09 David Wiles - (Media Cybernetics) Introduction to Image Analysis
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Transcript of 09 David Wiles - (Media Cybernetics) Introduction to Image Analysis
![Page 1: 09 David Wiles - (Media Cybernetics) Introduction to Image Analysis](https://reader034.fdocuments.us/reader034/viewer/2022052700/55ada99e1a28ab9f748b4720/html5/thumbnails/1.jpg)
Introduction to Image Analysis
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What is an Image
Optical Systems project light to a focal plane
Imaging systems measure light intensity at the focal
plane of the optical system
Intensity measurements are stored in a numerical
matrix rendered on the screen as an image
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Image Generation
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www.mediacy.com
What is an image?
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What is Resolution?
It is the ability to differentiate objects
Defined as the smallest gap that can be measured
In optical systems it is limited by
Diffraction radii
Sampling rate
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Resolution
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Resolution
Raleigh Criterion Limit
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Resolution
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Resolution
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Resolution
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Resolution
Sampling Rate
Objects are easily
resolved optically
but sampling rate is
much higher than
required
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Resolution
Sampling Rate
Objects are easily
resolved optically
and sampling rate
is optimal
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Resolution
Sampling Rate
Objects are easily
resolved optically
but sampling rate is
too low
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Optimising resolution
Lens resolution is limited by Raleigh criterium
r(nm) = 1.22λ/ (NAobj+NAcon) in brightfield
r(nm) = 1.22λ/ 2NAobj in fluorescence
Camera resolution is limited by pixel separation
r(nm) = ΔPix(nm) / (MagObj x MagCon) x 2
If camera resolution is finer then the lens resolution, the
system is optimised
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Optimising Resolution
0
1
2
3
4
4x NA 0.10 10x NA 0.3 20x NA 0.45 40x NA 0.70 60x Oil NA 1.40
Camera resolution 6.45um pixel Optical Resolution
Camera resolution 4.5um pixel
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Bit Depth
Computers store data in a binary mode
All data consists of variations of combinations of a
specific number 1s and 0s
1 bit = 2 possible values (1/0)
2 bit = 4 possible values (00/01/10/11)
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Bit Depth
8 bit
28 Variations
Max 256
12 bit
212 Variations
Max 4,096
16 bit
216 Variations
Max 65,536
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Bit Depth
Digital sensors count photo-electrons
A pixel has a finite capacity (typically between
10,000-100,000e- full well)
No Electrons collected = 0
As many electrons as can be collected in pixel =
Bit Depth Max value (255/4095/65535)
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Bit Depth
In theory, higher bit depth means higher precision
when measuring intensity
But photo-electron counts are not very precise
(typically ±10e- read noise)
Intensity precision = Full Well / Read Noise
For a typical camera this could mean 10,000/10 =
1000 levels of variation
Other noise factors mean that intensity resolution is
usually worse
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Bit Depth
0
250
500
750
1000
1250
0 80 160 240 320 400 480 560 640 720 800
n Pixels
-250
0
250
500
750
1000
1250
0 320 640 960 1280 1600 1920 2240 2560 2880 3200 3520 3840
n Pixels
-250
0
250
500
750
1000
1250
0 25 50 75 100 125 150 175 200 225 250
n Pixels
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The Problem with Colour
Pixels not inherently wavelength sensitive
An e- is an e- regardless of whether it was excited by
a blue photon or a red photon
Colour measurements require us to discriminate
between wavelengths of light
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The Problem with Colour
How to generate a colour image?
Need to measure intensities at each point in red,
green and blue
Use filters to restrict to only the wavelength of light
you are interested in measuring
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The Problem with Colour
3 sensors
with a prism1 Sensor
with 3 filters
1 sensors
with Bayer
mask
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The Problem with Colour
3CCD:
+ Fast, Good colour resolution
- Expensive and technically challenging
Filter Wheel:
+ Great colour resolution, removable for higher
sensitivity in monochrome
- Slow, disabling it requires moving mechanical parts
Bayer Mask
+ Fast and inexpensive
- Resolution and sensitivity are sacrificed
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Everything in Imaging is a
Compromise
Optical resolution - Higher resolution means
shallower depth of field
Pixel sampling rates - more pixels means slower
frame rates
Bit depth - Higher bit depth require more computing
power
Colour - makes analysis more complicated
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Image Investigation
Tools
Bitmap Analysis
Image Histogram
Line Profile
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Line Profile Analysis
Plot pixel intensities along a
line
Detect profile features:
Valleys
Peaks
Falling Edges
Rising Edges
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Measuring Objects
Groups of pixels together make
objects
From Outlines we can get
measurements:
Perimeter
Area
Bounding box
Centre coordinates
Radii
Much, much more...