Autofocusing in microscopy based on the OTF and sampling

Boddeke, Frank R; Vliet, Lucas J. van; Netten, Hans; Young, Ian T.

doi:10.1002/1361-6374(199412)2:4<193::aid-bio4>3.0.co;2-l

Cited by 30 publications

(7 citation statements)

References 14 publications

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“…Hence, the threshold θ = 0.5 is still applicable [29]. A three-dimensional object can be considered as a stack of infinitely thin two-dimensional layers [20], [30], [31]. When three-dimensional objects are imaged through a microscope, the image projected onto the charge coupled device (CCD) element consists of contributions from all layers.…”

Section: Optical Imaging Theory and Simulationsmentioning

confidence: 99%

“…When three-dimensional objects are imaged through a microscope, the image projected onto the charge coupled device (CCD) element consists of contributions from all layers. A sphere is considered in focus when the middle layer of the sphere is in the focal plane [28], [31]. Bubbles which are large compared to the resolution have to be considered as part of the optical system [32].…”

Section: Optical Imaging Theory and Simulationsmentioning

confidence: 99%

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Simulations and measurements of optical images of insonified ultrasound contrast microbubbles

Postema

Bouakaz

Chin

et al. 2003

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Ultrasound contrast agents (UCAs) are used in a clinical setting to enhance the backscattered signal from the blood pool to estimate perfusion and blood flow. The UCAs consist of encapsulated microbubbles, measuring 1-10 µm in diameter. Acoustic characterization of UCAs is generally carried out from an ensemble of bubbles. The measured signal is a complicated summation of all signals from the individual microbubbles. Hence, characterization of a single bubble from acoustic measurements is complex.In this study, 583 optical observations of freely flowing, oscillating, individual microbubbles from an experimental UCA were analyzed. The excursions during ultrasound exposure were observed through a microscope. Images were recorded with a high frame rate camera operating at 3 MHz. Microbubbles on these images were measured off-line and maximal excursions were determined. A technique is described to determine the diameters of the bubbles observed. We compared the maximal excursions of microbubbles of the same initial size in an ultrasound field with a 500 kHz center frequency at acoustic amplitudes ranging from 0.06 MPa to 0.85 MPa.It was concluded that maximal excursions of identical bubbles can differ by 150% at low acoustic pressures (mechanical index or MI < 0.2). At a high acoustic pressure (MI = 1.2) an image sequence was recorded on which a bubble collapsed, while an apparently identical bubble survived.

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Section: Optical Imaging Theory and Simulationsmentioning

confidence: 99%

Section: Optical Imaging Theory and Simulationsmentioning

confidence: 99%

Simulations and measurements of optical images of insonified ultrasound contrast microbubbles

Postema

Bouakaz

Chin

et al. 2003

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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“…The value of the focus function is computed from an image captured at that z position. Research on microscope autofocusing dates back over a quarter of a century, and many autofocusing functions and algorithms have been proposed in the literature [1][2][3][4]. A commonality of existing autofocusing methods is that all of them are based on single-resolution image analysis, and have inherent limitations.…”

mentioning

confidence: 99%

“…The proposed method employs a Haar transform to generate a multiresolution image representation. Then across different resolutions the image power is computed as the focus measure and a coarse-to-fine search is performed to find the best focus position [2]. The method was tested for fluorescence microscopy on 30 fields of FISH cytogenetic specimens (DAPI channel images), along with several current best-performing methods.…”

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confidence: 99%

A Multiresolution Autofocusing Method for Automated Microscopy

Wu¹,

Li²,

Chen³

et al. 2003

Microsc Microanal

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Autofocusing is crucially important for automated microscopy, where fully automatic acquisition of microscope images in unattended operation is required. In addition to providing automation of imaging for scanning microscopy applications, it facilitates objective, accurate, and consistent image measurements for quantitative analysis. Autofocusing algorithms determine the in-focus position for an image based on maximization of a focus function, which represents the measure of focus as a function of the axial z position, and is sampled at different positions along the z-axis. The value of the focus function is computed from an image captured at that z position. Research on microscope autofocusing dates back over a quarter of a century, and many autofocusing functions and algorithms have been proposed in the literature [1][2][3][4]. A commonality of existing autofocusing methods is that all of them are based on single-resolution image analysis, and have inherent limitations. Recent developments in signal processing and wavelet transform theory, however, suggest an alternative approach to tackle the problem and to overcome the limitations by resorting to multiresolution image analysis.

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“…Best focus is found by searching for the optimum in the focus curve. In a classical approach the value of the focus score is estimated for a few focus positions (4,8,10). Evaluation of the scores indicates where on the focus curve to take the next sample.…”

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confidence: 99%

Robust autofocusing in microscopy

et al. 2000

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Background: A critical step in automatic microscopy is focusing. This report describes a robust and fast autofocus approach useful for a wide range of microscopic modalities and preparations. Methods: The focus curve is measured over the complete focal range, reducing the chance that the best focus position is determined by dust or optical artifacts. Convolution with the derivative of a Gaussian smoothing function reduces the effect of noise on the focus curve. The influence of mechanical tolerance is accounted for. Results:The method is shown to be robust in fluorescence, bright-field and phase contrast microscopy, in fixed and living cells, as well as in fixed tissue. The algorithm was able to focus accurately within 2 or 3 s, even under extremely noisy and low contrast imaging conditions. Conclusions: The proposed method is generally applicable in light microscopy, whenever the image information content is sufficient. The reliability of the autofocus method allows for unattended operation on a large scale. Cytometry 39:1-9, 2000.

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Autofocusing in microscopy based on the OTF and sampling

Cited by 30 publications

References 14 publications

Simulations and measurements of optical images of insonified ultrasound contrast microbubbles

Simulations and measurements of optical images of insonified ultrasound contrast microbubbles

A Multiresolution Autofocusing Method for Automated Microscopy

Robust autofocusing in microscopy

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