Week 4
BME 695Y / BMS 634
Confocal Microscopy: Techniques and Application Module
Image Structure and 2D image Analysis Principles
& Sample Preparation Techniques
Purdue University Department of Basic Medical Sciences,
School of Veterinary Medicine
& Department of Biomedical Engineering, Schools of Engineering
J.Paul Robinson, Ph.D.
Professor of Immunopharmacology & Biomedical Engineering
Director, Purdue University Cytometry Laboratories
These slides are intended for use in a lecture series. Copies of the graphics are distributed and students encouraged to take their
notes on these graphics. The intent is to have the student NOT try to reproduce the figures, but to LISTEN and UNDERSTAND
the material. All material copyright J.Paul Robinson unless otherwise stated, however, the material may be freely used for
lectures, tutorials and workshops. It may not be used for any commercial purpose.
One useful text for this course is Pawley “Introduction to Confocal Microscopy”, Plenum Press, 2nd Ed. A number of the ideas and
figures in these lecture notes are taken from this text.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 1 t:/classes/BMS602B/lecture 3 602_B.ppt
Digital image analysis is Data Analysis.
• Data files are a representation of an original image, which is itself a representation
of reality.
• The chain of digital image processing includes both creation of digital data from an
image, and recreation of an image from the digital data.
• Data file formats are created in order to make specific operations more convenient.
The most convenient format may differ with the particular application.
• For most purposes, a one-to-one mapping of pixels to data values is most useful,
but the internal representation of the data values may be different for different file
formats.
• Files can be either compressed, or not, and compression can be either lossy or not.
For scientific analysis lossy compression is unacceptable; it may be useful for
overview presentations.
• Image manipulation can take place before image acquisition, during image
acquisition, on the digital data, or during recreation of an output image.
• Simple image manipulation includes brightness or contrast variation, re-sizing,
median filtering, and spatial kernel filtering.
• Brightness and contrast variation are controlled by a system input-output curve.
Spatial kernel filtering and median filtering use information local to a particular area of
an image to modify that area.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 2 t:/classes/BMS602B/lecture 3 602_B.ppt
How an image is created
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 3 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 4 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 5 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 6 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 7 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 8 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 9 t:/classes/BMS602B/lecture 3 602_B.ppt
Noise removal
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 10 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 11 t:/classes/BMS602B/lecture 3 602_B.ppt
How do humans classify objects?
Human method is pattern recognition based upon multiple
exposure to known samples. We build up mental templates of
objects, this image information coupled with other information
about an object allows rapid object classification with some
degree of objectivity, but there is always a subjective element.
 We are sensitive to differences in contrast. We will tend to
overestimate the amount or size of an object if there is high
contrast vs low contrast.
 We are sensitive to perspective and depth changes
 We are sensitive to orientation of lighting. We prefer light to
come from above.
 We fill in what we think should be in the image
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 12 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 13 t:/classes/BMS602B/lecture 3 602_B.ppt
This illustration was first published in 1861 by Ewald Hering. Astronomers became
very interested in Hering's work because they were worried that visual
observations might prove unreliable.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 14 t:/classes/BMS602B/lecture 3 602_B.ppt
This illusion was
created in 1889 by
Franz Müller-Lyon.
The lengths of the
two identical vertical
lines are distorted by
reversing the
arrowheads. Some
researchers think the
effect may be related
to the way the human
eye and brain use
perspective to
determine depth and
distance, even
though the objects
appear flat.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 15 t:/classes/BMS602B/lecture 3 602_B.ppt
In 1860 Johann Poggendorff created this line distortion illusion. The two
segments of the diagonal line appear to be slightly offset in this figure.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 16 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 17 t:/classes/BMS602B/lecture 3 602_B.ppt
An ambiguous image by the Dutch artist Gustave Verbeek
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 18 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 19 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 20 t:/classes/BMS602B/lecture 3 602_B.ppt
The above figure represents a series of 3 pixel x 3 pixel
kernels. Many image processing procedures will perform
operations on the central (black) pixel by using use
information from neighboring pixels. In kernel A,
information from all the neighbors is applied to the central
pixel. In kernel B, only the strong neighbors, those pixels
vertically or horizontally adjacent, are used. In kernel C,
only the weak neighbors, or those diagonally adjacent are
used in the processing. It is various permutations of these
kernel operations that form the basis for digital image
processing.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 21 t:/classes/BMS602B/lecture 3 602_B.ppt
modifying image contrast and brightness
• The easiest and most frequent method is
histogram manipulation
• An 8 bit gray scale image will display 256 different
brightness levels ranging from 0 (black) to 255
(white). An image that has pixel values throughout
the entire range has a large dynamic range, and
may or may not display the appropriate contrast for
the features of interest.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 22 t:/classes/BMS602B/lecture 3 602_B.ppt
It is not uncommon for the histogram to display
most of the pixel values clustered to one side of
the histogram or distributed around a narrow
range in the middle. This is where the power of
digital imaging to modify contrast exceeds the
capabilities of traditional photographic optical
methods. Images that are overly dark or bright
may be modified by histogram sliding. In this
procedure, a constant brightness is added or
subtracted from all of the pixels in the image or
just to a pixels falling within a certain gray scale
level ( i.e. 64 to 128).
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 23 t:/classes/BMS602B/lecture 3 602_B.ppt
A somewhat similar operation is histogram
stretching in which all or a range of pixel values in
the image are multiplied or divided by a constant
value. The result of this operation is to have the
pixels occupy a greater portion of the dynamic
range between 0 and 255 and thereby increase or
decrease image contrast. It is important to
emphasize that these operations do not improve
the resolution in the image, but may have the
appearance of enhanced resolution due to
improved image contrast.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 24 t:/classes/BMS602B/lecture 3 602_B.ppt
Histogram Stretching
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 25 t:/classes/BMS602B/lecture 3 602_B.ppt
B
A
Histogram sliding and stretching
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 26 t:/classes/BMS602B/lecture 3 602_B.ppt
Gamma - The gamma of a histogram curve is the slope,
expressed as a ratio of the logs of the output to input values. A
gamma value of 1.0 equals an output:input ratio of 1:1 and no
correction is applied. In some programs, a gamma function
applies a lookup table function to compensate or correct for the
bias which may be built into the video source. A camera's light
response is often set to a power function (Gamma function) to
mimic the photometric response of the human eye. This may
result in a non-linear response from the video source and cause
errors if you are making densitometric measurements. The
camera bias can be removed by applying an inverse gamma
function. This function calculates a lookup table to correct for the
bias based on operator provided parameters. The gamma
function for decalibrating the camera can be obtained from the
camera manufacturer.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 27 t:/classes/BMS602B/lecture 3 602_B.ppt
1.0
The straight line at the 45 degree angle in the
output lookup table indicates that no processing
has been performed on the pixels - gamma = 1.0
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 28 t:/classes/BMS602B/lecture 3 602_B.ppt
In this image a gamma factor of 1.8 has been
applied to the histogram of the output LUT
histogram
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 29 t:/classes/BMS602B/lecture 3 602_B.ppt
In this image a gamma factor of 2.2 has been
applied to the histogram of the output LUT
histogram
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 30 t:/classes/BMS602B/lecture 3 602_B.ppt
Inverse function applied to previous image
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 31 t:/classes/BMS602B/lecture 3 602_B.ppt
Arbitrary adjustment to the output LUT histogram
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 32 t:/classes/BMS602B/lecture 3 602_B.ppt
Removing noise in an Image
Images collected under low illumination
conditions may have a poor signal to noise
ratio. The noise in an image may be reduced
using image averaging techniques during the
image acquisition phase. By using a frame
grabber and capturing and averaging multiple
frames (e.g. 16 to 32 frames) the information in
the image may be increased and the noise
decreased. Cooled CCD cameras have a
better signal to noise ratio that non-cooled CCD
cameras. Noise in a digital image may also be
decreased by utilizing spatial filters.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 33 t:/classes/BMS602B/lecture 3 602_B.ppt
Filters such as averaging and gaussian filters will
reduce noise, but also cause some blurring of the
image. The use of these filters on high resolution
images is usually not acceptable. Median filters
cause minimal blurring of the image and may be
acceptable for some electron microscopic images.
These filters use a kernel such as a 3 x 3 or 5 x 5 to
replace the central or target pixel luminance value with
the median value of the neighboring pixels. The effect
is a blending of the brightness of the pixels within a
selection. The filter discards pixels that are too different
from adjacent pixels.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 34 t:/classes/BMS602B/lecture 3 602_B.ppt
Remove dirt and noise with a 3 x 3 median filter
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 35 t:/classes/BMS602B/lecture 3 602_B.ppt
Periodic noise in an image may be removed
by editing a 2-dimensional Fourier transform
(FFT). A forward FFT of the image below, will
allow you to view the periodic noise (center
panel) in an image. This noise, as indicated
by the white box, may be edited from the
image and then an inverse Fourier transform
performed to restore the image without the
noise (right panel).
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 36 t:/classes/BMS602B/lecture 3 602_B.ppt
Remove periodic noise with fast
fourier transforms
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 37 t:/classes/BMS602B/lecture 3 602_B.ppt
Pseudocolor image based upon
gray scale or luminance
Human vision more sensitive to color. Pseudocoloring
makes it is possible to see slight variations in gray scales
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 38 t:/classes/BMS602B/lecture 3 602_B.ppt
Image Analysis: After adjustment for contrast and
brightness, and noise, the next phase of the process is
feature identification and classification.
Most image data may be classified into areas that feature
closed boundaries (e.g. a cell), points - discrete solid points
or objects that may be areas, and linear data. For objects to
be identified they must be segmented and isolated from the
background. It is often useful to convert a gray scale image
to binary format (all pixel values set to 0 or 1). Techniques
such as image segmentation and edge detection are easily
carried out on binary images but may also be performed on
grayscale or color images.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 39 t:/classes/BMS602B/lecture 3 602_B.ppt
Simplest method for image segmentation is to use
thresholding techniques.
Thresholding may be performed on monochrome or color
images. For monochrome images, pixels within a
particular grayscale range or value may be displayed on
a computer monitor and the analysis performed on the
displayed pixels.
Greater discrimination may be achieved using color
images. Image segmentation may be achieved based
upon red, green, and blue (RGB) values in the image, or
a more powerful method is to use hue, saturation and
intensity (HSI).
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 40 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
intensity
The HSI method of color
discrimination is closer to how
the human brain discriminates
colors. Hue = is the
wavelength of light reflected
from or transmitted through
an object. Saturation = purity
of the color and represents
the amount of gray in
proportion to the hue - 0%
(gray) to 100% (fully
saturated). Intensity =
Relative lightness or darkness
- 0 (black) , 100 (white)
hue
0°
Slide 41 t:/classes/BMS602B/lecture 3 602_B.ppt
Image thresholding based on RGB or HIS
Hue – Saturation - Intensity
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 42 t:/classes/BMS602B/lecture 3 602_B.ppt
Threshold objects of interest
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 43 t:/classes/BMS602B/lecture 3 602_B.ppt
Preparation Techniques, stereo and 3D
Imaging
UPDATED February 2002
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 44 t:/classes/BMS602B/lecture 3 602_B.ppt
Characteristics of Fixatives
• Chemical Fixatives
• Freeze Substitution
• Microwave Fixation
Ideal Fixative
Penetrate cells or tissue rapidly
Preserve cellular structure before cell can react
to produce structural artifacts
Not cause autofluorescence, and act as an
antifade reagent
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 45 t:/classes/BMS602B/lecture 3 602_B.ppt
Chemical Fixation
• Coagulating Fixatives
• Crosslinking Fixatives
Coagulating Fixatives
• Ethanol
• Methanol
• Acetone
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 46 t:/classes/BMS602B/lecture 3 602_B.ppt
Coagulating Fixatives
Advantages
• Fix specimens by rapidly changing hydration state of
cellular components
• Proteins are either coagulated or extracted
• Preserve antigen recognition often
Disadvantages
• Cause significant shrinkage of specimens
• Difficult to do accurate 3D confocal images
• Can shrink cells to 50% size (height)
• Commercial preparations of formaldehyde contain
methanol as a stabilizing agent
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 47 t:/classes/BMS602B/lecture 3 602_B.ppt
Crosslinking Fixatives
• Glutaraldehyde
• Formaldehyde
• Ethelene glycol-bis-succinimidyl
succinate (EGS)
• Form covalent crosslinks that are
determined by the active groups of
each compound
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 48 t:/classes/BMS602B/lecture 3 602_B.ppt
Glutaraldehyde
• First used in 1962 by Sabatini et al*
• Shown to preserve properties of subcellular structures by EM
• Renders tissue autofluorescent so less useful for fluorescence
microscopy, but fluorescence can be attenuated by NaBH4.
• Forms a Schiff’s base with amino groups on proteins and
polymerizes via Schiff’s base catalyzed reactions
• Forms extensive crosslinks - reacts with the -amino group of
lysine, -amino group of amino acids - reacts with tyrosine,
tryptophan, histidine, phenylalanine and cysteine
• Fixes proteins rapidly, but has slow penetration rate
• Can cause cells to form membrane blebs
*Sabatini, D.D., et al, “New means of fixation for electron
microscopy and histochemistry. j. hISTOCHEM.cYTOCHEM. 37:61-65
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 49 t:/classes/BMS602B/lecture 3 602_B.ppt
Glutaraldehyde
• Supplied commercially as either 25% or 8%
solution
• Must be careful of the impurities - can change
fixation properties - best product from Polysciences
(Worthington, PA)
• As solution ages, it polymerizes and turns yellow.
• Store at -20 °C and thaw for daily use. Discard.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 50 t:/classes/BMS602B/lecture 3 602_B.ppt
Formaldehyde
• Crosslinks proteins by forming methelene bridges between reactive groups
• The ratelimiting step is the de-protonation of amino groups, thus the pH
dependence of the crosslinking
• Functional groups that are reactive are amido, guanidino, thiol, phenol,
imidazole and indolyl groups
• Can crosslink nucleic acids
• Therefore the preferred fixative for in situ hybridization
• Does not crosslink lipids but can produce extensive vesiculation of the
plasma membrane which can be averted by addition of CaCl2
• Not good preservative for microtubules at physiologic pH
• Protein crosslinking is slower than for glutaraldehyde, but formaldehyde
penetrates 10 times faster.
• It is possible to mix the two and there may be some advantage for
preservation of the 3D nature of some structures.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 51 t:/classes/BMS602B/lecture 3 602_B.ppt
Ethelene glycol-bis-succinimidyl
succinate (EGS)
• Crosslinking agent that reacts with primary amino
groups and with the epsilon amino groups of lysine
• Advantage is its reversibility
• Crosslinks are cleavable at pH 8.5
• Mainly used for membrane bound proteins
• Limited solubility in water is a problem
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 52 t:/classes/BMS602B/lecture 3 602_B.ppt
Fixation and preparation of
tissue
• Solutions
– 8% glutaraldehyde EM grade
– 80 mM Kpipes, pH 6.8, 5 mM EGTA, 2 mM MgCl2, both
with and without 0.1% Triton X-100 (triton for cytoskeletal
proteins)
– PBS Ca++/Mg++ free
– PBS Ca++/Mg++ free, pH 8.0
• When using glutaraldehyde 8% - open new vial, dilute to
0.3% in solution of 80 mM Kpipes, pH 6.8, 5 mM EGTA, 2 mM
MgCl2, 0.1% triton X-100. Store aliquots at -20°C. Never re-use
once thawed out.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 53 t:/classes/BMS602B/lecture 3 602_B.ppt
Fixation Protocol
pH-shift/Formaldehyde
• Method developed for fixing rat brain
• Excellent preservation of neuronal
cells and intracellular compartments
• Formaldehyde is applied twice - once
at near physiological pH to halt
metabolism and second time at high
pH for effective crosslinking
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 54 t:/classes/BMS602B/lecture 3 602_B.ppt
Method
• Solutions
–
–
–
–
–
40% formaldehyde in H2O (Merck)
80 mM Kpipes, pH 6.8, 5 mM EGTA, 2 mM MgCl2
100 mM NaB4 O7 pH 11.0
PBS Ca++/Mg++ free
PBS Ca++/Mg++ free, pH 8.0 (plus both with and
without 0.1% Triton X-100
– premeasured 10 mg aliquots of dry NaBH4
– see detailed methods page 314 of Pawley , 2nd ed.
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 55 t:/classes/BMS602B/lecture 3 602_B.ppt
Fluorescence Labeling
• There are no “standard” methods for all
cells - each cell type will be different.
• It is useful to use vital labeled specimens
to determine changes induced by the
fixation procedure
– e.g.: Rhodamine 123
[mitochondria]
– 3,3’-dihexyloxaccarbo-cyanine (DiOC6) [ER]
– C6-NBD-ceramide
[Golgi]
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 56 t:/classes/BMS602B/lecture 3 602_B.ppt
Examples of Fluorescent labels
DiI
DiOC6(3)
Bodipy ceramide
Fl tubulin
Rho phalloidin
Fl dextran
Rho 6G
Purdue University Cytometry Laboratories
Plasma membrane or ER
ER/mitochondria
Golgi
Microtubules
Actin
Nuclear envelope breakdown
Leukocyte labeling
© 1995-2004 J.Paul Robinson - Purdue University
Slide 57 t:/classes/BMS602B/lecture 3 602_B.ppt
Rhodamine 123
Rhodamine 123 staining mitochondria (endothelial cells)
Imaged on a Bio-Rad MRC 1024 scope
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 58 t:/classes/BMS602B/lecture 3 602_B.ppt
Test Specimen
• According to Terasaki & Dailey (p330,
Pawley, 2nd ed) a convenient test specimen
for a living cell is onion epithelium
• Stain with DiOC6(3) (stock solution is 0.5
mg/ml in ethanol. For final stain dilute
1:1000 in water
• Stains ER and mitochondria
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 59 t:/classes/BMS602B/lecture 3 602_B.ppt
Test Specimen - Onion
Peel off epithelium
Stain with DiOC6(3)
Modified from Pawley,
“Handbook of Confocal
Microscopy”, Plenum Press
Purdue University Cytometry Laboratories
ER and Mitochondria stained
© 1995-2004 J.Paul Robinson - Purdue University
Slide 60 t:/classes/BMS602B/lecture 3 602_B.ppt
Test images
Onion Fluorescence Images
Imaged on a Bio-Rad MRC 1024 scope
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 61 t:/classes/BMS602B/lecture 3 602_B.ppt
3D Imaging and 3D
Reconstruction techniques
# z sections =#images
y
z
x
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 62 t:/classes/BMS602B/lecture 3 602_B.ppt
3D Image Reconstruction
y
y
z
y
z
x
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 63 t:/classes/BMS602B/lecture 3 602_B.ppt
3D Image Reconstruction
y
y
z
z
x
y
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 64 t:/classes/BMS602B/lecture 3 602_B.ppt
© J.Paul Robinson - Purdue University Cytometry Laboratories
Fluorescent image of paper
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 65 t:/classes/BMS602B/lecture 3 602_B.ppt
© J.Paul Robinson - Purdue University Cytometry Laboratories
Pine Tree pollen - collected on a Bio-Rad MRC
1024 at Purdue University Cytometry Laboratories
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 66 t:/classes/BMS602B/lecture 3 602_B.ppt
Fly eye! - collected on a Bio-Rad
MRC 1024 at Purdue University
Purdue University Cytometry Laboratories
University
Cytometry- Purdue
Laboratories
1995-2004
J.Paul Robinson
University
Cytometry
Laboratories © J.Paul Robinson ©- Purdue
Slide 67 t:/classes/BMS602B/lecture 3 602_B.ppt
Collagen fibers collected using transmitted light and
fluorescence [collected on a Bio-Rad MRC 1024 at Purdue University Cytometry Laboratories ]
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 68 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 69 t:/classes/BMS602B/lecture 3 602_B.ppt
3D visualization tools
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 70 t:/classes/BMS602B/lecture 3 602_B.ppt
http://ioerror.ucsf.edu:8080/~dfdavy/Images/Images.html
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 71 t:/classes/BMS602B/lecture 3 602_B.ppt
Stereo Imaging
There are two methods for creating stereo pairs:
1. Use different colors for each image eg.
Anaglyphs of red-green which produce a
monochrome 3D image
2. Side-by-side display of both images
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 72 t:/classes/BMS602B/lecture 3 602_B.ppt
Creating Stereo pairs
Pixel shifting -ive pixel shift for left
+ive pixel shift for right
z
x
y
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 73 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
74Robinson
t:/classes/BMS602B/lecture
3 602_B.ppt
©Slide
J.Paul
- Purdue University Cytometry
Laboratories
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
75Robinson
t:/classes/BMS602B/lecture
3 602_B.ppt
©Slide
J.Paul
- Purdue University Cytometry
Laboratories
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
76Robinson
t:/classes/BMS602B/lecture
3 602_B.ppt
©Slide
J.Paul
- Purdue University Cytometry
Laboratories
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
77Robinson
t:/classes/BMS602B/lecture
3 602_B.ppt
©Slide
J.Paul
- Purdue University Cytometry
Laboratories
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide Robinson
78 t:/classes/BMS602B/lecture
3 602_B.ppt
© J.Paul
- Purdue University Cytometry
Laboratories
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 79 t:/classes/BMS602B/lecture 3 602_B.ppt
• Top: Endothelial cells (live) cultured on a
coverslide chamber. The cells were stained
with stain that identified superoxide
production (hydroethidine) and were color
coded (red =high stain, green =low stain)
then a 3D reconstruction performed and a
vertical slice of the culture shown. Here,
the original image was collected with
many more pixels - so the magnified image
looks better!
•
Purdue University Cytometry Laboratories
Left: Same endothelial cells with
hydroethidine stain (live cells) showing a
fluorescence reconstruction - note
fluorescence is only in nuclear regions - no
cytoplasm is stained. Imaged on Bio-Rad
MRC 1024 system.
© 1995-2004 J.Paul Robinson - Purdue University
Slide 80 t:/classes/BMS602B/lecture 3 602_B.ppt
Calculation and Measurement in
3D Imaging
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Depth and Thickness
Length, Area and Volume
Surface Area
Fluorescence Intensity
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 81 t:/classes/BMS602B/lecture 3 602_B.ppt
Depth and Thickness
• Axial resolution is related to the
square of the NA so use highest NA
lens possible
• Refractive Index (RI) must (should!)
be same between sample, and lens
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 82 t:/classes/BMS602B/lecture 3 602_B.ppt
Real and apparent depth
Focus shift
2 mm
Apparent depth
2 mm
Real depth
3 mm
Diagram modified from Confocal Microscopy: Methods
and Protocols Ed. Stephen Paddock, Humana Press
1999 p362 Fig 3)
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 83 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 84 t:/classes/BMS602B/lecture 3 602_B.ppt
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 85 t:/classes/BMS602B/lecture 3 602_B.ppt
Summary
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Structure of images & Image formats
Image Processing techniques
Image Analysis Techniques
Good confocal images require good preparation techniques
Preparations is the most significant factor in image quality
Preparation techniques can damage the 3D structure of
specimens
Quality control of specimen preparation requires attention to
protocols
To create 3D structure requires careful imaging
Stereo images are rapid techniques for visualization
Quantitative imaging requires accurate collection information
Purdue University Cytometry Laboratories
© 1995-2004 J.Paul Robinson - Purdue University
Slide 86 t:/classes/BMS602B/lecture 3 602_B.ppt