Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis.

Détails

ID Serval
serval:BIB_4DBF725FE3C4
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis.
Périodique
Journal of the Optical Society of America. A, Optics, Image Science, and Vision
Auteur(s)
Liu R., Dey D.K., Boss D., Marquet P., Javidi B.
ISSN
1520-8532 (Electronic)
ISSN-L
1084-7529
Statut éditorial
Publié
Date de publication
2011
Peer-reviewed
Oui
Volume
28
Numéro
6
Pages
1204-1210
Langue
anglais
Notes
Publication types: Journal ArticlePublication Status: ppublish
Résumé
We propose to apply statistical clustering algorithms on a three-dimensional profile of red blood cells (RBCs) obtained through digital holographic microscopy (DHM). We show that two classes of RBCs stored for 14 and 38 days can be effectively classified. Two-dimensional intensity images of these cells are virtually the same. DHM allows for measurement of the RBCs' biconcave profile, resulting in a discriminative dataset. Two statistical clustering algorithms are compared. A model-based clustering approach classifies the pixels of an RBC and recognizes the RBC as either new or old based. The K-means algorithm is applied to the four-dimensional feature vector extracted from the RBC profile.
Mots-clé
Algorithms, Cluster Analysis, Discriminant Analysis, Erythrocytes/classification, Holography/methods, Humans, Microscopy/methods
Pubmed
Web of science
Création de la notice
05/04/2013 9:27
Dernière modification de la notice
20/08/2019 14:02
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