Published 2001

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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

Contributors : Kohler, Achim; Høst, Vibeke; Ofstad, Ragni

Issue : 3

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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.