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.
journal_name
J Biomed Optjournal_title
Journal of biomedical opticsauthors
Ahmadzadeh E,Jaferzadeh K,Lee J,Moon Idoi
10.1117/1.JBO.22.7.076015subject
Has Abstractpub_date
2017-07-01 00:00:00pages
76015issue
7eissn
1083-3668issn
1560-2281pii
2646552journal_volume
22pub_type
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