Identification of fat, protein matrix, and water/starch on microscopy images of sausages by a principal component analysis‐based segmentation scheme | Zendy

Kohler Achim | Zendy; Høst Vibeke | Zendy; Enersen Grethe | Zendy; Ofstad Ragni | Zendy

Open Access

Identification of fat, protein matrix, and water/starch on microscopy images of sausages by a principal component analysis‐based segmentation scheme

Author(s) -

Kohler Achim,

Høst Vibeke,

Enersen Grethe,

Ofstad Ragni

Publication year - 2003

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.4950250302

Subject(s) - segmentation , principal component analysis , pixel , artificial intelligence , starch , computer vision , matrix (chemical analysis) , microscopy , computer science , image segmentation , pattern recognition (psychology) , biological system , materials science , chemistry , food science , biology , composite material , optics , physics

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.

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