Digital Fluorescence Imaging Using Cooled CCD Array Cameras invisible Lucas J. van Vliet1, Damir Sudar2, and Ian T. Young1 1) Pattern Recognition Group, Faculty of Applied Physics, Delft University of Technology Lorentzweg 1, 2628 CJ Delft, The Netherlands 2) Life Sciences Division MS 74-157, Lawrence Berkeley National Laboratory 1 Cyclotron Road, Berkeley CA 94720, U.S.A. e-mail:
[email protected] www: http://www.ph.tn.tudelft.nl/~lucas
Abstract The progress in solid state technologies has led to a range of CCD cameras from inexpensive video cameras to expensive scientific digital camera systems. The best choice (best value for money) depends heavily on the application. A state-ofthe-art camera’s performance is limited by the fundamental laws of nature. The various noise sources (photon statistics, thermal, readout and quantization) are examined to help you understand some specific operating conditions such as cooling and the choice of readout rates. 1. Introduction Quantitative microscopy is more than putting a camera on top of a microscope to produce pretty pictures for reproduction or archiving. Its goal is to measure a wide variety of “analog” quantities from digitized data as accurately and precisely as possible. Advances in molecular biology and biochemistry have made it possible to selectively tag specific parts of cells or cellular constituents. For example, using fluorescence in situ hybridization (FISH) specific target sequences of DNA molecules can be fluorescently labeled. For very small sequences these signal are very localized and extremely weak. To facilitate imaging we need state-of-theart instrumentation and image analysis software. An item that is frequently overlooked is the image sensor. This paper focuses on the properties of scientific CCD array cameras suitable for low-light level quantitative fluorescence microscopy. It is neither a comprehensive market survey nor a buyer’s guide. As of today, several manufacturers of high quality scientific CCD cameras exist. Many of them have an exhibit at major conferences or advertise in scientific and trade journals. This paper may help you decide which properties are useful for your application in fluorescence microscopy. More details about techniques for CCD camera characterization that are also used in this paper can by found in (Mullikin at al. 1994). Other reading material includes an introduction into image sensors, image formation and image processing (Castleman 1996), principles of fluorescence microscopy (Young 1989; Inoue 1986), and a general description of scientific CCD cameras (Aikens 1989).
L.J. van Vliet, D. Sudar, and I.T. Young, Digital fluorescence imaging using cooled charge-coupled device array cameras, in: J.E. Celis (eds.), Cell Biology, Second Edition, Volume III, Academic Press, New York, 1998, 109-120.
2. Light: waves and particles CCD cameras are used in light microscopy as an image sensor. Before exploring the various properties of CCD cameras, we will explain the physical descriptions of light that are needed to understand the process of image formation and image acquisition. Physics teaches us two descriptions of light: as waves and as particles. Both descriptions of light yield fundamental limits to the quality of the image that can be observed. A state-of-the-art camera neither reduces the optical resolution below the diffraction limit, nor does it increase the noise level above the photon noise. 2.1 Wave description of light The wave description of light allows us to explain diffraction. Diffraction limits the optical resolution of a microscope. An ideal lens is a lens without aberrations. Such a lens allows us to model the imaging process as a linear shift-invariant (LSI) system followed by a pure magnification system. The definitions and consequences of LSI systems are discussed in (Castleman 1996 and Young 1989). The first system can be fully characterized by its impulse response: the point spread function (PSF). Each point source in the object plane is replaced by a scaled and translated PSF in the image plane. The PSF depends on the size and shape of the lens aperture and the wavelength of light being imaged. A circular aperture yields a circularly symmetric PSF – also called Airy disc (cf. Figure 1). Each impulse response has a corresponding transfer function – they form a Fourier transform pair. The optical transfer function (OTF) shows how the spatial frequencies pass through the optical system.
PSF r
2 J1 a r = ar
2
2 cos −1 ω −
ωc OTF ω = π 0
2π NA ,a = λ
2 ω 1 − ω
ωc
ωc
,
ω ≤1 ωc
,
ω >1 ωc
Figure 1: The PSF or Airy disc (left) and the OTF (right) of diffraction limited optics. All the light that enters the objective lens contributes to the image, OTF(0) = 1. A uniformly illuminated field passes through the systems unaltered. Any object can be thought of as a linear superposition of spatial frequencies, which pass through the optical system with reduced amplitude. The OTF of the lens is bandlimited, i.e. there exists a highest frequency ωc (ωc = 2a = 4πNA/λ ) above which the OTF equals zero. The lens acts as a low-pass filter with cutoff frequency ωc. A higher cutoff frequency yields a “crisper” image. The cutoff frequency is proportional to NA/λ. For example, an oil immersion lens with NA=1.25 and green light with a wavelength of 500 nm yields a cutoff frequency of ƒ c = 5.0 cycles per micron. The Nyquist sampling theory requires the number of image samples per micron to be greater than twice the cutoff frequency. Satisfying this condition guarantees that the analog image (also visible through the eye-pieces of the microscope) is accurately represented by the 2
image samples. For the above example with a cutoff frequency of 5.0 cycles per micron, the Nyquist sampling rate is 10 samples per micron. The microscope camera combination needs to satisfy this spatial sampling rate. The overall optical magnification divided by the pixel size needs to be larger than the Nyquist rate. This favors CCD’s with small pixels over CCD’s with large pixels. 2.2 Quantum nature of light The quantum nature of light explains the noisy images that often occur in low light-level situations. Light is now considered as a series of particles called photons. Each photon carries a certain amount of energy, E = hν = hc/λ, where h is Planck’s constant from quantum mechanics and c the speed of light. Scientific CCD cameras are sensitive enough to detect, store, and count individual incoming photons per pixel. Photon production by any light source is a statistical process governed by the laws of quantum physics. The source emits photons at random time intervals. The number of photons in a fixed observation interval will result in a number that obeys Poisson statistics. The probability distribution for counting p photons in an observation window of T seconds is displayed in Figure 2
0.15 0.12 0.09
P (p ρ T )=
(ρT ) e − ρT
16
20
p
p!
0.06 0.03 0.00 0
2
4 6 8 10 12 14 actual number of photons p
18
22
24
Figure 2: Poisson distribution for an expected number of photons ρT =10. with ρ the photon flux in photons per seconds. The product ρT yields the expected number of photons. However, instead of the expected number of photons, each observation will measure a number p with a probability given by P(p|ρT). The average of a large number of observations will approximate the expected photon production ρT. 3. Introduction to CCD cameras A charge-coupled-device (CCD) camera is a semi-conductor device that acts as transducer between incoming light and electrical charge. It consists of a rectangular array of pixels (see Figure 3). Both the size and the number of pixels may vary widely among today’s CCD chips. After production each chip receives a quality mark which depends on the number of “bad” pixels and other blemishes.
3
(serial) readout register
on-chip pre-amplifier
off-chip AD converter ADC
parallel register or pixel array pixel
discrete pixel data in AD units (ADU)
photons potential well to store electrons
parallel transfer of collected electrons
thermal vibration full frame CCD
vertical pixel size
photo-sensitive area “dead” space between pixels horizontal pixel size Figure 3: Layout of a full frame CCD. In order to understand how the camera functions, we have to work with the quantum description of light. An incident photon of sufficient energy can release an electron from the valence band into the conduction band by creating a so called electron-hole pair. The freed electrons called photo-electrons are collected in potential wells. After a certain integration time, all charge is shifted towards the serial register. Pixel by pixel the charge is amplified and transformed into an electrical signal. This electrical signal is then converted into a discrete number by the A/D converter. In scientific cameras, there exists a one-to-one relationship between the CCD’s potential wells and the pixels of a digitized image. Video cameras deliver an analog video signal that can be connected to a variety of digitizers. Another quality of 4
scientific CCD’s is their high fill factor. A fill factor of 100% means that the entire pixel surface on the CCD is photo sensitive, i.e. there is no dead space between adjacent pixels. The fixed spatial organization avoids geometric distortions such as pin-cushion. Also, assuming the proper lens configuration, there is no vignetting near the corners of the CCD. Unfortunately, not all photons that reach the CCD are converted into electrons. Like the human eye, the CCD has a sensitivity curve, which is wavelength dependent. We limit ourselves to monochrome cameras. Color images can be acquired using appropriate filters in the imaging system. 4. Properties of CCD cameras A variety of properties are important for applications in quantitative fluorescence microscopy. To measure the amount of fluorescence, the camera needs to have a linear response. If weak signals require long integration times the camera needs cooling to suppress dark current. Why does my camera have a low readout rate? How many photons contribute to a single output unit? Is my camera photon limited? All these questions, and more, will be answered below. 4.1 Noise sources All acquired images will be contaminated by noise from a variety of sources. Noise is a stochastic phenomenon that cannot be compensated for as opposed to systematic distortions such as shading or some forms of image blur. The noise sources that play a role in scientific CCD cameras are: photon noise, thermal noise (dark current and hot pixels), readout noise (amplifier noise, on-chip electronic noise and KTC noise), and quantization noise. Some of these noise sources can be made negligible by proper electronic design and careful operating conditions. One of them – photon noise – can never be eliminated and thus forms the limiting case when all other noise have become negligible compared to this one. 4.1.1 Photon noise Photon noise is unavoidable and caused by a fundamental law of nature – the quantum nature of light. The photo-electrons, n, generated by incident photons inherit their statistical properties, i.e. Poisson distribution. The Poisson distribution has a fixed relation between its expected value and its variance, E(n) = var(n). Even if the photon noise were the only noise source, the signal-to-noise ratio would still be finite. It improves slowly with increasing photon (photo-electron) counts, i.e. more light or longer integration time. Thus the ideal SNR becomes SNRphoton = 10 log(n) dB It is important to remember that photon noise is not independent of the signal, not additive, and not Gaussian. The maximum SNR is limited by the well capacity. The well capacity is proportional to the pixel area and, with current technology, an photoelectron density given by about 700 e– µm–2. A chip with small pixels 6.8 µm x 6.8 µm has a SNRmax = 45 dB, whereas a chip with large pixels 23 µm x 23 µm yields a maximum SNRmax = 56 dB. 4.1.2 Thermal noise: dark current and hot pixels Thermal noise or dark current refers to the creation of electron-hole pairs due to thermal vibration. These thermal-electrons cannot be distinguished from photo-electrons. This dark current is a stochastic process and yields a Poisson distribution for the number of thermalelectrons generated in a fixed time-interval. The production rate of thermal-electrons is an increasing function of temperature. Dark current reduces the dynamic range of a pixel and adds
5
(ADU s–1 pixel–1)
a substantial amount of noise. Thermal noise can be greatly reduced by cooling the CCD chip (see Figure 4). The dark current reduces by a factor of two for every 6°C reduction of temperature. Cooling down to –40°C can be achieved using Peltier elements, which themselves need to be cooled by air or liquids such as ethylene glycol. Cooling below 4°C requires a vacuum around the CCD chip to avoid condensation. Air cooled cameras with operating temperatures around 4°C should not be used in areas with a high humidity. Note that an airconditioned room has typically a very low humidity. Some CCD chips can be operated in a special accumulation mode called multi-phase pinning, MPP. This technique may reduce the average dark current significantly in exchange for a smaller potential well for storing electrons. 1000
100
10
dark current
1
0.1
0.01 –60
–40
–20
0
20
temperature
40
(ÞC)
Figure 4: Dark current measurements. Average dark current (in ADU per second per pixel) as a function of temperature. The pixel size is 23 µm x 23 µm and each ADU level corresponds to about 90 thermal-electrons. Table 1: Some statistics of dark images (shutter closed) acquired using a non-cooled CCD camera with MPP to reduce dark current. The hot pixels have a dark current of 20 times the mode of the ordinary “cold-pixel” dark current distribution. integration time (s) 0 1 5 10 50
“cold” pixels 88 90 100 114 159
mode 103 106 114 138 193
“hot” pixels 119 149 459 815 1863
Due to impurities in the CCD’s silicon layer, some pixels severely suffer form dark current. They build up thermal-electrons at a much faster rate (often up to a hundred fold) than the majority of pixels. After a few seconds of integration on a non-cooled camera a dark image looks like an image with stars on a clear night (see Table 1 and Figure 5). Cooling the CCD also reduces that impact of hot-spots or hot-pixels by the same amount as the average dark current.
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Figure 5: Hot pixel measurements. Dark images of respectively 1, 5, 10, and 50 seconds of integration acquired using a non-cooled CCD camera with MPP to reduce dark current. In all images, black refers to zero and white refers to 255. All pixel values higher than 255 have been clipped. 4.1.3 Readout noise: on-chip electronics, pre-amplifier and KTC noise This noise originates in the process of reading the signal from the sensor. It is caused by the CCD’s on-chip electronics and strongly depends on the readout rate. For extremely low readout rates the noise has a 1/f -character. For moderate readout rates the readout noise is minimal and about constant. Scientific cameras usually operate in this range [20kHz, 500kHz]. For high readout rates the readout noise increases and becomes a significant component of the overall noise. The readout noise is additive, Gaussian distributed and independent of the signal. It is therefore expressed by its standard deviation (RMS value) in number of electrons. At low readout rates the readout noise may be as low as a hand full of electrons. At high readout rates (video speed = 15 MHz.) it can be as high as a few hundred electrons. Figure 6 shows the histogram of intensity levels for a dark image (shutter closed) with zero integration time and a readout rate of 4 MHz. Readout noise is the only noise source that contributed to this image. Readout noise originates from several on-chip sources, the pre-amplifier and the field effect transistor, FET. Pre-amplifier noise is generally negligible in well-designed electronics. KTC noise (associated with the gate capacitor of a FET) can almost completely be eliminated by employing correlated double sampling and dual slope integration.
Readout noise
90
100
110
120
intensity level (ADU) Figure 6: Distribution of readout noise for a 12 bit CCD camera. The readout rate is 4 MHz. The readout noise with an RMS error of 3.9 ADU (= 39 e–) sits on top of an offset of 103 ADU. Each ADU corresponds to about 10 electrons. 7
4.1.4 Quantization noise Quantization noise is inherent to the quantization of the pixel amplitude in a finite number of discrete levels by the analog-to-digital converter, ADC. The ADC converts the amplitude of an electronic signal into a binary representation, a pixel value. The associated round-off errors are called quantization noise. This noise is additive, uniformly distributed [–0.5, +0.5], and independent of the signal. The SNR for quantization noise is SNRqn = 6b + 11 dB, with b the number of bits. Scientific CCD cameras use a high quality ADC with 8 to 16 bits. Quantization noise is very small and usually ignored. A summary of the noise sources is presented in Table 2. Table 2: Summary of noise sources. Noise Photon
Distribution
Dependent on
Poisson
SNR
Remarks
n
signal
Unavoidable! SNR increases with signal. Effectively suppressed by cooling.
10 log(n) dB Thermal
Poisson
nt
temperature and integration time
Readout
Gaussian, additive
readout rate
Quantization
uniform, additive
number of bits in ADC
10 log(nt) dB rms = 5-10 e– @[20,500] kHz 2b / 1 12
Noise increases rapidly with readout rate. Negligible for ADC with ³ 8 bits.
11 + 6b dB
(ADU)
4000
3000
grey-value
2000
1000 gain setting 1× gain setting 4× 0 0
2
4
exposure time
6
8
10
(s)
Figure 7: Linearity of photometric response for two different settings of the (electronic) camera gain. The pixel values (in ADU) as a function of the integration time for a scientific CCD camera cooled to –40°C. 4.2 Linearity of photometric response The pixel values (in ADU) should be linearly proportional to the number of captured electrons and thus to the amount of incoming light. Continuous CCD readout contains mainly readout noise. A small offset (typically around 50 to 100 ADUs for a 12 bits digital signal) prevents the 8
clipping of the signal at the lowest value. A proper setting of the electronic gain guarantees that the ADC’s dynamic range stays within the linear working range of the CCD. In Figure 7 we show that this excellent linearity is present over the entire range of light levels. 4.3 Signal-to-noise ratio As described above all images are contaminated by noise. Table 2 states that all noise sources can be effectively suppressed or avoided except photon noise. The SNR of a state-of-the-art CCD camera should therefore be photon noise limited. Photon noise is the only noise source that depends on the signal. Due to the properties of its Poisson distribution, the pixel variance, var(l), should increase linearly with the pixel value, l, as is true for the camera in Figure 8. The slope is equal to the camera gain: var l g 2 • var n = =g l g•n
The SNR of this slow-scan (500 kHz) cooled CCD camera is photon limited over almost the entire range of light levels because the slope of Figure 8 is linear over the entire range of light levels. Note that we have measured the variations of individual pixels. 2000 (ADU2 )
(dB)
50
SNR
40
1000 variance
gain setting 1× gain setting 4×
30 gain setting 1× gain setting 4× 20
0 0
1000 grey-value
2000
3000
4000
(ADU)
0
1000 grey-value
2000
3000 (ADU)
Figure 8: SNR of a slow-scan (500 kHz) cooled (–40°C) CCD camera for two different settings of the electronic gain. left) The pixel variance as a function of pixel value. right) The SNR as a function of pixel value. Measuring the image variance instead of the pixel variance yields a completely different result. The pixel variation is much larger than the Poisson noise of individual pixels. The pixel variation is caused by a difference in response of the individual CCD wells to the same amount of light. This is clearly demonstrated in Figure 9. Here the image SNR before calibration never exceeds 30 dB SNR. To achieve the photon limited behavior, we need to calibrate the image. Image calibration corrects the image for differences in pixel response (see section on image calibration and flat-field correction).
9
4000
(dB)
50
40
30
image SNR after calibration
SNR
20
image SNR before calibration photon limited SNR 10 0
1000
2000
intensity
3000
4000 (ADU)
Figure 9: SNR before and after calibration, to correct for differences between pixels. 4.4 Sensitivity CCD sensors, like many other sensors, are not equally sensitive to all wavelengths of light (see Figure 10). The spectral sensitivity for CCD sensors is called the quantum efficiency, QE. The QE denotes the probability that a photon of a certain wavelength will create a photo-electron. The QE for front-illuminated CCD’s is virtually zero for UV light below 400 nm and reaches its maximum for IR around 1000 nm.
1.0
sensitivity of the CCD’s silicon sensitivity of the human eye
0.8
sensitivity
0.6 0.4 IR
UV 0.2 0.0 200
300
400
500
600
700
800
900
1000
1100
1200
wavelength of light (nm) Figure 10: Sensitivity of CCD’s silicon (QE) versus the sensitivity of the human eye. Note that the QE of a back-illuminated CCD comes close to silicon’s QE. The QE for a front-illuminated CCD is roughly half that of silicon’s QE.
10
To increase the sensitivity for low wavelengths, the CCD surface must be coated with a very thin layer of a so-called wavelength converter. This layer absorbs UV and emits light at a higher wavelength. Back-illuminated CCD’s offer superior spectral sensitivity compared to the normal front-illuminated CCD’s. Apart from the QE there are two ways to describe the sensitivity of cameras. Both are based on photo-electrons rather than photons. Absolute sensitivity — The minimum number of detectable photo-electrons is called the absolute sensitivity. Here we are not limited by photon noise, but by readout noise. Even the very best cameras have about 5 electrons RMS readout noise. To ensure detectability of a signal it should be 3 times larger than the RMS readout noise. Relative sensitivity — The number of photo-electrons needed to change a single brightness level after A/D conversion is called relative sensitivity. The relative sensitivity is the inverse of the camera’s gain. This quantity can easily be measured for photon-limited CCD cameras. The brightness level l = g n, with g the gain and n the number of photo-electrons. The variance in brightness is var(l) = g2 var(n) = g2n. Thus after eliminating any offsets, g= var(l)/l. The CCD gain is roughly proportional to the ratio of the well capacity and the total output range. The first depends on the pixel size and the second on the number of output bits. Some examples are listed in Table 3. Table 3: Relative sensitivity (S = g–1) for various scientific CCD’s. pixel size (µm x µm) 23.0 x 23.0 6.8 x 6.8
S (e– / ADU) 90.9 7.9
ADC bits 12 12
4.5 Dynamic range and blooming Dynamic range is the maximum signal divided by the camera’s noise floor. For cooled, slowscan CCD cameras the noise floor is the readout noise. For non-cooled cameras one should be aware that dark current not only adds uncertainty, but its average value also reduces the dynamic range. Ideally, the number of output bits should be large enough to accommodate the full dynamic range. More bits will only be filled with noise. Fewer bits reduces the relative sensitivity and ultimately does not allow the detection of very weak signals – signals just above the readout noise. Very bright signals may not only saturate the camera output, but spill electrons to adjacent wells. This flooding of electrons to neighboring pixels is called blooming. Some CCD’s have a higher resistance against blooming in exchange for a lower well capacity and a lower quantum efficiency. 4.6 Spatial frequency response (SFR) The spatial frequency response quantifies the camera response as a function of spatial frequency. An ideal response is flat (equal to 1) for all spatial frequencies. This can only be achieved by point sampling. However, a CCD pixel has a photo-sensitive surface that may be as large as the entire pixel (fill factor = 1). This corresponds to a sinc shape response as depicted in Figure 11. The overall SFR (camera x optics) should be compared to the optical transfer function OTF for sampling at the Nyquist rate. The camera should not significantly reduce the overall response below the OTF. Other sources that may decrease the spatial frequency response are:
11
•
mechanical vibration of the camera during image integration (forced air cooling using a fan mounted on the camera head), • leakage of charge (smearing) during CCD readout, • conversion to and from video. Scientific cameras usually do not suffer from any of these problems and do not significantly reduce the optical resolution for proper sampling.
SFR ideal point sampling
camera response
1
SFR !square pixel sampling
0.8 0.6 OTF x SFR ideal point sampling
0.4 0.2
OTF x SFR !square pixel sampling 0.1
0.2
0.3
0.4
0.5
spatial frequency (f/fsampling) Figure 11: Spatial frequency response. Point sampling yields a constant response, whereas a rectangular reduces the higher frequencies. The overall response of the optics and a squarepixel camera is similar to the overall response of an “ideal” diffraction limited system. 4.7 Image calibration: flat-field correction Image calibration or flat-field correction corrects a variety of systematic errors such as pixel variation, “hot” pixel artifacts, non-homogeneous illumination, shading, and dirt on glass surfaces. In addition to the image I, it requires two additional images of the same exposure time: a dark image Idark (shutter closed) and a blank field image Iblank (illuminated image without objects). The corrected image Icorrected becomes Icorrected x, y =
I x, y − Idark x, y K Iblank x, y − Idark x, y
The above expression is a point operation that needs to be computed in floating point. The first term performs the normalization whereas the second term, K, scales the image. The effect of image calibration is depicted in Figure 12.
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Figure 12: Brightfield image of a latex sphere before and after image calibration. 5. Discussion This paper shows that the performance of state-of-the-art scientific cameras are indeed limited by the laws of nature. The optimal resolution is determined by ideal diffraction limited optics that create an image at the surface of the CCD chip. The fixed square sampling grid allows equi-distant sampling. Each sampling element consist of a square photo-sensitive tile, which causes negligible image blur if sampled at the Nyquist rate. CCD arrays with many small pixels are very useful in quantitative microscopy. Small pixels allow sampling at the Nyquist rate. The size of the CCD array should be slightly smaller than the microscope’s tube diameter to permit a large field-of-view. All images are contaminated by noise. Photon noise cannot be avoided and is caused by the quantum nature of light. High quality cameras yield photon-limited signal-to-noise ratios over almost the entire range of output signals. Cooling virtually eliminates dark current and the influence of hot pixels. Peltier cooling (air or liquid) lowers the operating temperature to –40°C. It is indeed needed for applications that require integration times of more than 3 to 5 seconds. The readout rates can be set to moderate levels (2 to 4 MHz.) for most applications. Only for extremely weak signals is the readout noise a serious threat to the overall signal-tonoise ratio and requires slow to very-slow readout rates. Slow-scan CCD cameras always need cooling to suppress dark current during image readout. The relative sensitivity can be extremely high (around 2 photo-electrons per ADU level) and limited by the CCD’s quantum efficiency. Note that image brightness can also be increased by using better optics. Scientific CCD cameras are available from a variety of manufacturers and have successfully been used for more than a decade in many application in cell biology. 6. Acknowledgment This work was partially supported by the Resource for Molecular Cytogenetics US DOE DEAC03-76SF00098, the Netherlands Organization for Scientific Research (NWO) Grant 900538-040, the Dutch Foundation for Technical Sciences (STW) Project 2987, the Concerted Action for Automated Molecular Cytogenetics Analysis (CA-AMCA), the Human Capital and
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Mobility Project FISH, and the Rolling Grants program of the Foundation for Fundamental Research in Matter (FOM). 7. References Castleman 1996 K.R. Castleman, Digital Image Processing, Prentice-Hall, (second edition), 1996. Inoue 1986 S. Inoue, Video Microscopy, Plenum Press, 1986. Young 1989 I.T. Young, Image fidelity: Characterizing the imaging transfer function, in: D.L. Taylor (eds.), Methods in Cell Biology, Academic Press, 1989, 1-45. Aikens 1989 R.S. Aikens, Solid-state imagers for microscopy, in: : D.L. Taylor (eds.), Methods in Cell Biology, Academic Press, 1989, 291-313. Mullikin et al. 1994 J.C. Mullikin, L.J. van Vliet, H. Netten, F.R. Boddeke, G.W. van der Feltz, and I.T. Young, Methods for CCD camera characterization, Proc. SPIE Conference (San Jose CA, Febr.9-10, 1994), SPIE, vol. 2173, 1994, 73-84.
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