classification, and platelet estimation seems to be acceptable even in abnormal samples with improvement after verification. DI-60 would help optimize the workflow in hematology laboratory with reduced manual workload.

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