Automatic quality assessment for fluorescence microscopy images

Abstract

Abstract-Fluorescence microscopy imaging is a constant trade off between signal to noise ratio, total observation time and spatio-temporal resolution due to photo toxicity. In this paper, we propose a method to estimate the quality of a fluorescent image acquisition, from a single image, taking into account both signal dependent and signal independent noise. We propose a method for the calculation of the signal to noise ratio globally and locally. We validated our algorithm on real experimental data and data with known simulated noise. Results allow us to conclude that this fully automatic method provides a good quantification of the image quality., where 8 is the signal corrected for background, V is the estimated noise variance, and A, P and M are the variances of the three noise components considered: additive, Poisson and multiplicative noise, respectively. In [4], the estimation of A was considered inaccurate and moreover bad results were obtained when the background value was O. In addition, the parameters fitting was done on times series of 128 images.In general, noise estimation methods follow two different approaches. The first is the smoothing based approach, where the noise is estimated by the difference between the original

Image Quality Ranking Method for Microscopy

“…It is therefore not a surprise, that computer-based objective image quality assessment is a rather popular research topic. Only a small number of publications however can be found on microscopy applications 1 2 3 , and to our knowledge, currently there are no applicable, easily accessible image quality assessment tools available for microscopy. Quantitative microscopy image analysis tools are being developed 4 5 6 7 , but for some reason, image quality assessment does not seem to be of great concern.…”

Wavelet‐Based Image Deconvolution and Reconstruction

“…Note that a recent trend is to resort to more realistic noise models. In this respect, attention is paid to Poisson-Gaussian probabilistic models in several areas such as astronomy [122], microscopy [145,45], and medical engineering [177]. This is motivated by the fact that the Poisson component reflects the photon-counting during the acquisition step whereas the Gaussian one corresponds to the thermal noise induced by the electronics of the imaging system (typically CCD sensors).…”

Assessing and benchmarking multiphoton microscopes for biologists

“…Whichever method is preferred, it should be used identically for all measurements. There are several methods for calculating image noise (Paul, Kalamatianos, Duessman, & Huber, 2008; Yotter & Wilson, 2003) all of which have practical applications, but for our purposes in PMT detection where there can be high shot noise, the standard deviation of pixel intensity in areas without beads is most suitable. Two 140 × 140 pixel regions of interest (ROIs) in a single plane free of beads were selected and used to calculate the standard deviation of the noise (Fig.…”