Autofocusing for tissue microscopy

Yeo, Thomas; Ong, Sim‐Heng; Jayasooriah,; Sinniah, Raja

doi:10.1016/0262-8856(93)90059-p

Cited by 204 publications

(127 citation statements)

References 5 publications

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“…The main weakness of spectral methods is that they are computationally expensive and require special-purpose hardware for real-time applications (Yeo et al, 1993). We therefore based our measure of focus on an existing solution, i.e., the DCT, which operates on images of 8 × 8 pixels, also referred to as 8 × 8 DCT.…”

Section: Methodsmentioning

confidence: 99%

“…And since the DFT's basis functions are complex, this entails a larger computational load than when using the DCT. Furthermore, Krotkov (1987) and Yeo et al (1993) suggest that the spectrum of the DFT contains some information that is superfluous to the task of focusing (e.g., the phase information). Based on these arguments, we chose the DCT to be the basis of our focus measure in preference to the DFT.…”

Section: Discrete Cosine Transform Of An Imagementioning

confidence: 99%

“…Numerous focus measures have been presented in the literature (Krotkov, 1987;Gillespie et al, 1989;Firestone et al, 1991;Yeo et al, 1993;Santos et al, 1997;Choi et al, 1999;Subbarao and Tyan, 1998;Widjaja and Jutamulia, 1998;Geusebroek et al, 2000;Chern et al, 2001;Lin et al, 2004;Yap and Raveendran, 2004). From among these we selected four well-known measures to compare against our measure.…”

Section: Reference Measures Of Focusmentioning

confidence: 99%

“…Passive focusing based on image quality is a common tool in general-purpose CCD cameras and consists of three major parts: the region being focused (the focusing window); a measure of focus, which yields a maximum at the focused position; and an optimization algorithm, which searches for a global maximum of the focus measure. Many types of focus measure have been investigated in microscopy (Firestone et al, 1991;Yeo et al, 1993;Santos et al, 1997;Geusebroek et al, 2000), digital holography (Gillespie et al, 1989) and digital photography (Krotkov, 1987;Subbarao and Tyan, 1998;Widjaja and Jutamulia, 1998;Choi et al, 1999;Chern et al, 2001;Lin et al, 2004;Yap and Raveendran, 2004). Usually, these measures are calculated using certain norms of the image gradients or derivatives (Krotkov, 1987;Subbarao and Tyan, 1998;Chern et al, 2001), manipulation of gray level histograms (Krotkov, 1987;Chern et al, 2001) or operations on the longest lines in the images (Lin et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform

Kristan

Perš

Perše

et al. 2006

Pattern Recognition Letters

101

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In this paper we present a novel measure of camera focus based on the Bayes spectral entropy of an image spectrum. In order to estimate the degree of focus, the image is divided into non-overlapping subimages of 8 by 8 pixels. Next, sharpness values are calculated separately for each sub-image and their mean is taken as a measure of the overall focus. The sub-image spectra are obtained by an 8 × 8 discrete cosine transform (DCT). Comparisons were made against four well-known measures that were chosen as reference, on images captured with a standard visible-light camera and a thermal camera. The proposed measure outperformed the reference measures by exhibiting a wider working range and a smaller failure rate. To assess its robustness to noise, additional tests were conducted with noisy images.

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Section: Methodsmentioning

confidence: 99%

Section: Discrete Cosine Transform Of An Imagementioning

confidence: 99%

Section: Reference Measures Of Focusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform

Kristan

Perš

Perše

et al. 2006

Pattern Recognition Letters

101

View full text Add to dashboard Cite

show abstract

“…We use the image variance method, since its computational efficiency and robustness against noise has been previously noted [12]. The variance sharpness measure can be defined as:…”

Section: Pipette Point Focusingmentioning

confidence: 99%

Optomechatronic System For Automated Intra Cytoplasmic Sperm Injection

Shulev¹,

Tiankov²,

Ignatova³

et al. 2015

Journal of Theoretical and Applied Mechanics

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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 for tissue microscopy

Cited by 204 publications

References 5 publications

A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform

A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform

Optomechatronic System For Automated Intra Cytoplasmic Sperm Injection

Robust autofocusing in microscopy

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