Multivariate Image Analysis in Chemistry : An Overview

This chapter treats the relationship between exploratory data analysis and regression as used in chemometrics on one hand, and images, especially multivariate images on the other hand. The method presented for multivariate exploratory data analysis is principal component analysis (PCA) (1,2), with all its attributes and statistical diagnostics. The principles of latent variable regression methods are also introduced. As a contrast to the use of these methods in data analysis, the image analysis versions are focused on visualization of results and less oriented towards numerical output. This makes these methods much more user-friendly both conceptually as well as in practical use.

[1]  John L. Campbell,et al.  PIXE: A Novel Technique for Elemental Analysis , 1988 .

[2]  I. Smith,et al.  NMR spectroscopy and cancer research: the present and the future. , 1990, Analytical chemistry.

[3]  C. Oatley,et al.  The scanning electron microscope. , 1966, Science progress.

[4]  Paul Geladi,et al.  Image analysis and chemical information in images , 1986 .

[5]  I J Cox.  Scanning optical fluorescence microscopy , 1984, Journal of microscopy.

[6]  T. Næs,et al.  Principal component regression in NIR analysis: Viewpoints, background details and selection of components , 1988 .

[7]  Thomas L. Lentz,et al.  Advances in Optical and Electron Microscopy , 1970, The Yale Journal of Biology and Medicine.

[8]  Paul Geladi,et al.  Can image analysis provide information useful in chemistry? , 1989 .

[9]  John W. Schott.  Remote Sensing Of The Earth: A Synoptic View , 1989 .

[10]  K. Esbensen,et al.  Strategy of multivariate image analysis (MIA) , 1989 .

[11]  Integrated image processing/ GIS software/hardware for the PC : The ERDAS hw/sw system , 1989 .

[12]  George H. Morrison,et al.  SIMIPS: secondary ion mass image processing system , 1987, J. Chem. Inf. Comput. Sci..

[13]  H. E. Cascio,et al.  Microscopic spectral imaging using a video camera , 1986, Journal of microscopy.

[14]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[15]  S. Wold,et al.  Principal component analysis of multivariate images , 1989 .

[16]  W. S. Woodward,et al.  A microcomputer based digital imaging system for ion microanalysis , 1985 .

[17]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[18]  C. Fiori.  The new electron microscopy: imaging the chemistry of nature. , 1988, Analytical chemistry.

[19]  Paul Geladi,et al.  Tissue discrimination in magnetic resonance imaging: A predictive multivariate approach , 1989 .

[20]  B. P. Richards,et al.  Failure analysis of plastic encapsulated components—the advantages of IR microscopy * , 1987 .

[21]  Jae S. Lim,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[22]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[23]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[24]  J. Devillers,et al.  Practical applications of quantitative structure-activity relationships (QSAR) in environmental chemistry and toxicology , 1990 .

[25]  G. Morrison,et al.  Evaluation of matrix effects in ion microscopic analysis of freeze‐fractured, freeze‐dried cultured cells , 1987, Journal of microscopy.

[26]  Michael Michael Hord,et al.  Digital image processing of remotely sensed data , 1982 .

[27]  D. W. Turner,et al.  Photoelectron emission: images and spectra , 1984 .

[28]  H. Seiler.  Electron spectroscopy in the scanning electron microscope , 1985 .

[29]  Paul Geladi,et al.  Chemical Multivariate Image Analysis: Some Case Studies , 1991 .

[30]  Paul Geladi,et al.  Data analysis of multivariate magnetic resonance images I. A principal component analysis approach , 1989 .