Automated segmentation of multiple red blood cells with digital holographic microscopy.

Détails

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Etat: Public
Version: de l'auteur
ID Serval
serval:BIB_F50284FFDE75
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Automated segmentation of multiple red blood cells with digital holographic microscopy.
Périodique
Journal of Biomedical Optics
Auteur(s)
Yi F., Moon I., Javidi B., Boss D., Marquet P.
ISSN
1560-2281 (Electronic)
ISSN-L
1083-3668
Statut éditorial
Publié
Date de publication
2013
Peer-reviewed
Oui
Volume
18
Numéro
2
Pages
26006
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov'tPublication Status: ppublish
Résumé
We present a method to automatically segment red blood cells (RBCs) visualized by digital holographic microscopy (DHM), which is based on the marker-controlled watershed algorithm. Quantitative phase images of RBCs can be obtained by using off-axis DHM along to provide some important information about each RBC, including size, shape, volume, hemoglobin content, etc. The most important process of segmentation based on marker-controlled watershed is to perform an accurate localization of internal and external markers. Here, we first obtain the binary image via Otsu algorithm. Then, we apply morphological operations to the binary image to get the internal markers. We then apply the distance transform algorithm combined with the watershed algorithm to generate external markers based on internal markers. Finally, combining the internal and external markers, we modify the original gradient image and apply the watershed algorithm. By appropriately identifying the internal and external markers, the problems of oversegmentation and undersegmentation are avoided. Furthermore, the internal and external parts of the RBCs phase image can also be segmented by using the marker-controlled watershed combined with our method, which can identify the internal and external markers appropriately. Our experimental results show that the proposed method achieves good performance in terms of segmenting RBCs and could thus be helpful when combined with an automated classification of RBCs.
Pubmed
Web of science
Création de la notice
28/03/2013 16:11
Dernière modification de la notice
20/08/2019 16:21
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