PC/MAC* Image Processing Freeware for Examining Spectral Images

2000 ◽  
Vol 6 (S2) ◽  
pp. 1056-1057
Author(s):  
D. S. Bright
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Low Voltage ◽  
Processing System ◽  
Principal Component ◽  
Public Domain ◽  
Data Cube ◽  
Energy Dispersive ◽  
X Ray ◽  
Adjacent Channels

MacLispix, a public domain image processing system for the Macintosh*, has been applied to a variety of image processing problems, such as using Principal Component Analysis to explore correlated images. The tools provided by MacLispix are now available in Lispix, the updated version that runs on both the PC and the Macintosh.I will illustrate the utility of Lispix by way of an example ‘data cube', a low voltage energy dispersive x-ray spectrum image provided by Ian Anderson. The ‘cube'is 200x150 pixels, each pixel having a spectrum of 512 two-byte channels The spectra were smoothed and reduced by adding adjacent channels to reduce them to 256 channels each. Since Lispix is image oriented rather than spectrum oriented, the reduced cube is stored, and represented internally as 256 images (one image for each channel in the spectrum), rather than as 200x150 spectra (one spectrum for each pixel in the image).

An energy dispersive X-ray fluorescence spectrometry approach for the identification of geographical origin of wheat flour

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Tong Chen ◽  
Xingpu Qi ◽  
Zaiyong Si ◽  
Qianwei Cheng ◽  
Hui Chen
Keyword(s):  
Principal Component Analysis ◽  
Wheat Flour ◽  
Principal Component ◽  
Component Analysis ◽  
Mineral Elements ◽  
Energy Dispersive ◽  
Fluorescence Spectrometry ◽  
Energy Channel ◽  
X Ray ◽  
Different Origins

Abstract In this work, a method was established for discriminating geographical origins of wheat flour based on energy dispersive X-ray fluorescence spectrometry (ED-XRF) and chemometrics. 68 wheat flour samples from three different origins were collected and analyzed using ED-XRF technology. Firstly, the principal component analysis method was applied to analyze the feasibility of discrimination and reduce data dimensionality. Then, Competitive Adaptive Reweighted Sampling (CARS) was used to further extract feature variables, and 12 energy variables (corresponding to mineral elements) were identified and selected to characterize the geographical attributes of wheat flour samples. Finally, a non-linear model was constructed using principal component analysis and quadratic discriminant analysis (QDA). The CARS-PCA-QDA model showed that the accuracy of five-fold cross-validation was 84.25%. The results showed that the established method was able to select important energy channel variables effectively and wheat flour could be classified based on geographical origins with chemometrics, which could provide a theoretical basis for unveiling the relationship between mineral element composition and wheat origin.

Multi-Variate Analysis of Trace Elements from XRF Studies for Classification According to Origin

1992 ◽  
Vol 46 (5) ◽  
pp. 843-847 ◽  
Author(s):  
C. T. Yap
Keyword(s):  
Principal Component Analysis ◽  
Trace Elements ◽  
Geographical Origin ◽  
Principal Component ◽  
Energy Dispersive ◽  
Fluorescence Technique ◽  
X Ray ◽  
Elegant Method ◽  
Variate Analysis ◽  
Made In

The concentrations of twelve trace elements (Mn, Fe, Co, Ni, Cu, Zn, As, Rb, Sr, Y, Zr, and Nb) in 143 pieces of Chinese porcelain made in Jingdezhen, China and elsewhere were obtained with the use of the energy-dispersive x-ray fluorescence technique. An elegant method of multi-variate analysis, known as principal component analysis, was successfully employed in fingerprinting the geographical origin of the porcelain samples.

scholarly journals Microanalysis of individual silver halide microcrystals

1992 ◽  
Vol 50 (2) ◽  
pp. 1612-1613
Author(s):  
S. Wu ◽  
A. Van Daele ◽  
W. Jacob ◽  
R. Gijbels ◽  
A. Verbeeck ◽  
...  
Keyword(s):  
Image Processing ◽  
Electron Microscope ◽  
Silver Halide ◽  
Processing System ◽  
Energy Dispersive ◽  
Elemental Distribution ◽  
X Ray ◽  
Scanning Transmission ◽  
Carbon Coated ◽  
Electron Imaging

There is a considerable interest for the study of the elemental distribution and composition in silver halide photographic emulsions, particularly for the microanalysis of individual microcrystals. In this work, elemental distributions and contents of tabular and cubic silver halide microcrystals were obtained by backscattered electron imaging (BSEI), scanning transmission electron imaging (STEI), x-ray mapping and x-ray microanalysis in a scanning electron microscope (STEM) combined with energy-dispersive x-ray analysis (EDX).Several kinds of silver halide microcrystals were prepared, After removing the gelatin, repeated centrifugation and washing in distilled water, the grains were resuspended and dispersed onto carbon coated 50 mesh copper grids. All analyses were carried out on a JEOL 1200 EX electron microscope equipped with detectors for backscattered, secondary and transmitted electrons and an energy dispersive x-ray analysis system. An image processing system was used for acquiring and processing BSE images, STE images and x-ray maps. The role of the image processing computer system (IBAS Kontron) is twofold: it allows to optimize the acquisition conditions and to process the images afterwards.

Lispix: A Public Domain Scientific Image Analysis Program for the PC and Macintosh*

2000 ◽  
Vol 6 (S2) ◽  
pp. 1022-1023
Author(s):  
D. S. Bright ◽  
K. G. Milans
Keyword(s):  
Image Processing ◽  
Processing System ◽  
Public Domain ◽  
Analysis Program ◽  
X Ray ◽  
Data Cubes ◽  
Scientific Image ◽  
Particle Measurement ◽  
Ieee Standard

MacLispix, a public domain image processing system for the Macintosh, has been applied to a variety of image processing problems such as analysis of diffraction spots, uniform display of x-ray maps, determination of fractal dimension of particle outlines, and analysis of data cubes. Due to interest from the PC community, we have ported the software to Windows, renamed it Lispix', and distributed it for both platforms, along with example images, source and documentation.Lispix reads TIFF files and raw files (no image header), both with pixel types of signed and unsigned 8, 16 and 32 bit integers, 4 and 8 byte IEEE standard floating point numbers, and 3x8 bit RGB color. Lispix has a variety of standard image processing operations, such as thresholding, edge finding (gradient), filtering, scaling, Linear Hough Transform, false coloring, RGB color overlays and particle measurement.

Scanning x-ray fluorescence microscopy and principal component analysis

1992 ◽  
Vol 50 (2) ◽  
pp. 1752-1753
Author(s):  
Brian Cross
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Microscopy ◽  
Spatial Resolution ◽  
High Speed ◽  
Image Acquisition ◽  
Principal Component ◽  
Fluorescence Microscope ◽  
Acquisition Rate ◽  
X Ray ◽  
Speed Up

A relatively new entry, in the field of microscopy, is the Scanning X-Ray Fluorescence Microscope (SXRFM). Using this type of instrument (e.g. Kevex Omicron X-ray Microprobe), one can obtain multiple elemental x-ray images, from the analysis of materials which show heterogeneity. The SXRFM obtains images by collimating an x-ray beam (e.g. 100 μm diameter), and then scanning the sample with a high-speed x-y stage. To speed up the image acquisition, data is acquired "on-the-fly" by slew-scanning the stage along the x-axis, like a TV or SEM scan. To reduce the overhead from "fly-back," the images can be acquired by bi-directional scanning of the x-axis. This results in very little overhead with the re-positioning of the sample stage. The image acquisition rate is dominated by the x-ray acquisition rate. Therefore, the total x-ray image acquisition rate, using the SXRFM, is very comparable to an SEM. Although the x-ray spatial resolution of the SXRFM is worse than an SEM (say 100 vs. 2 μm), there are several other advantages.

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