Marking Pixels and Spectra Summation


Visualising the surface of a sample in terms of chemical state images is a goal in itself; however the ability to construct spectra from the individual spectra-at-pixels corresponding to a range of pixel intensities represents a means of examining the related chemistry in detail. Further, while PCA provides a means of examining the spectra at each pixel, the procedure does require the judicious choice of the information baring abstract factors and ultimately a least square criterion fits the abstract factors to the raw spectra-at-pixels; as such errors are possible, therefore a means of summing spectra from similar pixels provides both a useful tool and also an independent approach for generating spectra from the raw data in image data sets with an acceptable signal-to-noise for peak-fitting and quantification.


The procedure in CasaXPS for summing spectra-at-pixels is based on the definition of a false-colour visualisation of an image, where various intensity ranges are assigned different pixel colours. The resulting colour scale allows the pixels within an image to be partitioned into zones of identical chemical state information identified by a pixel colour and using the false-colour image, the raw spectra-at-pixels are summed to produce as many spectra from an image data set as there are false colours in the display of the image. The steps en route to summing the spectra-at-pixels are as follows:

  1. Acquire an image data set in which images are acquired at regular energy interval spanning all the photoelectric lines required for quantifying the sample surface.
  2. Use PCA to create a set of images representative of the chemical state information from the sample.
  3. Define a false-colour scale for an image such that the surface is identified using one or more intensity ranges and colours.
  4. Define the false-colour scale as the active template for summing the pixel spectra.
  5. For each spectra-at-pixels Experiment Frames generated from the original image data sets, apply the false-colour scale template. The result of applying the template to an Experiment Frame containing spectra-at-pixels is a new Experiment Frame containing the spectra representing the sum of all the pixel spectra corresponding to each of the false-colours in the template image.


The details for performing these tasks now follow, where the steps are illustrated using the example of an MRS-3 standard and data take from a Kratos Axis Ultra.


Step 1:  Images were acquired at energies spanning the Cr 2p doublet, O 1s, C 1s, Sn 3d and In 3d photoelectric lines (Figure 1).


Step 2: For this example, the Cr 2p images are converted to spectra at each pixel (Figure 2). At a pixel level, the signal-to-noise is poor, however by using PCA to partition the signal from the noise throughout the entire set of spectra (256x256 spectra), the data at each pixel is enhanced to permit the intensity at each pixel for the Cr 2p regions to be defined using a quantification region. The details of the PCA analysis is discussed elsewhere in the manual (see image processing section). However, the result of the PCA is shown in Figure 3.


Step 3: The image in Figure 3 in visualised using three false-colours. The intensity ranges, as seen via the colour scale in Figure 3, are defined using the Colour Scale property page on the Image Processing dialog window. The cursor is used to drag out a line on over the image from which the intensities at the end points of the line are available for use on the dialog window invoked by the Add False Colour button. Alternatively, dragging out a box over the colour scale displayed next to the image will also define a range of intensities, the limits of which will appear in the text-fields on the resulting dialog window. The intensities of the pixels at the end points of the line or from the box over the colour scale are entered on the Image Lookup Table Range dialog window, which appears when the Add False Colour button is pressed; these values can be manually altered using the edit text-fields. The colour used to represent the intensity range can also be chosen from the Image Lookup Table Range dialog window, however if the default colour is used rather than choosing a new one, the colour scale colours and the colour used to plot spectra, will be identical (assuming the default order of selection before the spectra are overlaid in the Active Display tile).


On pressing the Add False Colour button, a processing command is added to the processing history. Adjustments to the processing history entries can be made in the usual fashion using the Processing History property page on the Spectrum Processing dialog window.


Step 4: Once the false-colour scale is prepared as seen in Figure 3, the next step is to define the image as the template for generating spectra. The Image Processing property page on the Image Processing dialog window provides the means of specifying the image plus false-colour scale for use with the spectra-at-pixels created from the image data set. Display the false-colour image in the Active Tile and press the button labelled Define Image. The image will redraw in the Active Tile, which indicates that the definition has been achieved.


Step 5: Following the definition of the template image, any spectra-at-pixel files containing only those VAMAS blocks from the original creation step can be manipulated using the template image. Switch focus to the Experiment Frame containing the spectra-at-pixels and press the button labelled Sum Spectra Using Colours, also on the Image Processing property page. A new Experiment Frame is created in which a new VAMAS block (one for each colour used in the false-colour scale) containing the sum of all the spectra-at-pixels with identical colour assignments (Figure 4).




Figure 1: Image data set from which spectra at pixels are generated.

Figure 2: An example of a spectrum from a pixel


Figure 3: False Colour Scale highlighting the regions of interest.



Figure 4: Spectra summed from spectra at pixels for each of the three colour zones shown in Figure 3.