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Compare Cameras Supplemental Information

What is the Best Camera for Deconvolution?

(see "What Resolution...." at bottom of this page for answers to resolution issues)

What is the best camera for deconvolution? The two most important issues you must consider to answer this question are:

the characteristics of your specimens and
the software you will use to integrate your system.

Analog cameras

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.

Digital cameras

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.

Spectral response and sensitivity

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.

Speed

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.

Noise and bit depth

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.

Color cameras

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:

with a mechanical color filter wheel
with an electronic filter (e.g., acousto-optic)
by masking a chip and using a Bayer filter
using three separate chips, one for each red, green, blue channel.

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.

The One-Camera-for-Everything Syndrome

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.

Software

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.

The Buy-My-Own-Parts Syndrome

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/ for information about filters.

WHAT RESOLUTION SHOULD I HAVE FOR MY CAMERA?

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
d = the smallest resolvable distance between two points under the lens
Lambda = the wavelength of light
NA = the numerical aperture of the lens

The resolvable distance at a conjugate image plane where the camera chip is placed can be calculated with

2) D = d * M

where
D = is the resolution at the image plane
d = resolving power of the lens
M = the magnification of the lens

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
R is the maximum camera resolution in pixels/mm

Combining formulae 1, 2 and 3 we get the formulae:

R(x) =( (N * NA) / M) * (X/T)
R(y) =( (N * NA) / M) * (Y/T)

Where
R = the number of pixels required on the camera in the x or y direction
N = 4000/Lambda (in microns)
NA = the numerical aperture of the lens
M = the magnification of the lens
X = the chip size in the x direction in mm
Y = the chip size in the y direction in mm
T is the reduction or magnification factor of the camera coupler

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.

Click here to view Zeiss resolving power table.

CONVERTING SIGNAL TO NOISE FROM DB (DECIBLES)

Here's how you can convert Signal to Noise from dB (decibels) into ratios and back again.
Convert dB to ratio
Divide the dB number by 20. Use this number as an exponent of 10 to find the value of the ratio to 1.
Example: 60 dB / 20 = 3. 10^3 = 1000. Ratio is 1000:1

Convert ratio to dB
Find the log of the ratio and multiply by 20
Example: 2000:1 2000 = 10^3.301. 3.301 x 20 = 66 dB

See our definitions of camera specifications for more information relating to signal-to-noise, chip size, etc.

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