Towards The Automatic Segmentation of Multiple Micro Analytical Maps

1997 ◽  
Vol 3 (S2) ◽  
pp. 929-930
Author(s):  
N. Bonnet
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Automatic Segmentation ◽  
Data Sets ◽  
Sources Of Information ◽  
Multivariate Statistical ◽  
Data Set ◽  
Analytical Instruments ◽  
Different Sources

Multi-dimensional data sets are now produced by many analytical instruments. They include the series of spectra, the series of images and spectrum-images, which can be considered as a series of spectra at different positions or series of images at different wavelengths.The automatic (or semi-automatic) handling of such data sets requires that new multivariate analysis methods are made available. For instance, if we restrict ourselves to image sets, there is a need to deduce (from the multiple maps) a single map in which regions of the specimen with approximate homogeneous properties (composition ...) can be identified and quantified.At the present time, only a limited number of software tools are available for this purpose: - the scatterplot allows the display of the correlations between two or three spectra or images, - Interactive Correlation Partitioning (ICP) allows the user to divide the scatterplot into several parts and to reconstitute images with one selected part, -Multivariate Statistical Analysis (MSA) allows us to analyze a data set composed of several images and to identify the different sources of information, and to filter out noise and experimental artefacts.

scholarly journals Multivariate Statistical Analysis of Spectrum Lines and Images

1997 ◽  
Vol 3 (S2) ◽  
pp. 931-932 ◽  
Author(s):  
Ian M. Anderson ◽  
Jim Bentley
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quantitative Imaging ◽  
Boundary Segregation ◽  
Large Data ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Data Set ◽  
Recent Developments ◽  
Data Files

Recent developments in instrumentation and computing power have greatly improved the potential for quantitative imaging and analysis. For example, products are now commercially available that allow the practical acquisition of spectrum images, where an EELS or EDS spectrum can be acquired from a sequence of positions on the specimen. However, such data files typically contain megabytes of information and may be difficult to manipulate and analyze conveniently or systematically. A number of techniques are being explored for the purpose of analyzing these large data sets. Multivariate statistical analysis (MSA) provides a method for analyzing the raw data set as a whole. The basis of the MSA method has been outlined by Trebbia and Bonnet.MSA has a number of strengths relative to other methods of analysis. First, it is broadly applicable to any series of spectra or images. Applications include characterization of grain boundary segregation (position-), of channeling-enhanced microanalysis (orientation-), or of beam damage (time-variation of spectra).

Application of Multivariate Statistical Analysis to STEM X-ray Spectral Images: Interfacial Analysis in Microelectronics

2006 ◽  
Vol 12 (6) ◽  
pp. 538-544 ◽  
Author(s):  
Paul G. Kotula ◽  
Michael R. Keenan
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Curve Resolution ◽  
Foil Thickness ◽  
Beam Specimen ◽  
Spectral Image ◽  
Multivariate Statistical ◽  
Data Set ◽  
Thickness Change ◽  
X Ray

Multivariate statistical analysis methods have been applied to scanning transmission electron microscopy (STEM) energy-dispersive X-ray spectral images. The particular application of the multivariate curve resolution (MCR) technique provides a high spectral contrast view of the raw spectral image. The power of this approach is demonstrated with a microelectronics failure analysis. Specifically, an unexpected component describing a chemical contaminant was found, as well as a component consistent with a foil thickness change associated with the focused ion beam specimen preparation process. The MCR solution is compared with a conventional analysis of the same spectral image data set.

DUAL PLS ANALYSIS

2012 ◽  
Vol 11 (05) ◽  
pp. 879-891 ◽  
Author(s):  
STAN LIPOVETSKY
Keyword(s):  
Factor Analysis ◽  
Multivariate Analysis ◽  
Canonical Correlation ◽  
Statistical Technique ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Data Set ◽  
Correlation Matrices ◽  
Data Merging ◽  
Pls Analysis

A new multivariate statistical technique is obtained for comparing and combining two or more data sets each of which has a different number of respondents but the same variables. This approach can be considered as dual to such techniques as partial least squares, also known as inter-battery factor analysis and robust canonical correlation analysis for two data sets. It is shown that the problem can be reduced to the eigenproblem of the product of correlation matrices of each data set. The technique is generalized to three or more data sets in an eigenproblem of block-matrices of the correlations within each data set. This type of multivariate analysis can serve various practical problems of integration of data obtained from heterogeneous sources, particularly, for data merging in constructing data warehouses.

scholarly journals Discrimination of white ginseng origins using multivariate statistical analysis of data sets

2014 ◽  
Vol 38 (3) ◽  
pp. 187-193 ◽  
Author(s):  
Hyuk-Hwan Song ◽  
Ji Young Moon ◽  
Hyung Won Ryu ◽  
Bong-Soo Noh ◽  
Jeong-Han Kim ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Data Sets ◽  
Multivariate Statistical ◽  
White Ginseng

Prospect for HEW Applications of Multivariate Statistical Analysis in Microscopy and Spectroscopy

1990 ◽  
Vol 48 (1) ◽  
pp. 460-461
Author(s):  
N. Bonnet ◽  
E. Simova ◽  
S. Lebonvallet
Keyword(s):  
Electron Microscopy ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Large Data ◽  
List Type ◽  
Elemental Mapping ◽  
Multivariate Statistical ◽  
Significant Information ◽  
Data Set ◽  
Spatial Series

Multivariate Statistical Analysis has been developed in order to process large data sets and to extract from them the significant information.It has been introduced in electron microscopy in order to process image series of macromolecules and to classify individual images into several subsets of the series. This kind of series can be considered as a spatial series.In fact, electron microscopy and spectroscopy are using more and more image sequences in order to be able to access more precisely the content of the object studied. Among the different types of sequences which are already used and which will be used more extensively in the near future, one can mention : —spatial series (several slightly different objects, whose images are to be classified and combined ; or several microanalytical spectra recorded at different places of the object).—time series : in order to study changes induced in the object, one has to record successive images or spectra.—series in energy : one possibility to study the behavior of a specimen is to vary the accelerating voltage, to record several images or spectra and process the whole data set. Another exemple of series in energy is given by energy loss elemental mapping (or Auger elemental mapping) where several energy filtered images must be recorded (below and above the edge of interest) in order to get a “true” elemental map.—more “exotic” series can also be obtained, for instance series obtained by varying the spot size in MEB, series obtained with different configured detectors in STEM...

A note on tests of significance in multivariate analysis

1939 ◽  
Vol 35 (2) ◽  
pp. 180-185 ◽  
Author(s):  
M. S. Bartlett
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Correlation Coefficients ◽  
Multivariate Statistical ◽  
Generalized Variance ◽  
Vector Correlation ◽  
Tests Of Significance ◽  
The Matrix ◽  
Generalized Parameters

Multivariate generalizations. In multivariate statistical analysis, common terms such as variances and correlation coefficients have received certain generalizations. Wilks (7) has called the determinant |V|, where V is the matrix of variances and covariances between several variates, a generalized variance; certain ratios of such determinants have been called by Hotelling(5) vector correlation coefficients and vector alienation coefficients. While these determinantal functions have properties which justify to some extent this kind of generalization, it sometimes seems more reasonable to leave any generalized parameters, or corresponding sample statistics, in the form of matrices of elementary quantities. This is stressed by the formal analogy which then often exists between the generalized and the elementary formulae.

Multivariate Statistical Analysis of Three-Spatial-Dimension TOF-SIMS Raw Data Sets

2007 ◽  
Vol 79 (20) ◽  
pp. 7719-7726 ◽  
Author(s):  
V. S. Smentkowski ◽  
S. G. Ostrowski ◽  
E. Braunstein ◽  
M. R. Keenan ◽  
J. A. (Tony) Ohlhausen ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Spatial Dimension ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Raw Data ◽  
Tof Sims
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