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

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State: Public
Version: author
Serval ID
serval:BIB_F50284FFDE75
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Automated segmentation of multiple red blood cells with digital holographic microscopy.
Journal
Journal of Biomedical Optics
Author(s)
Yi F., Moon I., Javidi B., Boss D., Marquet P.
ISSN
1560-2281 (Electronic)
ISSN-L
1083-3668
Publication state
Published
Issued date
2013
Peer-reviewed
Oui
Volume
18
Number
2
Pages
26006
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov'tPublication Status: ppublish
Abstract
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
Create date
28/03/2013 17:11
Last modification date
20/08/2019 17:21
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