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Choosing a Microscope Camera

Table 2 : Magnifi ed microscope image projected onto a 6.5 µm 2 camera pixel. Lens

magnification 40× 63×


Magnification at camera

50× 78×


Lens NA 1.4 1.4 1.4

Microscope resolution

~ 0.2 µm ~ 0.2 µm ~ 0.2 µm

Magnified resolution

10 µm

15.6 µm 25 µm

Acceptable sampling?


Yes Yes

Note: A 40× NA 1.4 lens on a microscope with a 1.25× magnifi cation factor at the camera (total magnifi cation 50×) would project a 0.2 µm object (the smallest object that can be resolved at NA 1.4) as a 10 µm diameter image on the camera. This would not be suffi ciently sampled by a standard 6.5 µm × 6.5 µm photodiode. Adequate sampling is possible with the 63× and 100× objective lenses.

Obviously, the microscope magnifi es the image of an object onto the camera sensors. T us, one must consider how large the image of the smallest resolvable object in the specimen has become when it is projected onto the cCCD or sCMOS photodectors. If one wants to capture the full resolution available with a particular lens, one needs to make sure that the camera chosen is capable of sampling the full resolution. Table 2 compares the eff ect of image magnifi cation on the sampling of a diff raction-limited spot. All the lenses listed have a numerical aperture (NA) of 1.4 and are capable of resolving a structure about 0.2 µm in diameter. In this example, the microscope provides an additional 1.25× magnifi cation over that of the objective lens. T us, a 40× lens producing 50× magnifi cation would project a 0.2 µm spot to a spot with diameter of 10 µm. T e Nyquist-Shanon theorem indicates that this 10 µm diameter object would need to be sampled at least two times in the X direction and two times in the Y direction. A sensor size of 5 µm × 5 µm or less would be necessary to adequately sample this projected image. So a 6.5 µm × 6.5 µm photodetector would not capture the full diff raction-limited resolution of the lens. However, a 63× or 100× lens would project an image that was adequately sampled at greater than the Nyquist frequency. If your sensor size is smaller than that required to adequately sample the smallest object of interest at the Nyquist frequency (such as the 100× lens in Table 2 ), you can improve the signal- to-noise ratio of your image by binning the pixels. T is involves combining the signal from multiple sensors into one “super

pixel.” Usually this involves combining a 2 × 2 array of pixels ( Figure 3 ). T is increases the possible maximum signal by a factor of 4 but also reduces the spatial resolution by 50 percent [ 5 ]. Binning of CCD pixels also allows for faster readout of the information. With CMOS systems, since the binning occurs aſt er image readout, there is no increase in speed with binning. Most sensors developed for microscopy are equipped with the ability to bin pixels, but you should confi rm this by testing the binning capability of any system before purchase. Frame rate . T e frame rate of a camera refers to the number of full frames that can be captured per second. Digital camera systems allow you to adjust the frame rate up to some maximum. T is is where the parallel processing design of CMOS cameras clearly has an edge. Maximum frame rates for CMOS systems will be faster than for CCD systems, although some clever tricks have improved CCD frame capture rates. For both systems, however, faster frame rates mean reduced exposure time so the signal-to-noise ratio decreases as you increase the frame rate. Quantum effi ciency . Quantum effi ciency (QE) is a measure

Figure 3 : Example of binning. When the full lateral resolution of a sensor array (either CCD or CMOS) is not required, the charge from several sensors can be combined into a single value. This is called binning. The yellow sensors indicate a 2 × 2 binning to create a “super” sensor. By combining the signal from 4 adjacent photosensors, the signal-to-noise ratio is increased.

2017 September •

of the eff ectiveness of the conversion of photons to charge. If every photon were converted to an electron, the QE would be 1 (100%). T e quantum effi ciency of a sensor is not uniform across the spectrum. Both CCD and CMOS detectors tend to have less QE at the ends of their usable wavelength ranges. Several years ago, the QE of CMOS cameras was signifi cantly worse than for CCD systems, but the most recent CMOS designs rival the QE of CCD sensors. At their maximum, QE values are about 0.6. Spectral response . T e spectral response of the camera refers to how well specifi c wavelengths are detected by the sensor ( Figure 4 ). Both CCD and high-end (scientifi c) CMOS sensors are sensitive to wavelengths between 400 nm and 1000 nm. However, since the quantum effi ciency is not uniform across the entire spectrum, one should always check the spectral response curve of the specifi c system you are considering. T ere is usually some sensitivity in the near-infrared (NIR) range if you need to detect signals in this range. T e CMOS sensors tend to be better in this range than do CCD sensors. Dynamic range . T e dynamic range of a camera indicates how many separate and distinct gray levels the camera is capable of detecting and storing. In other words, it indicates the lowest light level the sensor can detect and how many photons a sensor can collect before it is saturated. If there is a large diff erence between the lowest light detectable and saturation, it is easier to discriminate subtle diff erences in the light coming from


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