The effects of pre‐processing of image data on self‐modeling image analysis | Zendy

Windig W. | Zendy; Keenan M. R. | Zendy; Wise B. M. | Zendy

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The effects of pre‐processing of image data on self‐modeling image analysis

Author(s) -

Windig W.,

Keenan M. R.,

Wise B. M.

Publication year - 2008

Publication title -

journal of chemometrics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.47

H-Index - 92

eISSN - 1099-128X

pISSN - 0886-9383

DOI - 10.1002/cem.1164

Subject(s) - outlier , noise (video) , image processing , pixel , heteroscedasticity , data processing , stearic acid , analytical chemistry (journal) , computer science , materials science , biological system , image (mathematics) , chemistry , artificial intelligence , chromatography , composite material , machine learning , biology , operating system

The use of chemical imaging of secondary ion mass spectrometry (SIMS) data for self‐modeling image analysis (SIA) has special challenges because of the following reasons: (a) At higher counting rates, the data are non‐linear. (b) The heteroscedastic nature of the noise causes structure in the data which gives rise to extra components. (c) There is a high amount of noise in SIMS data and outliers often cause problems. This paper will discuss an adaptation of a pre‐processing method to correct for heteroscedastic noise and a method to minimize the effect of outlying pixels. Examples will be given of the following: (a) Different mixtures of palmitic and stearic acid on aluminum foil. (b) A film coating of polyvinyl acetate (PVA) and polystyrene (PS). (c) A sample of copper and nickel and a fused layer. Copyright © 2008 John Wiley & Sons, Ltd.

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