Erik Clarkson

Fluorescence microscopy is an effective tool for imaging biological samples, yet captured images often contain noises, including photon shot noise and camera read noise. To analyze biological samples accurately, separating background pixels from signal pixels is crucial. This would ideally be guided by the knowledge of a parameter called the Poisson parameter, 𝜆_bg, representing the mean number of photons collected in a background pixel (for the case when quantum efficiency = 1 and the dark current is negligible). This study introduces a method for estimating 𝜆_bg, from an image which contains both background and signal pixels, using probabilistic noise modeling for an sCMOS camera. The approach incorporates Poisson-distributed photon shot noise and sCMOS camera read noise modelled with a Tukey-Lambda distribution. We apply a chi-square test and a truncated fit technique to estimate 𝜆_bg directly from a general sCMOS image, with camera parameters determined through calibration experiments. We validate our method by comparing 𝜆_bg estimates in images captured by sCMOS and EMCCD cameras for the same field of view. Our analysis shows strong agreement for low to moderate exposure images, where estimated values for 𝜆_bg align well between the sCMOS and EMCCD images. Based on our estimated 𝜆_bg, we perform image thresholding and segmentation using our previously introduced procedure. Our publicly available software provides a platform for photophysical image analysis for sCMOS camera systems.