Open AccessImage analysis of particle dispersions in microscopy images of cryo‐sectioned sausages
Author(s) -
Kohler A.,
Høst V.,
Ofstad R.
Publication year - 2001
Publication title -
scanning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1932-8745
pISSN - 0161-0457
DOI - 10.1002/sca.4950230302
Subject(s) - principal component analysis , box counting , fractal dimension , digital image analysis , univariate , microscopy , materials science , dispersion (optics) , mathematics , biological system , fractal analysis , particle (ecology) , scale (ratio) , fractal , pattern recognition (psychology) , artificial intelligence , statistics , optics , multivariate statistics , computer science , computer vision , physics , biology , mathematical analysis , ecology , quantum mechanics
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.