Processing Electron, X-ray, and CL images

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Electron probe microanalysisEPMA

• Processing Electron, X-ray, and CL images

Modified 8/22/08
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Whats the point?

• A picture is worth a thousand words.
• Raw images sometimes need to be processed
• to highlight particular features (sometimes easy
  to do, sometimes difficult and requires advances
  computation)
• to extract quantitative information (e.g. modal
  abundances)

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Image Processing Analysis

• Image enhancement
• Segmentation and thresholding
• Processing in frequency space
• Processing binary images
• Image measurements
• Image presentation

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Image Enhancement - Done Later

• Histogram normalization crunching from 16 to 8
  bit. This usually is a first step for visual
  presentation purposes, as most software packages
  only operate on 8 bit images. However, this does
  not apply for measuring absolute values of pixel
  intensity, such as X-ray counts.
• Brightness/contrast (and importantly, gamma)
  adjusting histogram levels
• Histogram equalization divide intensities into
  equal/weighted number of categories
• Kernels/Rank operators modify each pixel by
  some operation upon it and nearest neighbors
• Image math background subtraction ratio 2
  elements
• Processing in frequency space (Fourier
  transform) removing periodic noise
• Applying alternate lookup tables (LUTs) for
  improved presentation

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Intensities, Histograms, LUTs

• All images we are concerned with (e.g.,
  BSE, CL, X-ray) contain one channel of
  information, where each constituent pixel has a
  value from 0 to 255 (28) or 65535 (216). These
  can be ordered in a histogram of intensities,
  with the spread defining the contrast, and the
  absolute values defining how bright or dark the
  image is. These INPUT intensities are mapped onto
  an OUTPUT grayscale or color table known as a
  Look Up Table (LUT).
• The transfer function is known as gamma.
  A gamma of 1.00 indicates a linear relationship
  between pixel intensities and grayscales. A gamma
  gt1 is a non-linear function where the darker
  pixels are made preferentially brighter, whereas
  gamma lt1 has the very bright pixels
  preferentially darkened somewhat.
• Adjusting only brightness and
  contrast controls (highlighted in many image
  packages) generally give poorer results compared
  to tweaking the gamma as part of histogram
  adjustment.

LUT
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Brightness and Contrast or How I Learned to Love
the Histogram
Adjust gLevels
Photoshop
The original histogram is too bunched up poor
contrast. Notice the top (input) left and right
sliders are not close to the min/max brightness.
So we move the top (input) left and right sliders
in to the min/max brightness levels.And we move
the bottom (output) sliders to 10 and 254.
A last (important) step is to adjust the gamma,
the top middle slider. To left (higher) increases
brightness of mid grays (normally the best
option).
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Gamma Processing
Goldstein et al, 1992, Fig. 4.53, p. 238
The traditional imaging medium, photographic
paper, has a non-linear response to light
exposure through the overlying negative. Skilled
darkroom technique used this to bring out subtle
features in the shadows, or enhance bright
features that tend to wash out. For digital
images, such nonlinear processing, gamma
processing, provides selective contrast
enhancement at either the black or white end of
the gray scale, while preventing saturation or
clipping of the resulting image. The signal
transfer function is defined as
where g is an integer (1, 2, 3,
4) or a fraction (1/2, 1/3, 1/4) and K is a
linear amplification constant. For g2, a small
range of input signals at the dark end of the
gray scale are distributed over a larger range of
output gray levels, enhancing the contrast here
signals at the white end are compressed into
fewer gray levels. For g 1/2, expansion occurs
at the bright end, enhancing bright features.
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Histogram Levels Equalization
One alternative/complementary procedure to manual
adjust of brightness/contrast is equalization,
which can be applied to the raw image. It
stretches out the histogram, with the distinction
that it separates the intensities into weighted
bins, so that if there are a lot of pixels piled
in a few bins, these bins (intensities) will have
a larger number of new intensities mapped onto
them i.e., there will be spaces between them
on the histogram, meaning those intensities will
be stretched out. At the same time, bins with not
many pixels in them may be squeezed together, as
there is less total information relative to the
high populated pixels.
Russ, 1999, Fig. 4.11, p. 238.
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Kernels/Rank Operators

• Noisy images sometimes occur for a variety of
  reasons, some avoidable, some not. Noise refers
  to some randomness added to pixel intensity
  values, with noise worse where count rates are
  low. The simplest procedure to reduce noise is
  to take the average of the pixel and its
  surrounding neighbors, and put this new average
  value in as the new pixel intensity. You can
  create a matrix with values for the coefficient
  by which you weigh (multiply) each pixel and
  adjoining neighbors. For example, one such matrix
  could be
• 1 1 1 and 1 2 1
• 1 1 1 another 2 4 2 1 1 1 1 2 1
• These are called kernels, or rank operators.
• Say there was a noisy pixel with a value of
  100, when all the adjoining values were 10. The
  first kernel would return a new value of 20, and
  the noise would be drastically reduced.

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Neighborhood averaging
Results of applying one kernel
a) A noisy original image,
b) each 4x4 block of pixels is
averaged (less noise, but too coarse),
c) each pixel replaced by average of
3x3 neighbor-hood ( pretty nice),
d) each pixel replaced by average of
11x11 neighborhood ( less noise, but too big,
causing blurring)
Russ, 1999, The Image Processing Handbook (3rd
edition), Fig 3.3, p. 166
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Image Math
The values of each pixel can be operated on (e.g.
multiplied, divided, added or subtracted relative
to some constant), or different elements of the
same image can be operated on. The most common
operations are division and subtraction. Two
elements that vary together (e.g. Ca and Na in
feldspar) can be divided to yield an optimized
zonation map. Subtraction is useful for removing
the continuum contribution, particularly for
minor or trace elements.
Goldstein et al, 1992, Fig 10.6, p. 535
Above is an example of false compositional
contrast, an artifact of the background being a
function of Z (MAN). Specimen is Al-Cu eutectic
X-ray maps are (a) Al, (b) Cu, (c) Sc. The
contrast in (c) suggests Sc is present in the
Cu-rich phase. However, there is no Sc, only the
background in the Cu-rich phase is elevated
relative to the background in the Al-rich phase.
If image math is used subtracting an additional
X-ray map acquired at an off-peak (background) Sc
position a true map of Sc is seen in (d), where
it is clear there is no Sc present.
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2 Dimensional Histograms
Another mining of X-ray images utilizes both
the elemental information as well as the spatial
(X,Y) coordinates. Micro-Image includes a unique
histogram-histogram plotting feature for
unambiguous identification of numerous phases. In
this screen shot, the lower right image displays
a histogram-histogram plot which shows the
presence of at least 6 phases including a solid
solution
component. The upper right image display a
"traceback" of one selected phase cluster which
provides black and white mask of spatial
information. (From the Advanced Microbeam Inc
webpage)
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Processing in Frequency Space
Examples from Russ, Image ProcessingTool Kit
Tutorial, Part 4, Fig 4.C.1, page 8.
If there periodic noise in an image (e.g., the 2
frequencies on top of the clown image), the image
can be processing by a Fast Fourier Transform
(FFT) of it, as is done in the small subregion in
the left frame. The 2 frequencies of noise show
up as 2 pairs of dots (the clown features are the
NS, EW lines and center dot). If 4 small solid
circles are placed upon the
4 dots and then the resulted inverted, a mask is
made (center), which is then subtracted from the
left FFT image. Then an inverse FFT operation is
done on this image, and the result is the right
image, where the noise is removed. These
operations must be done on square images, using
NIH Image or Russs Image Toolkit with Photoshop.
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Look Up Tables

• The mapping of intensites (e.g., BSE voltages or
  X-ray counts) to a displayed image uses a Look Up
  Table, the most common one being a gray scale.
  The default with MicroImage is the thermal LUT.
  There are many others, and you can make up your
  own. It is a good idea to display the LUT as a
  bar next to the image if they might be some
  confusion as to what color means what intensity.

Gray scale Fire 1 Fire 2 Rainbow Ice
Some LUTs from NIH Image
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Processing binary images

• When we acquire images, we are in essence
  acquiring information about features defined as
  compositions, or sizes or shapes, of phases or
  boundaries or whatever. Our eyes brains are
  sorting out features constantly, such as in the
  process of sorting out the black lines and shapes
  against the white background here, translating
  into words and then into meanings.
• We can apply similar binary operations to our
  images focusing on one characteristic and
  ignoring the rest for the moment. This is known
  as thresholding, where we set upper and lower
  thresholds of intensity (e.g., BSE) and then
  define as a feature (e.g., one phase) the
  intensities that fall in between. Software can
  then be applied to such a binary image to do many
  things, e.g., count the number of pixels (thus,
  determine phase area).
• Boolean (logical) operations can be done on sets
  or images, taking two element maps and create a
  third one that shows the regions where features
  containing both elements are present, or only one
  without the other. Morphological operations can
  be done to modify individual pixels within an
  imageapply erosion and dilation operators to
  separate touching phases and then count total
  number of separate phases or measure the
  dimensions or orientation of each.

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Thresholding
NIH Image provides an easy way to threshold
images, shown here. You double click the little
up/down icon (6th from top, right column) which
gives you a red sliding palette that you use to
color in the phase you are selecting. You then
click Measure under the Analyze menu and the
total number of pixels is shown in the Info Box.
If you do this for all the phases
Cr-spinel 57208/44222512.9, Mg-rich
clay 215634/442225 48.8, Diopside
153904/44222534.8, Cracks
14947/4422253.4 Total (without fudging!)
99.9
Present, you should be able to get a total of
1005 easily.
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Making an Image into a Binary
Besides being able to determine area percentages,
you use the thresholded region to make a binary
image of that one feature/phase. In NIH Image it
is simple Process gt Binary gt Make Binary. The
result of that operation is shown in the center
image. Note that there are some outliers,
mainly in cracks. You need to
make some reasoned judgements about whether or
not to include them. Here, I decided not to
include them, so I then did 2 consecutive erode
operations (under Binary menu), and then 2
consecutive dilate operations, to yield the
final image on the right. Of course there could
well be cases where you would not do the erodes.
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Boolean Operations
Binary images consist of groups of pixels
selected on the basis of some common property.
Logical or Boolean operations can be applied,
pixel by pixel, to sets of images. The logical
operations typically are AND, OR, XOR (exclusive
or), NOT. The logical operator looks at each
pixel to see if it is on or off. AND
requires both pixels be ON to be ON in the
result. OR if either pixel is ON, it will be ON
in the result. XOR turns a pixel ON in the
result only if it is ON in only one, not both, of
the images. All 3 require 2 images. The NOT
operator only requires one, and it reverses the
meaning of each pixel.
Original X-ray maps (top) c) Si, d) Fe These
have been smoothed and thresholded to make binary
images. The thresholded Fe image is shown below
left (a), with Fe black. The Fe and Si images
have been combined as Fe AND NOT Si, to yield the
right (b) image of the Fe-oxide phase, excluding
the Fe-silicate phase.
From the symbolic logic developed by George
Boole, British mathematician, 1815-1864
Russ, The Image Processing Handbook, 1999, Figs
7.5, 7.6, p. 436.
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Color Superposition of Elemental Maps
While not strictly a Boolean operation (not
binary images), by defining each elemental map
with hues of either R, G or B, and then combining
(flattening) the image in Photoshop, phase
information can be extracted. Images from
research of Josh Kearns and Jill Banfield sand
from Tanana River, central Alaska
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Erosion/Dilation
Sometimes you want to measure features but the
binary image isnt unambiguous, as shown in the
example to the right. Here, you are attempting to
measure the area of the middle gray phase (a),
but when you threshold it, there are outlines of
the bright phase (b). The outline is only 1 pixel
wide, so you can apply an erode operation, which
will remove the outlines that you want to get rid
of, but also it will remove the outer layer of
pixels from all of the features you are
interested in (c). No problem.
Just apply the dilate operation, and where there
are any existing pixels, there will be added a
layer of pixels (d), and now you can do your
measurement.
Russ, The Image Processing Handb ook, 1999, Fig.
7.36, p. 462
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Image measurements
Geology 777
ImageJ 1.28
NIH Image 1.63
Features in images lend themselves to measurement
without too much difficulty
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Resources

• Software
• MicroImage (interfaces with SX51)
• Matrox Intellicam (interfaces with SX51 video
  display)
• NIH/Scion Image for a manual-article-tutorial
  , go to rsb.info.nih.gov/nih-image/more-docs/Tutor
  ial/Contents.html
• Adobe Photoshop
• Image Processing Tool Kit (Russ/Reindeer Games)
  plug-ins for Photoshop
• Graphic Converter (Mac)
• Books
• The Image Processing Handbook by John C. Russ,
  3rd Ed, 1999, CRC Press (he teaches a week-long
  short course at North Carolina State University)
• Quick Photoshop for Research, A guide to digital
  imaging for Photoshop 4xd, 5x,6x,7x by Jerry
  Sedgewick, 2002, Kluwer Academic/Plenum Publishers

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Conclusion

• Imaging covers a wide range of topics and we have
  just skimmed the surface here.