Image analysis of particle dispersions in microscopy images of cryo-sectioned sausages
Publication details
Journal : Scanning , vol. 23 , p. 165–174 , 2001
Publisher : John Wiley & Sons
International Standard Numbers
:
Printed
:
0161-0457
Electronic
:
1932-8745
Publication type : Academic article
Issue : 3
Links
:
DOI
:
doi.org/10.1002/sca.4950230302
If you have questions about the publication, you may contact Nofima’s Chief Librarian.
Kjetil Aune
Chief Librarian
kjetil.aune@nofima.no
Summary
Two feature extraction methods, the three-dimensional (3-D) local box-counting method and the area distribution method, are presented to describe the fat dispersion pattern on digital microscopy images of cryo-sectioned sausages. Both methods calculate whole arrays of variables for each microscopy image. The 3-D box-counting method calculates scale dependent (local) dimensions. This is in contrast to common fractal methods, which are univariate. Principal component analysis (PCA) was used to show that different sausages yield different fat dispersion patterns. Partial least square regression (PLS) shows that there is a correlation between the variables gained with both methods and the fat content.
