Multivariate image

As a result of 3D SIMS analysis, a stack of multivariate images is stored which can be described in case of qualitative species information by... 

The interpretation of a multivariate image is sometimes problematic because the cause for pictorial structures may be complex and cannot be interpreted on the basis of images of single species even if they are processed by filtering etc. In such cases, principal component analysis (PCA) may advantageously be applied. The principle of the PCA is like that of factor analysis which has been mathematically described in Sect. 8.3.4. It is represented schematically in Fig. 8.33. 

Artyushkova K, Fulghum JE (2002) Multivariate image analysis methods applied to XPS imaging data sets. Surface Interface Anal 33 185... 

Geladi P, Grahn H (1996) Multivariate image analysis. Wiley, Chichester... 

Geladi P, Grahn H, Esbensen K, Bengtsson E (1992b) Image analysis in chemistry. II. Multivariate image analysis. Trends Anal Chem 11 121... 

P. Geladi and H. Grahn, Multivariate Image Analysis, John Wiley Sons, Chichester, 1996. 

Multivariate Image Analysis Strong and Weak Multiway Methods Strong and weak -way methods analyze 3D and 2D matrices, respectively. Hyperspectral data cube structure is described using chemometric vocabulary [17]. A two-way matrix, such as a classical NIR spectroscopy data set, has two modes object (matrix lines) and V variables (matrix columns). Hyperspectral data cubes possess two object modes and one variable mode and can be written as an OOV data array because of their two spatial directions. 

Geladi, P. 8c Grahn, H., Multivariate Image Analysis John Wiley 8c Sons Chichester, 1996. 

Analytical Problem Solving Selection of Analytical Methods Multivariate Image Analysis Quality Assurance in Analytical Chemistry Quantitative Spectroscopic Calibration... 

Wiencke,D., vandenBroek, W. andBuydens,L. (1996) Identification of plastics among nonplastics in mixed waste by remote sensing near-infrared imaging spectroscopy 2. Multivariate image rank analysis for rapid classification. Anal. Chem. 67, 3760-6. 

Multivariate image reconstruction of FTIR array data... 

The data processing can be divided into three phases. Phase 1 is the removal of poor quality spectra with an automated routine. Phase 2 is the data preprocessing of the spectra, which passed the quality test. This usually entails some type of baseline correction and normalization process. Phase 3 is multivariate image reconstruction where the spectra are classified and reproduced as color points... 

Phase 2 - data preprocessing. There are many ways to process spectral data prior to multivariate image reconstruction and there is no ideal method that can be generally applied to all types of tissue. It is usual practice to correct the baseline to account for nonspecific matrix absorptions and scattering induced by the physical or bulk properties of the dehydrated tissue. One possible procedure is to fit a polynomial function to a preselected set of minima points and zero the baseline to these minima points. However, this type of fit can introduce artifacts because baseline variation can be so extreme that one set of baseline points may not account for all types of baseline variation. A more acceptable way to correct spectral baselines is to use the derivatives of the spectra. This can only be achieved if the S/N of the individual spectra is high and if an appropriate smoothing factor is introduced to reduce noise in the derivatized spectra. Derivatives serve two purposes they minimize broad... 

Nattkemper, T.W. 2004. Multivariate image analysis in biomedicine. J. Biomed. Inform. 37 380-391. 

MALDI MCM-41 MCR MD ME MEM MI MPM MRI MS MVA Matrix-assisted Laser Desorption/Ionization Mobile Crystalline Material-41 Multivariate Curve Resolution Molecular Dynamics Matrix-enhanced Magnetic Force Micrscopy Multivariate Image Multiphoton Microscopy Magnetic Resonance Imaging Mass Spectroscopy Multivariate Analysis... 

Paul Geladi, Multivariate Image Analysis in Chemistry and Related Areas Chemometric Image Analysis, Wiley, Chichester, 1996. 

Fig. 8.33. Common PC decomposition of a data matrix Xm>n (a) and PC decomposition of a data block Xm>n>9 of a multivariate image (b)...

Multivariate Image Analysis (MIA) and Principal Component Analysis (PCA)... 

Quantitative Image Information Multivariate Image Regression (MIR) 77 2.5... 

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