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VayTek's Homepage
Copyright All information on this World Wide Web site is copyrighted and may be reproduced only with permission from VayTek, Inc.
VayTek's Homepage
VayTek Website Links
VayTek offers Advanced Image Processing Systems, including both hardware and software for:
VayTek, Inc.
Tel 641-472-2227
VayTek's Homepage
VayTek Website Links
Copyright All information on this World Wide Web site is copyrighted and may be reproduced only with permission from VayTek, Inc. This site looks best with a monitor setting 800 pixels wide; and when viewed with one of the following browsers: Safari, Netscape Navigator or Internet Explorer. VayTek, Inc. Tel 641-472-2227
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Most of the problems associated with poorly deconvolved images are related to unprocessed images that are inappropriate for deconvolution. Most of those problems can be solved by adjusting image acquisition techniques. There are several issues that relate to image acquisition, including:
Each of these issues is discussed in detail below. 1. Background Noise and Dynamic Range The most important issues are background noise and dynamic range of the signal. VayTek's MicroTome software removes haze mathematically, whereas the confocal laser scanning microscope (CLSM) removes the haze with a pinhole or slit. The total energy in the signal from a CLSM is attenuated as the light passes through the aperture. To compensate for the dimmer image, the specimen is often scanned multiple times under the CLSM and the result is summed to give a brighter image. Thus, even though the final image from the CLSM has been attenuated, the user never sees the reduction in the dynamic range in the histogram. MicroTome, on the other hand, attenuates the dynamic range of the image after image acquisition as part of the haze removal. To compensate, the user can adjust the gain in MicroTome to make a brighter image. This adjustment multiplies the signal, but it also multiplies the background noise. 2. Use of Background Subtraction, Averaging and Integration The user can attenuate the background noise in the MicroTome image in two ways. During image acquisition, the unprocessed image must be adjusted to minimize background noise. This can be done by altering light levels, using appropriate filters, changing the gain and contrast enhancement settings on the camera, and using background subtraction and averaging during image acquisition. The second way to minimize background noise is to adjust the threshold of the image after it has been deconvolved. This can be done in IpLab Spectrum with the brightness and contrast controls. Most background noise is in the lower portion of the dynamic range and will easily disappear with this adjustment. Optimizing the dynamic range of the signal after the background has been minimized is the other major issue for capturing appropriate unprocessed images. This is particularly important for fluorescent images. An ideal signal will have 8 bit pixel values that range from 0 (black) to 255 (white). The user should create an image with the largest possible dynamic range in the signal. Images with a dynamic range of 30 or less will probably not deconvolve very well. The dynamic range can be increased by using a more sensitive camera and employing image integration during acquisition. A particularly effective technique is to subtract a background from an image before it is integrated. Also, the brightness and contrast enhancement controls of the camera can be adjusted. The techniques used to capture a good image for deconvolution will vary depending on the light source involved. For transmitted mode, most specimens will be opaque or semitransparent. The brightness and contrast controls of the camera should be turned down and the transmitted light increased to a maximum. A filter should be used to eliminate as much background interference as possible. A 546 green filter is particularly good for the sensitivity of CCD cameras. To capture a fluorescence image appropriate for deconvolution requires a camera with enough sensitivity to produce a relatively bright signal. In addition, background subtraction helps to eliminate some camera noise. The thickness of a specimen can also affect the quality of the unprocessed image. Thick specimens can be difficult to deconvolve, particularly if they are fairly opaque, and if the user is trying to focus fairly deep in the specimen. In addition, the quality of the deconvolved image will degrade progressively as the focus plane penetrates the specimen. This is true of both the CLSM and MicroTome. Maximum penetration will range from about 20 microns to 40 microns depending on the transparency of the tissue. Conversely, very thin specimens of 1 micron or less (particularly in transmitted mode) will not deconvolve well unless special care is taken during image acquisition. Specimens this thin usually have little haze to remove. If the user is working with very thin specimens, it is important to insure that the focal planes of the acquired images fall within the depth of the specimen. MicroTome can remove out-of-focus haze from any microscope image, including DIC, phase contrast, confocal laser scanned images and polarized light images. However, the degree of improvement in these images will vary depending on the amount of haze in the unprocessed image. This is particularly true for phase contrast, laser scanned confocal images and polarized light images. These unprocessed images generally tends to have less haze than fluorescent or transmitted light images. Some specimens that are opaque or reflective will tend to scatter light and produce haze that is not associated with the haze from the optical characteristics of the lenses. Neither the CLSM nor MicroTome can remove haze that results from light scatter. And finally, the distance between the unprocessed images will affect the quality of the deconvolved images. The distance between the optical slices is determined by the numerical aperture used. The following table gives the suggested range of distances for varying numerical apertures:
One question that is often asked by those who want to do deconvolution is "What resolution should I have for my camera?". This depends on your lens magnification, na, the wavelength of light, the camera chip dimensions, and the camera coupler. It is easy to compute. We have the formula for calculating the resolving power of a lens: 1) d = (Lambda/2) * NA where The resolvable distance at a conjugate image plane where the camera chip is placed can be calculated with 2) D = d * M where In order to get the maximum resolution in the image on the camera chip it is necessary to capture two pixels worth of data. The number of pixels per mm can be calculated as: 3) R = 2/D where Combining formulae 1, 2 and 3 we get the formulae: R(x) =( (N * NA) / M) * (X/T) Where For example, a 100x, 1.4 na lens projection onto a 2/3" (8.5 x 6.4 mm) camera chip, using a .63 camera coupler and red light (Lambda = .6) would require a camera with a resolution of 760 x 570 pixels. Table of Resolving
Power of Zeiss Objectives What is the best camera for deconvolution? The two most important issues
you must consider to answer this question are: If you are working with specimens in brightfield
or transmission mode, then you can use most any camera. The less
expensive, analog cameras will have more noise, however, and
create images that will not deconvolve quite as well as images
captured with digital cameras. The instances in which VayTek
recommends an analog camera are limited to users who have small
budgets, who need the analog camera for live recording, and who
will be doing only occasional deconvolutions, usually for improving
images for publication. Images captured with analog cameras are
confined to the nearest neighbor algorithm. Deconvolution is really designed for fluorescent
specimens. This is because the mathematical formulae for the
deconvolution algorithms assume the light in the specimens radiates
equally in all directions. Consequently, the implications of
specimen characteristics for choosing a camera become more obvious. If you are working with very bright fluorescent
specimens, then you may still be able to work with an analog
camera, although the analog camera will not give the best results
for deconvolution. A digital camera will be a much better choice,
even for bright specimens. There are several reasons for this. Digital cameras have better dynamic ranges
than analog cameras - more gray levels mean more accurate deconvolution.
Digital cameras, which are usually cooled, have lower noise and
are better for deconvolution - the mathematics of deconvolution
will amplify noise in an image so it is important to minimize
it during acquisition. Digital cameras integrate over time, thereby
letting you work with a wide variety of fluorescent dyes and
intensities of dyes. And finally, digital cameras usually have
more pixels on the chip and yield images with higher resolution,
often at the resolution limits of the microscope. If you are planning to conduct experiments
in which you will be measuring the intensity values of the pixels
in the image, you will need to use the constrained iterative
algorithm for deconvolution, probably with a measured point spread
function. This means you will most certainly need a cooled, digital
camera for data acquisition. Another important specimen-related issue to
consider when selecting a camera for digital deconvolution is
the spectral response of the camera. CCD chips have a characteristic
response curve depending on the color of the light. For example,
the Sony interline chips are more sensitive in the blue-green
region of the spectrum, and less sensitive in the red spectrum.
If you are doing calcium imaging, then you will probably favor
a camera that uses the Sony chip. The Kodak chip, however, is
more sensitive the red region. This camera is especially good
for dyes like Cy3 and Cy5. You can check the spectral response
curve of several cameras by consulting the spread sheet on our
web site. You should consider this issue carefully.
If you have to integrate longer, then the specimen may bleach
over time. This is especially true if you are attempting to acquire
a stack of images for 3D reconstruction. Camera acquisition and readout speed are other
important issues. Different cameras have different readout speeds.
If you are working with fixed specimens and dyes that do not
photobleach easily, then you can use a camera with slower readout
times. The analog cameras work at 30 frames/second, but have
noisy images. The cooled digital cameras' readout speeds vary
from 1 Mhz (considered slow) to 20 Mhz, or higher (for IEEE 1394
Firewire). A 1 Mhz camera will usually produce a full frame image
(about 1 million pixels) in about 1.5 seconds. On the other end
of the spectrum, the 20 Mhz, or faster, cameras can operate at
10 frames/second or better. Note that a faster readout increases
the noise in the image. If you bin an image, or take a subregion,
then the frame rates can approach 100 frames/second, or better. If you are working with live specimens, doing
calcium ratio imaging, or working with specimens that photobleach
easily, you will want the greatest sensitivity and the fastest
readout times. Your specimens are related to the noise and
bit depth of the camera you choose. If your specimens are dim
and you have to integrate a long time, or you have to capture
images quickly, then the noise will be greater in the images.
The noisier the image, the less accurate the deconvolution. To
minimize camera noise under these circumstances, select a camera
with the lowest cooling temperature, the largest well capacity,
the lowest dark current and the lowest read noise. It is sometimes difficult to compare cameras
on this issue. There is considerable confusion as to the meaning
of bit depth and noise. All chips have a bit depth rating from
the manufacturer. For example the Sony interline and the Kodak
KAF chips are both 12 bit chips. However, the effective bit depth
of an image from these two chips is quite different. The Sony
chip has a well depth of about 18,000 electrons. Given that it
has a readout noise of about 9 electrons per well, the effective
signal-to-noise ratio is about 18,000/9 or 2,000. This means
the camera can produce an image with about 2000 shades of gray
- or an image with a true bit depth of about 11 bits - not the
rated 12 bits. On the other hand, the Kodak chip has a well
depth of 40,000 electrons and a read noise of about 10 electrons.
This gives it a true bit depth of 40,000/10 = 4000 or 12 bits. Visually, the images from these two chips
are very similar. However, if you perform an exacting deconvolution
process on images from these two cameras, you will see slight
differences in the results. This is even more obvious if you
are working with dim specimens and the dark current noise builds
up over time and adds to the readout noise. Thus, if you are working with very dim specimens,
it is better to select a camera with a higher well depth, even
if it means sacrificing readout speed. If you are working with color specimens, or
multiple fluorescent dyes, you may want to capture color images.
This is essential if you are doing colocalization studies. Again, if you are working in brightfield,
you may be able to use an analog camera, but with the caveats
mentioned above. Many customers are now attempting to use color
cameras for multiple fluorescent dyes. For general imaging, color
cameras are acceptable. However, VayTek is reluctant to recommend
a color camera for most deconvolution applications. For color
fluorescent imaging, and color deconvolution, it is VayTek's
opinion that the best approach is to use a high-quality 12 bit,
cooled digital camera and an automated filter wheel. There are
several reasons for this. Color cameras create color images in four
different ways: All color cameras with filters use broadband
filters for red, green and blue. On the other hand, a microscope
with a monochrome camera and a filter wheel captures color images
that are more accurate because they use a filter wheel with the
glass filters designed specifically for the wavelengths of your
specimen dyes. If you use a color camera to collect color
fluorescent images you are adding additional filters in the optical
path, which causes some additional loss of light and increases
integration time. The exact amount of the loss depends on which
color camera is used. The color cameras generally take longer to
capture three separate channels and merge them into a single
color image than does an excitation filter wheel and 12 bit monochrome
camera. And finally, the color cameras are not as
well adapted for precise fluorescent imaging and some of the
demanding results required from color images. For example, the
color cameras with mechanical filter wheels can introduce vibration
into the camera and cause slight misalignment in the color channels.
The Bayer filter approach results in a loss of resolution. The
acousto-optic filter reduces light transmission by about 30%. In short, for the purpose of deconvolution,
you will have more control of the acquisition process, better
control and more accurate images with a good monochrome camera
and an excitation filter wheel. Many customers try to buy a single camera
that will serve all purposes. This is often done for budgetary
considerations. They want a cooled camera that produces 12 bit
monochrome images, or better, for accurate deconvolution. In
addition, they want the camera to be fast enough for calcium
or live imaging, and they want to do multichannel or color imaging.
They often want the camera to double for brightfield applications.
And most of all, they want to pay less than $5,000 for it. Needless to say, such a camera does not yet
exist. In fact, the biggest problem VayTek has had to contend
with in integrating full systems is this one-camera-for-everything
syndrome. The problem has become even more complex with the introduction
of several less expensive color cameras that use the Sony interline
chips. Some of these cameras are very good and are much more
versatile than previous color cameras. However, each of these
cameras still has several drawbacks that make them less than
desirable for serious deconvolution applications. The second biggest issue affecting the choice
of a camera, and the one that is most often ignored, or at least
put off until the last, is the choice of software to be used
with the camera. When considering software, you need to determine
if the program will control the camera properly and give you
all the functionality you are looking for. For example, the camera
may be rated at 10 frames per second, but if the software is
not written to operate at that speed, then you will not be able
to run the camera at 10 frames per second. The next issue to consider when evaluating
software is how comprehensive it is. Many camera companies offer
software packages with their cameras. However, this software
usually has minimal functionality. Most of these programs, for
example, do not integrate z stage motors, shutters or filter
wheels. More complex experiments will also need software control
of triggers and annunciators. Many customers overlook these points. This
leads to the buy-my-own-parts syndrome. When customers begin
to shop for a system, they often concentrate on the hardware
first. They believe they can save money by getting the best deal
on a microscope, then the best deal on a camera, then the best
deal on a computer, etc. After they have saved a few thousand
dollars by getting the best deals, they then expect that all
they need is a piece of software to capture some images and process
them. Unfortunately, this approach often ends up
costing the customer more in the long run. The software is key
to pulling all the parts of the system together. There are many
details, problems and exceptions to getting complex hardware
to function together as a system. The best approach is to determine
the uses for the system, then talk to an expert about integrating
a system around those uses. The most typical process is to determine
the research issues and specimens. Then, settle on the microscope
and then the camera. This will determine the type of computer,
framegrabber board and operating system. Next, a complete understanding
of the acquisition paradigms and the analysis software is needed.
Finally, if any peripherals, such as filter wheels and stage
motors, are needed they can be added to the mix. It is important to work with someone who has
a detailed understanding of microscopes, optics, cameras, microscope
peripherals, computers, software, biomedical research, statistics
and research design. Such a person will understand which camera
is best for your research needs. They will also understand that
picking a camera should come early in the process and will determine
many of the other components. See CCD Camera Application
Note for more information. See http://www.omegafilters.com/
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