Phase contrast and differential interference contrast instrumentation and applications in cell, developmental, and marine biology

Gundlach, Heinz

doi:10.1117/12.142945

Cited by 21 publications

(9 citation statements)

References 1 publication

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“…Then the complex amplitude, 77), of the wave field in the back-focal plane of the condenser lens and right before the specimen can be obtained by the Fresnel superposition integral +00 +00 U(x0,y0) =fJ U,77) h0(,77;x0,y0) dd77, (2) e32ir(xoe + iii)I.\f specifies the complex amplitude of the illuminating plane waves (see Figure 3). Under the paraxial approximation the complex amplitude of the wave transmitted by the specimen is simply U0(x0, yo) f(x0 , yo) Uc (x0 , yo).…”

Section: Derivation Of the Two-dimensional Imaging Modelmentioning

confidence: 99%

<title>Imaging models for three-dimensional transmitted-light DIC microscopy</title>

Preza¹,

Snyder²,

Conchello³

1996

SPIE Proceedings

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Nomarski Differential-Interference-Contrast (DIC) microscopy' is a widely used method for imaging transparent specimens that are not visible with ordinary light microscopy. DIC microscopy enhances contrast in the images of such specimens by converting differential phase changes to intensity variations via the method of light interference. These phase changes are introduced in light as it passes through regions of different refractive index within a specimen.In this paper, the development of an imaging model that describes three-dimensional DIC imaging under partiallycoherent illumination is presented. Our approach in deriving the model involves the derivation of a two-dimensional model and its extension to three dimensions, assuming weak optical interactions within the specimen. The coherent limit of our two-dimensional model coincides with existing DIC models. Model predictions generated with the coherent limit of the three-dimensional model are compared to real DIC images acquired from imaging phantom specimens. It is shown that the model predictions resemble the real images obtained with the condenser aperture closed better than the images obtained with the aperture open. This result confirms the need for the general model that we have derived.

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Section: Derivation Of the Two-dimensional Imaging Modelmentioning

confidence: 99%

<title>Imaging models for three-dimensional transmitted-light DIC microscopy</title>

Preza¹,

Snyder²,

Conchello³

1996

SPIE Proceedings

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“…Dynamic analysis of live cells to quantify various motility characteristics has been instrumental in understanding ciliary function (11), cell development (28), early physiologic cellular reactions (26), nuclear organization (10), cytoplasmic forces (9), neuronal death (3), and the metastatic potential of animal models of prostate cancer (14,17). Common methods of imaging these cells for motility analysis have been brightfield, fluorescence, phase contrast, modulation contrast, and differential interference contrast microscopy (6). Not all of these imaging modalities are optimal for live prostate cells.…”

Section: Key Terms: Prostate; Cancer; Cell; Microscopy; Image Segmentmentioning

confidence: 99%

“…This filter causes the modulation of opposite gradients above and below average background intensity, which produces a three-dimensional appearance to a cell image (8). DIC microscopy also gives a three-dimensional appearance to a cell's image, but does so by using birefringence optics consisting of Nomarski prisms and a polarizer (6,8).…”

Section: Key Terms: Prostate; Cancer; Cell; Microscopy; Image Segmentmentioning

confidence: 99%

Automated image analysis system for detecting boundaries of live prostate cancer cells

et al. 1998

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Image analysis provides a powerful tool for quantifying cell motility and has been used to correlate motility with metastatic potential in an animal model of prostate cancer. However, widespread use of this image analysis method has been limited because earlier methods of quantitative analysis required time‐intensive and subjective manual tracing of cell contours. In this report, we describe a fully automated image segmentation algorithm for detection and morphometric description of prostatic cells. The segmentation system was tested on prostate cell images generated from Hoffman modulation contrast microscopy (47 cells at 64 time points = 3,008 images) and differential interference contrast microscopy (29 cells at 64 times points plus 1 cell at 62 time points = 1,918 images). Morphometric measurements were derived from computer‐determined cell boundaries and compared with the same measurements derived from manually traced cell boundaries. Final correlation coefficients for area and perimeter measurements for Hoffman and differential interference contrast microscopy were (0.76, 0.62) and (0.93, 0.93), respectively. Results with our differential interference contrast images demonstrate that our segmentation algorithm reliably and efficiently replaces the need for manually traced cell boundaries in addition to eliminating intraobserver variation. Our automated segmentation process will have immediate utility in our motility analysis system that relates cell motility with metastatic potential of prostate cancer. Cytometry 31:287–294, 1998. © 1998 Wiley‐Liss, Inc.

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“…However, this intensity information cannot be converted to OPD directly; moreover, the images are affected by an inherent halo and shade-off artifacts. Differential interference contrast microscopy is a kind of shearing interferometry that generates the phase gradient contrast by slightly shifting two polarized light beams and then having them interfere with each other [3]. This method is more popular than Zernike phase contrast for its good pseudo three-dimensional view and improvement on the transverse resolution, although it cannot be employed to quantitative OPD measurements due to its nonlinear response [4].…”

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confidence: 99%

Optical path difference microscopy with a Shack–Hartmann wavefront sensor

et al. 2017

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In this Letter, we show that a Shack-Hartmann wavefront sensor can be used for the quantitative measurement of the specimen optical path difference (OPD) in an ordinary incoherent optical microscope, if the spatial coherence of the illumination light in the plane of the specimen is larger than the microscope resolution. To satisfy this condition, the illumination numerical aperture should be smaller than the numerical aperture of the imaging lens. This principle has been successfully applied to build a high-resolution reference-free instrument for the characterization of the OPD of micro-optical components and microscopic biological samples.

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Phase contrast and differential interference contrast instrumentation and applications in cell, developmental, and marine biology

Cited by 21 publications

References 1 publication

<title>Imaging models for three-dimensional transmitted-light DIC microscopy</title>

<title>Imaging models for three-dimensional transmitted-light DIC microscopy</title>

Automated image analysis system for detecting boundaries of live prostate cancer cells

Optical path difference microscopy with a Shack–Hartmann wavefront sensor

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