A self-adaptive approach for white blood cell classification towards point-of-care testing

2021 ◽  
pp. 107709
Author(s):  
Na Dong ◽  
Meng-die Zhai ◽  
Jian-fang Chang ◽  
Chun-ho Wu
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Point Of Care ◽  
Point Of Care Testing ◽  
Cell Classification ◽  
Adaptive Approach ◽  
Self Adaptive

Impact of point-of-care testing for white blood cell count on triage of patients with infection in the remote Northern Territory of Australia

Pathology ◽  
2019 ◽  
Vol 51 (5) ◽  
pp. 512-517 ◽  
Author(s):  
Brooke Spaeth ◽  
Mark Shephard ◽  
Rana Kokcinar ◽  
Lauren Duckworth ◽  
Rodney Omond
Keyword(s):  
Blood Cell ◽  
Cell Count ◽  
White Blood Cell Count ◽  
White Blood Cell ◽  
Point Of Care ◽  
Northern Territory ◽  
Point Of Care Testing ◽  
Blood Cell Count

White blood cell counting at point‐of‐care testing: A review

2020 ◽  
Vol 41 (16-17) ◽  
pp. 1450-1468 ◽  
Author(s):  
Jianke Luo ◽  
Chunmei Chen ◽  
Qing Li
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Point Of Care ◽  
Cell Counting ◽  
Point Of Care Testing ◽  
Blood Cell Counting ◽  
White Blood Cell Counting

scholarly journals Clinical training and validation of the LeukoScope: a low-cost, point-of-care device to perform white blood cell and neutrophil counts

RSC Advances ◽  
2019 ◽  
Vol 9 (47) ◽  
pp. 27324-27333
Author(s):  
Catherine E. Majors ◽  
Michal E. Pawlowski ◽  
Daniel C. Burke ◽  
Tomasz S. Tkaczyk ◽  
Alyssa Rieber ◽  
...  
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Clinical Training ◽  
Point Of Care ◽  
Low Cost

We present a novel, point-of-care method to perform WBC and neutrophil counts with a drop of blood and portable reader.

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.

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