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tions and then dividing by the noise from thesignal and camera to obtain the SNR. Theother approach is more empirical. It involvesmeasuring the noise (standard deviation) andmean signal in each pixel in a captured imageand then dividing the two to calculate the SNRat each pixel. The pixels containing the fea-tures of interest can then be averagedtogether to obtain an average SNR. If the cor-rect optical and camera parameters are usedand the images carefully acquired and mea-sured, the two methods of calculating SNRshould reach closely matching values.Calculating the SNR based on optical andcamera specifications has been covered inParts 2 and 3 [2, 3]. Briefly, dye concentrationand molecular brightness determine the lightflux at the sample, which is collected with acertain efficiency determined mainly by thenumerical aperture of the collection optics.The light flux hitting each pixel then dependson the spread of the image by the objectivemagnification and by the size of each of thecamera’s pixels collecting the light. The elec-tronic signal generated then depends on thisper-pixel light flux, the quantum efficiency(QE) of conversion of light to electrons, andthe exposure time during which the light is col-lected. The noise sources – mainly photon shotnoise, read noise, and dark current noise – canbe determined from the signal, the time, andeither the camera specifications or empiricalmeasurements. These noise values are thenadded in quadrature to obtain the total noisefor calculating the SNR per pixel.

B I O G R A P H YYashvinder Sabharwalhas a BS in optics fromthe University ofRochester and MS andPhD in optical sciencesfrom the University ofArizona. He was a co-founder of Optical Insights LLC where hewas responsible for the development ofimaging platforms for fluorescencemicroscopy, high-throughput screening, andhigh-content screening. Following the saleof Optical Insights, he managed productmarketing for scientific digital cameras atPhotometrics. Since 2006, Dr Sabharwal hasworked as a technical and business consul-tant with various companies and entrepre-neurial organizations. In 2010, he moved toAustin and is currently the COO for XerisPharmaceuticals.

A B S T R A C TIn this article, the last in a four part series,the signal-to-noise ratio (SNR) comparisonsbegun in Part 3 are expanded with examplesof existing cameras in low light andmedium-to-high light scenarios. The opticalsystem throughput calculations are com-bined with collection, conversion, and noisespecifications from typical cameras availableon the market to generate SNR values at dif-ferent exposure times and under commonuser scenarios. Direct quantitative compar-isons of image quality are made using SNRgraphs and experimental images.

K E Y W O R D Slight microscopy, digital cameras, CCD,EMCCD, scientific CMOS, imaging, life sci-ences

A U T H O R D E TA I L SYashvinder Sabharwal, Solexis Advisors LLC, 6103 Cherrylawn Circle, Austin, TX 78723, USAEmail: yash@solexis-advisors.com

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DIGITAL CAMERAS PART 4

I N T R O D U C T I O NThe previous three parts of this series covereda large amount of ground in comparing thethree most common camera technologies forbio-imaging – CCD, EMCCD, and CMOS sen-sors. They provided an introduction to the dif-ferent technologies [1], discussed their differ-ences, and compared their performances interms of sampling requirements [2], lightthroughput at the detector, noise types, andsignal-to-noise ratios (SNR) [3]. The goalthroughout the series has been to utilize real-istic optical parameters of an imaging systemin the selection of a camera for particular bio-logical imaging applications. In this final part of the series, the SNR com-parisons begun in a general sense in Part 3 [3]will be expanded to examples of existing cam-eras in low-light and medium-to-high-lightscenarios. The optical system throughput cal-culations will be combined with collection,conversion, and noise specifications from typ-ical cameras available on the market to gener-ate SNR values at different exposure times andunder common user scenarios. Direct quanti-tative comparisons of image quality will thenbe made using SNR graphs and experimentalimages.

T W O WAY S T O C A L C U L AT E S I G N A L - T O - N O I S E R AT I O SThere are two common approaches to deter-mining the signal-to-noise ratio. One involvescalculating the signal at the detector based onknown camera and optical system specifica-

Digital Camera Technologies for ScientificBio-Imaging. Part 4: Signal-to-Noise Ratioand Image Comparison of CamerasYashvinder Sabharwal, Solexis Advisors LLC, Austin, TX, USA

Figure 1: Outline of a procedure to empir-ically calculate the average sig-nal-to-noise ratio (SNR) in animage from a stack of sampleimages and a stack of biasimages.

E M P I R I C A L C A L C U L AT I O N O FS I G N A L - T O - N O I S E R AT I OAs mentioned above, calculating the averageSNR in an image can also be determinedempirically using a stack of the same sampleimage and stack of bias images. The processused is outlined in Figure 1 and in more detailin a previous paper [4].Briefly, the SNR can be measured using astack of sample images and a stack of biasimages. First, a stack of 10 images is acquiredfor a camera at a given exposure time. Thestandard deviation and mean of each pixelthrough the stack is then found. The averagebias value is next subtracted from the meansample image to obtain a signal at each pixel.This image is then divided by the standarddeviation image, or noise, to obtain the SNR ateach pixel. The mean and standard deviationof a background area is measured to deter-mine the threshold, which is set at one back-ground standard deviation above the back-ground mean. Finally, the mean value of theSNR of all the pixels that are above this back-ground threshold is measured to find an aver-age SNR for the sample features in a sampleimage.

C O M PA R I S O N O F S N R VA L U E SF R O M B O T H A P P R O A C H E SIf the read noise, magnification, and other val-ues for a given camera/microscope system areaccurate and various factors such as systemtransmission are taken into account, the SNRvalues calculated from the system parametersshould match fairly closely the empirical dataobtained from sample images. To demonstratethis, graphs of SNR versus integration timewere generated for two cameras with knownmeasured read noise, dark current, and otherparameters. This SNR graph is displayed in Figure 2.In this case two cameras with the same typeof sensor – a scientific-grade complimentarymetal oxide semiconductor (CMOS) – werechosen for the comparison. The two cameras,however, differed in pixel size and, conse-quently, in sampling requirements. A moder-ate light level common to many microscopicalbio-imaging applications was used over arange of typical exposure times for the com-parison. Here, a deep-cooled scientific CMOScamera (sCMOS, Neo from Andor Technology)with 6.5-µm pixels was compared to anotherscientific CMOS camera (Rolera Bolt fromQImaging), which has smaller 3.63-µm pixels.As described in Part 2 of this series [2], Nyquistsampling requires that the distances resolvableby the microscope optics be magnified to atleast twice the size of the camera sensor’s pix-els. For example, with a 60� objective, thedeep-cooled sCMOS’s 6.5-µm pixels can ade-quately Nyquist sample a specimen. However,the 3.63-µm pixel scientific CMOS would over-sample. Thus because it has smaller pixels, itcan use a lower magnification while stillNyquist sampling.To achieve this lower magnification for the3.63-µm pixel scientific CMOS while stillNyquist sampling, one of the newer 30� or40� objectives with high NA can be used, or a

0.5� coupling lens can be added before thecamera when a 60� objective is used. Bothoptions provide lower magnification of theimage hitting the sensor so that the image ismuch less spread out, and thus it is condenseddown before projection onto the camera sen-sor. This provides two advantages: 1. Condens-ing the image concentrates more light perpixel to boost signal and SNR; and 2. Lessspreading out of the image allows more of theimage to fit on the sensor for a larger effectivefield of view.Three common exposure times were chosen,as indicated by the dashed lines in Figure 2,and used to acquire image stacks at the appro-priate light levels of the same sample using thetwo cameras. Simultaneous imaging of thesame sample by the two cameras was per-formed by using a DualCam DC2 multichannelsystem with a 50/50 beamsplitter to project theimage equally onto the two cameras. Theacquired images are pictured in Figure 3. TheSNR values empirically calculated from eachimage are also included in Figure 3. Note thatall of the images in Figure 3 and in other fig-ures in this article are zoomed in to make thefeatures appear the same size for image qual-ity comparison, despite differences in the

fields of view and pixel sizes. All images havealso been scaled so that the minimum andmaximum intensities displayed are set to theintensities at the same locations in all imageswithin the same figure. As can be seen by comparing Figures 2 and 3,the SNR graph values calculated from cameraand system parameters match the valuesobtained from images acquired under thoseconditions reasonably well. Additionally, it isnoteworthy that although the 3.63-µm pixelscientific CMOS produces images with overalllower SNRs than the deep-cooled 6.5 µm pixelsCMOS, the difference is not extremely large.The image quality is also similar between thetwo sets. The deep-cooled 6.5-µm pixel sCMOS does,however, have two other main advantagesover the 3.63-µm pixel scientific CMOS in thatit has a faster 100 frames per second shortburst maximum full frame rate and a largerfield of view. Additionally, at lower light levels,the difference between the two camerasbecomes more significant. In low light situa-tions, the low read noise in the deep-cooled6.5-µm pixel sCMOS would give it a noticeableadvantage over the 3.63-µm pixel scientificCMOS as this would be a read noise-limited

Figure 2: Graph of signal-to-noiseratio (SNR) for a 6.5-µmpixel deep-cooled scien-tific CMOS and a 3.63-µmpixel scientific CMOScamera at increasingexposure times. Verticaldashed lines indicate sam-ple integration times cho-sen for empirical compar-ison in Figure 3.

Figure 3: Comparison of signal-to-noise ratios (SNR) of differently exposed images of the same samples of fluorescent owl monkey kidney cells.(a) The deep-cooled 6.5-µm pixel scientific CMOS. (b) 3.63-um pixel scientific CMOS.Note that other issues were discovered for the 6.5-µm pixel scientific CMOS images which are displayed in Figs 7, 8, and 9 (See Unexpected Findings).

Exposure 100 msExposure 50 ms Exposure 200 ms

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scenario. It is also useful from a camera selection view-point to compare the effect that cameras witha variety of pixel sizes, read noises, and otherparameters have on SNR under differentexperimental scenarios. To this end, SNRgraphs of different camera types with suchvarying parameters can be produced usinglight levels commonly encountered in bio-imaging experiments. These can be split intotwo major groups covering a majority of bio-logical imaging applications: low light levels,and moderate light levels.

C O M PA R I S O N O F S I G N A L - T O -N O I S E R AT I O I N L O W L I G H T Under low-light conditions, camera sensors arephoton-starved, and image quality is conse-quently limited in terms of noise, most oftenby the camera’s read noise. These situationstypically encompass fluorescence imagingwith low signal resulting from low excitationpower, low fluorophore density, or short expo-sure times. These conditions are typicallyrequired for dynamic live-cell imaging, fluo-rescent protein imaging, spinning disk confo-cal microscopy, superresolution microscopy,and single molecule fluorescence, among oth-ers. Electron multiplying CCD (EMCCD) cam-eras have been developed for this type ofimaging, as they possess the ability to increasethe detected signal orders of magnitudebefore reading out. Since read noise isimparted during the readout process, this mul-tiplicative ability beforehand makes theireffective read noise orders of magnitudelower, or less than half an electron. Such a lowread noise is a tremendous advantage in low-light scenarios where read noise is the majorlimiting factor.Scientific CMOS cameras have recently beendeveloped with read noise values as low asaround 1 e- when not imaging at maximumspeed and depending on how read noise isdefined. While this is still somewhere aroundan order of magnitude higher than someEMCCD cameras, it may provide enough sensi-tivity for some low-light imaging. Figure 4compares the SNR response of two of these-camera types under low-light conditions andtypical exposure times: an EMCCD camera(Evolve 512 from Photometrics) and a deep-cooled scientific CMOS camera (Neo fromAndor Technology).It is evident from the blue and red curves inFigure 4 that the EMCCD has much higher SNRvalues than the 6.5 µm deep-cooled sCMOSunder these conditions. This is a result of sev-eral factors. The first is the EMCCD’s low effec-tive read noise, which is approximately oneorder of magnitude lower than the scientificCMOS. The second reason is the EMCCD’sapproximately 93% peak QE, which is signifi-cantly larger than the 6.5 µm deep-cooledsCMOS’s 57% peak QE. The final reason is theEMCCD’s larger 16 µm-wide pixels, which are alittle over double the width (and quadruplethe collection area) of the deep-cooledsCMOS’s 6.5 µm pixels. The pixel size can besomewhat equalized by software binning thedeep-cooled 6.5-µm pixel sCMOS. Since it is a

Figure 4: (a) SNR versus integration time graph comparing an EMCCD with a deep-cooled 6.5-µm pixel scientific CMOS with and without software binning.(b) Images of the same human osteosarcoma U2 cells taken by the EMCCD (left) and the deep-cooled 6.5 µm scientific CMOS with and without soft-ware binning at 50 ms.

Figure 5: (a) SNR response at increasing integration times for both the EM-C2 EMCCD and the 6.5 µm deep-cooled scientific CMOS at low light levels. (b) Fluorescent fox lung fibroblast images taken at 50 ms with the two cameras: left EM-C2; right CMOS.

sCMOSEvolve sCMOS 2x2 binningb

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bCMOS sensor, the pixels cannot be binned on-chip, the method of binning employed in CCDsand EMCCDs. As a result, the maximumimprovement in SNR cannot be realized. How-ever, with a 2�2 software bin, the deep-cooled 6.5 µm sCMOS’s pixels are effectivelycloser in size to the EMCCD, and this gives anapproximately 2� improvement in the SNR.This graph is plotted as the green line in Figure4, which brings the response closer to that ofthe EMCCD but still well below it. The differ-ences in image quality among these three aredemonstrated in the images in Figure 4b.While EMCCDs are commonly used for lowlight imaging, not all are back-illuminated likethe EMCCD tested here. Other EMCCDs, suchas the Rolera EM-C2, are front-illuminated,which lowers the QE somewhat (peak QEaround 62%) but also lowers the cost to pro-duce. However, these EMCCDs still display lessthan half an electron of effective read noise asa result of their EM gain. The low light SNRresponse of the EM-C2 is plotted in Figure 5a,along with that of the deep-cooled 6.5 µm sCMOS. The EM-C2 has higher SNR perfor-mance for the range covered due to the muchlower effective read noise and slightly higherQE. At longer exposure times, however, the 6.5µm deep-cooled sCMOS will meet and begin toovertake the EM-C2 due to the excess noisefactor’s effect at higher light. Representativeimages of the two cameras are shown in Fig-ure 5b, demonstrating higher SNR qualityimages for the EM-C2 EMCCD. Overall, thesecalculations and images demonstrate that forlow-light imaging, EMCCDs are the sensors ofchoice due to the boost in SNR given by theirelectron multiplying capabilities, even forfront-illuminated EMCCDs.

C O M PA R I S O N O F S I G N A L - T O -N O I S E R AT I O I N M O D E R AT EL I G H T A large percentage of biological imagingapplications acquire light at moderate lightlevels. From immunofluorescence and bright-field imaging to GFP and calcium imaging,moderate light levels describe a wide range oftypical bio-imaging situations. Under thesehigher photon fluxes, read noise is still impor-tant but to a lesser degree. This occurs as inher-ent photon shot noise becomes more domi-nant, equalizing many sensors to a degree,although EMCCDs have added noise in thishigher light regime due to their excess noisefactor. These different noise types are dis-cussed at length in Part 3 of the series [3]. Anexample of an interline CCD scientific camerawith the ubiquitous Sony ICX-285 chip typicallyemployed in this range is the CoolSNAP HQ2.The 6.5 µm deep-cooled scientific CMOSdescribed earlier is also designed for this rangeof light levels and performs similarly to theHQ2 in terms of SNR in Figure 6a. It is apparentthat, although the SNR responses of the twocurves are close, the deep-cooled 6.5 µm sci-entific CMOS’s response is slightly higher forshorter exposures. However, although theread noise is lower for the deep-cooled 6.5-µmpixel scientific CMOS and the pixel sizes arealmost identical, inherent photon shot noise

begins to dominate at moderate light levelsand greatly equalizes the two cameras. In fact,since the peak QE is a little higher for the HQ2compared to the 6.5 µm deep-cooled scientificCMOS, the HQ2 meets and overtakes the 6.5µm deep-cooled scientific CMOS at longerexposures. The image comparison in Figure 6bshows little noticeable difference between thetwo cameras at these light levels, as expectedfrom the SNR graph. It should be also notedthat for low light scenarios, the lower readnoise of the deep-cooled scientific CMOS cam-era would provide a somewhat higher SNRresponse than the HQ2.

Figure 6: (a) SNR plot of the HQ2 CCD and deep-cooled 6.5 µm scientific CMOS atmedium-light levels. (b) Fluorescence image comparison ofrabbit kidney RK-13 cells taken by thetwo cameras. Left: HQ2; Right: sCMOS.Note the additional characteristics of thescientific CMOS camera in the Unex-pected Findings section and in Figures 7,8 and 9.

Figure 7: Histogram of deep-cooled 6.5 µm pixel scientific CMOS camera 16-bit image of a fluorescent cell sample. Spacings of 32 intensity units per gap indi-cate histogram stretching of 11-bit data to 16 bit.

U N E X P E C T E D F I N D I N G SIn the SNR image quality analyses describedabove, quantification issues with the differentcameras appeared, mainly in the deep-cooledscientific CMOS images. The first characteristicwas in the intensity histograms of the deep-cooled 6.5-µm pixel scientific CMOS images.This particular sCMOS camera switchesbetween two gain amplifiers and associatedanalog-to-digital (A/D) converters to convertdetected photoelectrons in each pixel into anintensity value. Each A/D converter is reportedto have a bit depth of 11 bits, which is said tobe combined on the camera into a 16-bit

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Figure 8: The photon response curve(mean-variance curve) takenusing 6.5-µm pixel deep-cooled scientific CMOS cam-era. Note the three differentlinear regions where the cam-era responds differently tolight and the characteristicbehavior circled between theseregions.

Figure 9: Full frame image from deep-cooled 6.5-µm pixel scientific CMOS cam-era with a flatfield light source. Employing two different readout direc-tions produces differences in intensities in the two image halves.

image. However, analysis of the histogram inFigure 7 shows gaps between histogrampoints. This occurs over a range of bin sam-plings and consistently shows a gap of 32, or25, the exact difference between 11 bits and 16bits. Overall, this highly suggests image hist-ogram stretching; indeed, when the camera issupposed to be delivering 16-bit data, it isactually only using 11 bit data and mathemat-ically extrapolating. For some imaging appli-cations, these stretched 11 bit images may beadequate, but more demanding high-endapplications may show discrepancies from true16- bit images. Another issue affecting image quality andrelated to the use of two gain amplifiersappears in the curve in Figure 8. The figureshows what is generally known as a mean-vari-ance curve or photon transfer curve for thedeep-cooled 6.5-µm pixel scientific CMOS.Based on photon shot noise, a property inher-ent to all microscopy light sources, the vari-ance in the signal should increase linearly withthe mean signal intensity. Scientific camerastypically display a highly linear mean-variancecurve over their linear range and have a sloperelated to the camera’s gain. However, for the6.5 µm scientific CMOS camera, two differentgains are used, and the gain used for eachpixel depends on the intensity level. This isdemonstrated in the two straight-line regionswith differing slopes (and associated gains)that cross-over near the middle of Figure 8b.While this has the benefit of increasingdynamic range, having multiple gains in themean-variance curve that vary depending onintensity makes converting to a standardquantitative comparison unit, such as thephotoelectron, much more difficult. Other issues appear at the low and highends of the curve. At the low end, a jumpoccurs in the graph, presumably where aswitch from unstretched 11 bit to stretched 11bit data occurs. Additionally, the slope changesslightly, and the two lines do not actuallymeet. At the high end, instead of a quick dropnear the saturation intensity value expectedfor scientific cameras, there is a gradualdecrease and an unusual immense jump toalmost 50 times the values observed in the lin-ear regions (not shown because out of scale)before dropping to zero (see Figure 8). Fur-thermore, the cross-over regions between thetwo sets of linear ranges show unusual behav-ior that could further affect quantification ofimage features recorded at these intensities inparticular. Having these many distinct regions in whichcamera behavior varies according to averageintensity level can be problematic for quanti-tative data analysis. This behavior is particu-larly troublesome for imaging applicationswhere quantitative comparisons need to bemade of intensities expected to vary acrossthose three different intensity regions eitherover time or across the image. Fluorescentrecovering after photobleaching (FRAP) imag-ing is such an example, where quantitativelymonitoring the increase in intensity in a darkphotobleached spot to determine factors suchas molecular diffusion rates would be suspect.

It should be noted that, for the 3.63-µm scien-tific CMOS camera and the scientific CCD andEMCCD cameras shown here, the mean-variance curves have just a single consistentlylinear region.One final issue is the use of two differentreadout directions for two halves of the chip.This chip architecture doubles frame rate butadds an additional layer of variation to the 6.5µm scientific CMOS to do so. Figure 9 demon-strates this variability between chip halves. Itmanifests as a distinct difference in the aver-age intensity levels for two halves of the sen-sor, even with a flatfield distribution of lightacross the sensor. Furthermore, this intensityvariation appears over a range of light levels.This suggests even further variation in the gainin the two halves at both low and high inten-sity levels, a significant concern for quantifica-tion and noise/variability. Once again, it shouldbe noted that the 3.63-µm pixel scientificCMOS and the CCD and EMCCD cameras usedhere do not display this half-chip performancevariation.This unexpected findings section illuminatedsome interesting findings with respect to thenew scientific CMOS deep-cooled camera andhighlighted some differences between its cur-rent behavior and that which a researcherwould observe using the more widelyaccepted technologies. Upon consideration ofthe fact that this technology is quite new, it isprobably not surprising that such differencesexist, as new technologies tend to provide newissues as they go through a maturation period.For example, when the world’s first EMCCDmicroscopy camera was released in 2003 (theCascade:650), it utilized the Texas InstrumentsTI TC253 chip; however, this chip – whichalthough at the time seemed to be the bestlow light imaging CCD available due to itsnovel electron multiplication – was quicklysuperseded by much more capable sensorssuch as the CCD87 and CCD97 by e2V tech-nologies. Such maturation of new technolo-gies would seem to be more normal than not– indeed all data on the scientific CMOS pre-sented here were taken with a deep-cooledversion of the camera, and other implementa-tions may exhibit different behaviors.

C O N C L U S I O N SThis series has introduced the different sensortypes in cameras most often used in biologicalimaging, discussed the key differences amongthe different types in terms of critical parame-

ters such as read noise and readout modes,and even discussed the importance of the var-ious noise sources and their influence on sig-nal-to-noise ratio and image quality. A systemof calculating SNR for each sensor by propa-gating light throughout an optical systemfrom sample to detector was also proposedand used to generate graphs of SNR versusexposure time to compare the different sensortypes. This was further expanded upon in thispart of the series to demonstrate how the dif-ferent camera types compare under differentimaging application conditions in terms ofboth SNR values and sample image quality. As mentioned earlier, the goal of this serieswas not to state that one sensor technologywas the best performing under all circum-stances. Rather, this series aimed to help pro-vide a variety of factors to consider, supportedby several data comparisons of cameras inapplication scenarios, to assist in choosing acamera for an application of interest.

R E F E R E N C E S1. Sabharwal, Y. Digital Camera Technologies for Scientific Bio-

Imaging. Part 1: The Sensors. Microscopy and Analysis.25(4):S5-S8 (AM), 2011.

2. Sabharwal, Y., Joubert, J., Sharma, D. Digital CameraTechnologies for Scientific Bio-Imaging. Part 2: Samplingand Signal. Microscopy and Analysis. Web Features. July2011:1-5.

3. Joubert, J., Sabharwal, Y., Sharma, D. Digital CameraTechnologies for Scientific Bio-Imaging. Part 3: Noise andSignal-to-Noise Ratios. Microscopy and Analysis. WebFeatures. Sept 2011:1-4.

4. Joubert, J., and Sharma, D. Using CMOS Cameras for LightMicroscopy. Microscopy Today 19(4):22-29, 2011.

©2012 John Wiley & Sons, Ltd

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