NDLI: Detection of unstained living neurospheres from phase contrast images with very large illumination variations

Content Provider	IEEE Xplore Digital Library
Author	Wei Xiong Shue-Ching Chia Joo-Hwee Lim Shvetha, S. Ahmed, S.
Copyright Year	2011
Description	Author affiliation: Institute of Medical Biology, A∗STAR, Singapore 138648 (Shvetha, S.; Ahmed, S.) \|\| Institute for Infocomm Research, A∗STAR, Singapore 138632 (Wei Xiong; Shue-Ching Chia; Joo-Hwee Lim)
Abstract	Live imaging of neural stem cells and progenitors is important to follow the biology of these cells. Non-invasive imaging techniques, such as phase contrast microscopy, are preferred as neural stem cells are very sensitive to photoxic damage cause by excitation of fluorescent molecules. However, large illumination variations and weak foreground/background contrast make phase contrast images challenging for image processing. In the current work, we propose a new method to segment neurospheres imaged under phase contrast microscopy by employing high dynamic range imaging and advanced level-set method. The use of high dynamic range imaging enhances the fused image by expressing cell signatures from various exposure captures. We apply advanced level-set method in cell segmentation to improve the detection rate over simple methods such as thresholding. Validation experiments in the analysis of 21 images containing over 400 cells have demonstrated accuracy improvements over existing techniques.
Starting Page	6154
Ending Page	6157
File Size	810821
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091520
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Lighting Microscopy Dynamic range Active contours Sensitivity
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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