Imaging particle analysis: Difference between revisions

Revision as of 21:22, 3 March 2020

Revision as of 14:15, 30 March 2020

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# A gray scale [[Thresholding (image processing)|thresholding]] process is used to perform [[Image segmentation#Thresholding|image segmentation]], segregating out the particles from the background, creating a [[binary image]] of each particle.<ref name=Gonzalez>{{cite book|last=Gonzalez|first=Rafael C.|title=Digital Image Processing|year=2002|publisher=Pearson Education|isbn=978-8178086293|pages=595–611|author2=Woods, Richard E.}}</ref><ref name="Sankur2004">{{cite journal|last1=Sankur|first1=Bulent|title=Survey over image thresholding techniques and quantitative performance evaluation|journal=Journal of Electronic Imaging|volume=13|issue=1|year=2004|pages=146|issn=1017-9909|doi=10.1117/1.1631315|bibcode = 2004JEI....13..146S }}</ref><ref>{{cite journal|title=A Threshold Selection Method from Gray-Level Histograms|journal=IEEE Transactions on Systems, Man, and Cybernetics|volume=9|issue=1|year=1979|pages=62–66|issn=0018-9472|doi=10.1109/TSMC.1979.4310076|last1=Otsu|first1=Nobuyuki}}</ref>

# [[Digital image processing]] techniques are used to perform [[image analysis]] operations, resulting in morphological and grey-scale measurements to be stored for each particle.<ref name="CarterYan2005">{{cite journal|last1=Carter|first1=R M|last2=Yan|first2=Y|title=Measurement of particle shape using digital imaging techniques|journal=Journal of Physics: Conference Series|volume=15|issue=1|year=2005|pages=177–182|issn=1742-6588|doi=10.1088/1742-6596/15/1/030|bibcode = 2005JPhCS..15..177C }}</ref>

# The measurements saved for each particle are then used to generate image population statistics,<ref>{{cite web|last=Pouli|first=T.|title=Image Statistics and their Applications in Computer Graphics (2010)|url=http://www.cs.bris.ac.uk/~reinhard/papers/eg2010_tania.pdf|archive-url=https://wayback.archive-it.org/all/20110401055809/http://www.cs.bris.ac.uk/~reinhard/papers/eg2010_tania.pdf|url-status=dead|archive-date=1 April 2011|publisher=Eurographics, State of the Art|accessdate=2 January 2014|author2=Cunningham, D|author3=Reinhard, E.}}{{Dead link|date=January 2020 |bot=InternetArchiveBot |fix-attempted=yes }}</ref> or as inputs to algorithms for filtering and sorting the particles into groups of similar types. In some systems, sophisticated [[pattern recognition]] techniques<ref name="Rosenfeld1981">{{cite journal|last1=Rosenfeld|first1=A.|title=Image pattern recognition|journal=Proceedings of the IEEE|volume=69|issue=5|year=1981|pages=596–605|issn=0018-9219|doi=10.1109/PROC.1981.12027}}</ref><ref>{{cite book|last=Young|first=T. Y.|title=Handbook of Pattern Recognition and Image Processing|year=1986|publisher=Academic Press|isbn=978-0127745602|url-access=registration|url=https://archive.org/details/handbookofpatter0000unse}}</ref> may also be employed in order to separate different particle types contained in a heterogeneous sample.

Imaging particle analyzers can be subdivided into two distinct types, static and dynamic, based upon the image acquisition methods. While the basic principles are the same, the methods of image acquisition are different in nature, and each has advantages and disadvantages.