Image analysis of particle dispersions in microscopy images of cryo-sectioned sausages
Publikasjonsdetaljer
Tidsskrift : Scanning , vol. 23 , p. 165–174 , 2001
Utgiver : John Wiley & Sons
Internasjonale standardnummer
:
Trykt
:
0161-0457
Elektronisk
:
1932-8745
Publikasjonstype : Vitenskapelig artikkel
Sak : 3
Lenker
:
DOI
:
doi.org/10.1002/sca.4950230302
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Kjetil Aune
Bibliotekleder
kjetil.aune@nofima.no
Sammendrag
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.
