Identification of fat, protein matrix, and water/starch on microscopy images of sausages by a principal component analysis-based segmentation scheme.
A color-based segmentation scheme applied to microscopy images of cryosectioned sausages is proposed. The segmentation scheme is capable of segmenting three different levels on the microscopy images: the fat particles, the protein matrix, and water/starch. The method is based on principal component analysis. A user-friendly program was developed for the manual segmentation of a selection of image pixels by microscopists. Principal component models based on the manually classified pixels are then used to segment fat, protein matrix, and starch/water on microscopy images. The program can also be used as a training tool for microscopists.
Kohler
Achim
A
Norwegian Food Research Institute, Matforsk, Osloveien 1, Norway. achim.kohler@matforsk.no
Høst
Vibeke
V
Enersen
Grethe
G
Ofstad
Ragni
R
D000818:Animals; D003116:Color; D016616:Cryoultramicrotomy; D005223:Fats / Q000032:analysis; D005504:Food Analysis / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008461:Meat Products / Q000032:analysis*; D008853:Microscopy / Q000295:instrumentation / Q000379:methods*; D025341:Principal Component Analysis; D011506:Proteins / Q000032:analysis; D012984:Software; D013213:Starch / Q000032:analysis; D013552:Swine; D014867:Water / Q000032:analysis
D005223:Fats; D011506:Proteins; D014867:Water; D013213:Starch