Open AccessEvaluation of scanning electron microscopy images of a model dressing using image feature extraction techniques and principal component analysis
Author(s) -
Egelandsdal Bjørg,
Christiansen Kirsti F.,
Høst Vibeke,
Lundby Frank,
Wold Jens Petter,
Kvaal Knut
Publication year - 1999
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.4950210505
Subject(s) - principal component analysis , artificial intelligence , feature extraction , pattern recognition (psychology) , viscosity , dimensionality reduction , computer science , feature (linguistics) , measure (data warehouse) , extraction (chemistry) , biological system , scanning electron microscope , computer vision , mathematics , materials science , chemistry , chromatography , data mining , linguistics , philosophy , biology , composite material
Twelve dressing systems made by varying protein type, oil level, CaCl 2 , NaCl, and sucrose, were examined using scanning electron microscopy. Images from the 12 systems were quantitatively analysed using methods of feature extraction. These methods were based on vectorisations of the images followed by principal component analysis on the extracted vectors. These techniques were used to examine the reproducibility of the acquired images as well as to relate the images to rheologic and sensory texture parameters. Two feature extraction methods were used: the angle measure technique (AMT) and the absolute difference method (ABDF). The ABDF method used fewer principal components to extract information from images relevant to the complex modulus/sensory viscosity of the system, but the information seemed equally well preserved by the two‐feature extraction methods. The AMT was more efficient in classifying the images with respect to protein type. A fair correlation between images and complex modulus was obtained (R=0.73). It is suggested that a better correlation might be obtained by adding more systems, increasing the number of areas imaged for each system as well as avoiding systems of low viscosity.