Using CMOS Cameras for Light Microscopy
James Joubert and Deepak Sharma* QImaging, 19535 56th Avenue, Surrey, BC, Canada V3S 6K3 *
dsharma@qimaging.com
Introduction Te push in consumer electronics over past decades has
been toward smaller, faster, and cheaper products but with same or improved capabilities. Te consumer imaging world has been no exception with the integration, for example, of functional complementary metal-oxide-semiconductor (CMOS) cameras into ever smaller cellular phones. Te CMOS sensors have continued to develop and improve with increasing numbers of smaller, more sensitive pixels with larger photo-response capacity providing higher dynamic range. Tis technological expansion has inevitably spilled over into even the scientific imaging world, such as in biological light microscopy. Tis advancement of consumer CMOS digital camera technology invites comparison of CMOS cameras with the current standard charge coupled device (CCD) cameras in scientific imaging. Various comparisons can be made between current
CCD cameras and newer scientific-grade CMOS cameras, considering a variety of parameters. Some of these parameters include the ability to adequately sample at different magnifications, the signal-to-noise ratios (SNRs) achieved at the different exposure times, and the image quality at various exposures. In this article we describe and compare each factor.
Effective Use of CMOS Pixels Smaller pixels provide equivalent spatial resolution at lower
magnifications, where spatial resolution describes the ability of a camera to distinguish small specimen features. Because some scientific-grade CMOS cameras for microscopy have smaller pixels than typical CCD cameras, they can adequately sample an image at lower magnifications (that is, achieve adequate spatial resolution to properly resolve sample features) and thus take advantage of lower magnification objectives whereas many CCDs cannot. Tis provides three main advantages. Te first advantage is that the use of smaller magnification
allowed with smaller pixels increases the amount of light falling onto each pixel because more of the illuminated width of the specimen is transferred to the image sensor. At a lower magnification, an image representing a larger width of the illuminated specimen falls on the sensor. At a higher magnification with the same illumination, the light from a smaller region of the illuminated specimen is spread over the same width of sensor, so less light hits each pixel. More light increases signal and SNR for improved image quality. Te second advantage is a larger field of view. With high magnifications, only a small portion of the sample can be fit onto the camera sensor’s field of view because the sample image is magnified, or spread out, across the sensor. Using a smaller magnification means more of the sample area can fit onto the camera. Tird, because CMOS cameras do not need to read out pixels one at a time, they can read out somewhat more quickly than CCDs. For example, using the appropriate magnifications with similar fields of view, a typical microscope CCD camera may read out full frames at 10 frames per second,
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compared to a scientific-grade CMOS camera that can read out 30 frames per second. Tis is a significant advantage for many applications as more and more research focuses on live cell studies at video frame rates.
Image Quality: Signal-to-Noise Ratio Te SNR is an indicator of image quality because it ratios
the signal of interest from the sample to the uncertainty in that signal, the noise. Noise is defined as the uncertainty in a measurement and typically adds random variations to an image. For CCD and CMOS cameras, the noise sources are essentially the same, including read noise, dark noise, and photon noise [1]. Read noise is the uncertainty in reading out an electronic signal as photoelectrons generated by light hitting the sensor are converted into a voltage and a gray- scale value. It is determined by such factors as the readout electronics, sensor technology, and readout speed and is composed of multiple individual electronic components that sum together and that differ for different sensor technologies. For short, low-light exposures, this is typically the major noise source that limits image quality. Dark noise is random variation in the camera dark current, which is caused by thermal, rather than light-induced, generation of signal electrons. It increases with time and temperature so it becomes a limiting noise source at longer exposure times or when the camera is operated at higher temperatures. Dark noise can be reduced through cooling and careful readout electronics design. Photon noise is the variation in signal due to the quantized nature of the signal itself—individual photons and photoelectrons. Because it is signal-dependent, photon noise does not depend on the camera but rather on the magnitude of the signal, and it increases with the detected signal. At high light levels, the photon noise is the dominant noise source. Tese various noise sources combine in quadrature to
form the SNR equation given in Equation 1. Flux * QE * exposuretime
SNR = √ReadNoise2 + DarkNoise2 + PhotonNoise2 (1)
Here, flux is the amount of light hitting a sensor pixel in photons per second, and QE is the quantum efficiency, which indicates the percentage of incident photons that are converted into signal electrons. Te SNR equation is essentially the same for both CCD and CMOS cameras with the exception that scientific-grade CMOS sensors have random telegraph noise (salt-and-pepper type speckling), which is incorporated into the read noise. Additionally, some scientific-grade CMOS sensors have no noticeable dark current because of their advanced readout circuit design. Furthermore, another type of noise called fixed pattern noise exists in both CCD and CMOS cameras as a variation in intensity across an image rather than random fluctuations in each pixel. In some scientific CMOS sensors, it can still appear as a vertical “bar code” pattern as
doi:10.1017/S155192951100054X
www.microscopy-today.com • 2011 July
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