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Skeletonization of Fibrin Networks

Published online by Cambridge University Press:  02 July 2020

S. Chang ,
C. A. Kulikowski ,
S. M. Dunn ,
S. Levy  and
J. W. Weisel
S. Chang
Affiliation:
Dept. of Computer Science, Rutgers, The State University of New Jersey, Piscataway, NJ08854-8019
C. A. Kulikowski
Affiliation:
Dept. of Computer Science, Rutgers, The State University of New Jersey, Piscataway, NJ08854-8019
S. M. Dunn
Affiliation:
Dept. of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ08855-0909
S. Levy
Affiliation:
Dept. of Computer Science, Rutgers, The State University of New Jersey, Piscataway, NJ08854-8019
J. W. Weisel
Affiliation:
Dept. of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA19104-6058
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Extract

A complex three dimensional network structure results when blood fibrin polymerizes into branching threads. To understand the rheological behavior of fibrin clots and how this behavior is associated with network morphology, one must obtain quantitative measurements of morphometric parameters of the networks (e.g., fiber diameter, distance between branching points of fibers, and branching complexity) formed under various conditions. Over the past several years, researchers have measured the parameters manually from the 3D reconstruction of networks using stereo images generated with intermediate voltage electron microscopy. Our goal is to help automate this time consuming process as much as possible, and skeletonization is a promising tool. Skeletonization is a process of transforming an object into idealized thin lines. Although various techniques for skeleton computation have been developed, most of them require explicit boundary information of the objects being skeletonized, which is not directly available from raw images.

Type
Advances in Digital Imaging
Information
Microscopy and Microanalysis , Volume 6 , Issue S2: Proceedings: Microscopy & Microanalysis 2000, Microscopy Society of America 58th Annual Meeting, Microbeam Analysis Society 34th Annual Meeting, Microscopical Society of Canada/Societe de Microscopie de Canada 27th Annual Meeting, Philadelphia, Pennsylvania August 13-17, 2000 , August 2000 , pp. 1024 - 1025
Copyright
Copyright © Microscopy Society of America

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References

References:

[1]Baradet, T. C. et al., Biophysical Journal 68(1995)1551.CrossRefGoogle Scholar
[2]Blum, H. and Nagel, R. N., Pattern Recognition 10(1978)167.CrossRefGoogle Scholar
[3]Danielsson, P., Computer Graphics And Image Processing 14(1980)227.CrossRefGoogle Scholar
[4]Eichel, P. H. and Delp, E. J., Proceedings of the 22nd Allerton Conference on Communications, Control, and Computers (1984)782.Google Scholar
[5]Huang, J. and Dunn, S. M., Journal of Computer Assisted Microscopy 9:4(1997).Google Scholar
[6]Huang, J. et al., Journal of Computer Assisted Microscopy 6:2(1994)85.Google Scholar
[7]Kass, M. et al., International Journal of Computer Vision 1:4(1988)321.CrossRefGoogle Scholar
[8]Qin-Zhong, Ye, Proceedings of the IEEE Ninth International Conference on Pattern Recognition (1988)721.Google Scholar
[9] The authors would like to thank Dr. Jean-Philippe Collet for his invaluable guidance in our understanding the issues related to fibrin clot networks.Google Scholar