Automated Detection and Classification of Schistocytes by a Novel Red Blood Cell Module Using Digital Imaging/Microscopy

2015 ◽  
Vol 4 (2) ◽  
pp. 184-186 ◽  
Author(s):  
Angelique Egele ◽  
Warry van Gelder ◽  
Jurgen Riedl
Keyword(s):  
Blood Cell ◽  
Red Blood Cell ◽  
Digital Imaging ◽  
Automated Detection ◽  
Digital Imaging Microscopy ◽  
Cell Module

Classification of several morphological red blood cell abnormalities by DM96 digital imaging

2016 ◽  
Vol 38 (5) ◽  
pp. e98-e101 ◽  
Author(s):  
A. Egelé ◽  
K. Stouten ◽  
L. van der Heul-Nieuwenhuijsen ◽  
L. de Bruin ◽  
R. Teuns ◽  
...  
Keyword(s):  
Blood Cell ◽  
Red Blood Cell ◽  
Digital Imaging

scholarly journals Classification of red blood cell shapes in flow using outlier tolerant machine learning

2018 ◽  
Vol 14 (6) ◽  
pp. e1006278 ◽  
Author(s):  
Alexander Kihm ◽  
Lars Kaestner ◽  
Christian Wagner ◽  
Stephan Quint
Keyword(s):  
Machine Learning ◽  
Blood Cell ◽  
Red Blood Cell ◽  
Cell Shapes

scholarly journals Classification of β-Thalassemia Carriers From Red Blood Cell Indices Using Ensemble Classifier

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45528-45538
Author(s):  
Saima Sadiq ◽  
Muhammad Usman Khalid ◽  
Mui-Zzud-Din ◽  
Saleem Ullah ◽  
Waqar Aslam ◽  
...  
Keyword(s):  
Blood Cell ◽  
Red Blood Cell ◽  
Ensemble Classifier ◽  
Red Blood Cell Indices

J0203-3-4 Electrical Classification of Single Red Blood Cell Deformability in High Shear Micro-Channel Flows (2nd report)

2009 ◽  
Vol 2009.6 (0) ◽  
pp. 199-200
Author(s):  
Kosuke NISHITANI ◽  
Tatsuki DOI ◽  
Yoichi KATSUMOTO ◽  
Kazuya TATSUMI ◽  
Kazuyoshi NAKABE
Keyword(s):  
Blood Cell ◽  
Red Blood Cell ◽  
Channel Flows ◽  
High Shear ◽  
Micro Channel ◽  
Cell Deformability ◽  
Red Blood Cell Deformability

Automatic Red Blood Cell Classification for MICAD Based on PSO-CSVM

2011 ◽  
Vol 121-126 ◽  
pp. 1952-1956
Author(s):  
Rui Hu Wang
Keyword(s):  
Blood Cell ◽  
Red Blood Cell ◽  
Depth Map ◽  
Experimental Result ◽  
Support Vector ◽  
Disease Treatment ◽  
Bivariate Polynomials ◽  
Multi Scale ◽  
Function Fitting

The automatic classification of erythrocyte is critical to clinic blood-related disease treatment in Medical Image Computer Aided Diagnosing(MICAD). After 3D height field recovered from the varied shading, the depth map of each point on the surfaces is applied to calculate Gaussian curvature and mean curvature, which are used to produce surface type label image. Accordingly the surface is segmented into different parts through multi-scale bivariate polynomials function fitting. The count of different surface types is used to design a classifier for training and classifing the red blood cell by means of support vector machine and particle swarm optimization. The experimental result shows that this approach is easily to implement and promising.

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