Optimisation of centroiding algorithms for photon event counting imaging

Suhling, Klaus; Airey, R.W.; Morgan, B. L.

doi:10.1016/s0168-9002(99)00770-6

Cited by 37 publications

(50 citation statements)

References 33 publications

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“…The accuracy limit has been shown to lie at approximately 1-2 nm under realistic conditions (Thompson et al 2002). Relevant analysis methods of photon-counting data, including the centroid calculation (Suhling et al 1999), have been previously developed in the field of astrophysics, where similar limitations and problems are encountered-weak signals combined with the need of high spatial resolution.…”

Section: Microscopy Imagingmentioning

confidence: 99%

Fluctuations as a source of information in fluorescence microscopy

Petrášek

Schwille

2008

J. R. Soc. Interface.

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Fluctuations in fluorescence spectroscopy and microscopy have traditionally been regarded as noise-they lower the resolution and contrast and do not permit high acquisition rates. However, fluctuations can also be used to gain additional information about a system. This fact has been exploited in single-point microscopic techniques, such as fluorescence correlation spectroscopy and analysis of single molecule trajectories, and also in the imaging field, e.g. in spatio-temporal image correlation spectroscopy. Here, we discuss how fluctuations are used to obtain more quantitative information from the data than that given by average values, while minimizing the effects of noise due to stochastic photon detection.

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Section: Microscopy Imagingmentioning

confidence: 99%

Fluctuations as a source of information in fluorescence microscopy

Petrášek

Schwille

2008

J. R. Soc. Interface.

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“…Selected, often used centroiding algorithms for sparse emission data sets, are center-of-gravity (COG) centroiding, centroiding algorithms based on analytical functions (that describe the emission cloud) or "interpolated convolution". Center-of-gravity (COG) centroiding [9,10,13,[15][16][17] is a simple and popular centroiding method. The center coordinates of a signal cluster are determined by calculating a weighted average over the signal cluster.…”

Section: Introductionmentioning

confidence: 99%

“…The center coordinates of a signal cluster are determined by calculating a weighted average over the signal cluster. A modification of this technique is the "all above threshold COG" [16,17], which takes into account all detector elements within a signal cluster that have a pre-defined signal level. There are several different centroiding algorithms based on analytical functions.…”

Section: Introductionmentioning

confidence: 99%

“…There are several different centroiding algorithms based on analytical functions. Functions as a Gaussian [10], a hyperbolic cosine [10], a parabola [10,15] or a Lorentzian [10,16] are fit to the signal distribution. The center of the distribution is extracted from the fit.…”

Section: Introductionmentioning

confidence: 99%

“…The zero-crossing of this discrete distribution is interpolated to sub-detector element position and is interpreted as the centroid location of the charge cloud [17]. Also, "hybrid" algorihms combine the successful performance aspects of more than one centroiding technique and have been suggested by Suhling and co-workers [16]. In particular, a hybrid algorithm fusing a Gaussian fit (for large clusters) and a 3-point center of gravity calculation (for small clusters) achieves low fixed pattern noise and a maximum detector quantum efficiency when compared to "ordinary" centroiding techniques or a other hybrids that were used to centroid data from the photon illumination of an MCP-phosphor screen-CCD camera assembly.…”

Section: Introductionmentioning

confidence: 99%

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Sparse spectral techniques for emission imaging

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Jungmann

MacAleese

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Fixed Pattern Noise in Localization Microscopy

et al. 2014

Self Cite

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Localization microscopy vastly improves the resolution achieved by fluorescence microscopy by fitting the positions of individual fluorophores. We examine the reconstructions produced by different fitting algorithms for instances of fixed pattern noise--systematic tendencies to alter estimated emitter positions according to their subpixel location in a way that does not reflect the ground truth structure. We show that while not readily visible at standard empirical signal strengths, fixed pattern noise can occur when performing sub-pixel fitting, and that its degree varies according to the algorithm used and the relative size of the pixels compared to the point spread function. For pixel sizes in the range 80-170 nm, this results in variations in accuracy of the order of 2-4 nm-comparatively small for many applications, but non-negligible in scenarios where very high accuracy is sought.

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Optimisation of centroiding algorithms for photon event counting imaging

Cited by 37 publications

References 33 publications

Fluctuations as a source of information in fluorescence microscopy

Fluctuations as a source of information in fluorescence microscopy

Sparse spectral techniques for emission imaging

Fixed Pattern Noise in Localization Microscopy

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