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Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes

Title
Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes
Authors
Ahmadzadeh, EzatJaferzadeh, KeyvanLee, JieunMoon, Inkyu
DGIST Authors
Moon, Inkyu
Issue Date
2017-07
Citation
Journal of Biomedical Optics, 22(7), 07601501-07601512
Type
Article
Article Type
Article
Keywords
RED-BLOOD-CELLSK-MEANSTRANSFUSIONCLASSIFICATIONALGORITHMANEMIA
ISSN
1083-3668
Abstract
We present unsupervised clustering methods for automatic grouping of human red blood cells (RBCs) extracted from RBC quantitative phase images obtained by digital holographic microscopy into three RBC clusters with regular shapes, including biconcave, stomatocyte, and sphero-echinocyte. We select some good features related to the RBC profile and morphology, such as RBC average thickness, sphericity coefficient, and mean corpuscular volume, and clustering methods, including density-based spatial clustering applications with noise, k-medoids, and k-means, are applied to the set of morphological features. The clustering results of RBCs using a set of three-dimensional features are compared against a set of two-dimensional features. Our experimental results indicate that by utilizing the introduced set of features, two groups of biconcave RBCs and old RBCs (suffering from the sphero-echinocyte process) can be perfectly clustered. In addition, by increasing the number of clusters, the three RBC types can be effectively clustered in an automated unsupervised manner with high accuracy. The performance evaluation of the clustering techniques reveals that they can assist hematologists in further diagnosis. © 2017 The Authors.
URI
http://hdl.handle.net/20.500.11750/6528
DOI
10.1117/1.JBO.22.7.076015
Publisher
SPIE
Related Researcher
  • Author Moon, Inkyu Intelligent Imaging and Vision Systems Laboratory
  • Research Interests
Files:
There are no files associated with this item.
Collection:
Department of Robotics EngineeringIntelligent Imaging and Vision Systems Laboratory1. Journal Articles


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