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Abstract

Digital Camera Technologies for Scientific Bio-Imaging

This four-part series of articles published in Microscopy and Analysis covers the factors to consider in choosing a camera among CCD, EMCCD, and scientific-grade CMOS camera technologies for biological imaging applications. The differences among the sensor architectures and the impact of parameters such as pixel size, noise, and QE on signal-to-noise performance, image quality, and Nyquist sampling are considered.

The charge-coupled device (CCD) sensor has been a staple in various imaging applications for over two decades and its performance has been well characterized over the years. Recently, the scientific-grade complementary metal oxide semiconductor (CMOS) sensor has emerged as an alternative technology for imaging applications, including fluorescence microscopy. While CMOS technology has been problematic in the past for these kinds of imaging applications, the improvements realized with the new scientific-grade CMOS products seek to address these issues and bring these sensors into the mainstream of fluorescence imaging.

 

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Part 1: The Sensors
Here, we discuss the different sensor technologies and their distinguishing properties. However, there are always numerous experimental variables to consider when making decisions on the sensor for imaging applications. These include light levels, integration times, optical configuration of your microscope, etc., and as a further complication, many of these variables are related in some fashion.

Part 2: Sampling and Signal
Part 2 of this series attempts to simplify the analysis by separating the discussion between imaging and radiometric considerations as an aid in choosing optimum camera characteristics, such as pixel size, for a given set of experimental conditions.

Part 3: Noise and Signal-to-Noise Ratios
Part 3 complements this analysis by looking more closely at the noise properties of the different sensors and discusses how noise should be calculated as well as its effect on image quality in different imaging scenarios.

Part 4: Signal-to-Noise Ratio and Image Comparison of Cameras
Part 4 shows the reader how to combine these various analyses to generate a signal-to-noise profile as a function of the signal level for different sensors in an effort to identify which sensor would be best for their particular application.

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