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Basics of Digital Microscopy

Tytus Bernas¹

¹Purdue University, West Lafayette, Indiana

Publication Name:

Current Protocols in Cytometry

Unit Number:

Unit 12.2

DOI:

10.1002/0471142956.cy1202s31

Online Posting Date:

February, 2005

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Abstract

Digital microscopy combines the equipment of classic light microscopy with a computerized imaging system, i.e., a digital camera. This technique comprises image formation by optics, image registration by a camera, and saving of image data in a computer file. This chapter describes limitations that are particular to each of these processes, including optical resolution, efficiency of image registration, and characteristics of image file formats. Knowledge with regard to these limitations serves, in turn, to help construct a set of guidelines aimed at optimization of digital microscopic imaging.

Keywords: Resolution; sampling; quantization; signal-to-noise ratio; image file format; compression

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Unit Introduction
Optical Imaging
Digital Registration of an Optical Image
Storage of Digital Image Data
Conclusions
Literature Cited
Figures
Tables

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Figures

Figure 12.2.1

Dependence of microscope PSF on numerical aperture (NA, in rows) and wavelength (, in columns). Plot coordinates are indicated in the top left corner: x,y = position (from –0.75 to 0.75 µm); i = intensity. Width of the PSF decreases with increasing numerical aperture and increases with wavelength.

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Figure 12.2.2

Summed image intensities (PSFs) of two points resolved according to (from left to right) Rayleigh, Sparrow, and FWHM (Houston) criteria. Plot coordinates are indicated in the top left corner: x,y = position (from –0.75 to 0.75 µm); i = intensity. Resolution (critical distance) depends on what intensity contrast is considered sufficient.

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Figure 12.2.3

Optical transfer function (OTF) corresponding to PSF from Figure 12.2.1 (lower part, NA = 0.95; = 0.53 µm). Plot coordinates are indicated in the top left corner: f_x,f_y = spatial frequencies in x and y direction (from –4.4 to 4.4 µm^–1); i = intensity. OTF is 0 above cutoff frequency (i.e., is band-limited).

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Figure 12.2.4

Schematic representation of a light-sensitive element (pixel) of a CMOS camera. The photons incident on the light-sensitive area and photoelectrons (e^–) trapped in the potential well are indicated.

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Figure 12.2.5

Influence of camera chip temperature (x axis, °C) and incident light intensity (y axis, arbitrary units) on different components of camera noise. Signal-to-noise ratio (SNR) is indicated by pie size, whereas relative contributions of photon, readout, and dark noise are depicted by pie slices. The parameters were estimated for a Sony ICX085 chip and full well capacity.

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Figure 12.2.6

Schematic representation of acousto-optical tunable filter (from Brimrose Corp.).

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Figure 12.2.7

Adherent filter matrix (bayer pattern) comprising red (R), green (G), and blue (B) filters. Top row: the whole matrix. Bottom row: decomposition into color components. Increased sampling intervals in the component images result in lower resolution.

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Figure 12.2.8

Diagram of a Foveon X3 chip (From Foveon Inc.; http://www.foveon.com), which filters the color components (RGB) by wavelength-dependent absorption via silicon layers.

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Figure 12.2.9

Image of AO-stained fibroblast nucleus sampled optimally at the Nyquist rate (2x binning, A) and oversampled two times (no binning, B).

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Figure 12.2.10

Comparison of image file formats. (A) Original image of tubulin fibers in FITC-stained fibroblast and (B) its magnified fragment. This image (stored as an uncompressed TIFF, 8-bit grayscale) was used as the standard for calculation of the compression ratios (C_r). The image was compressed using: (C) lossless LZW (TIFF); (D) lossless deflation (PNG); JPEG2000 using either lossless algorithm (E) or lossy algorithm (F); and (G) lossy JPEG (JPG). Compression errors (lossy JPEG2000 and JPEG) are illustrated using difference images (pixel values were multiplied by 8).

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Literature Cited

Literature Cited
	Gonzalez, R.C. and Woods, R.F. 2002. Digital Image Processing, 2nd ed. Prentice Hall, Upper Saddle River, N.J.
	Holmes, T.J. 1992. Blind deconvolution of quantum-limited incoherent imagery: Maximum-likelihood approach. J. Opt. Soc. Amer. A-9:1052-1061.
	Holmes, T.J., Bhattacharyya, S., Cooper, J.A., Hanzel, D., Krishnamurti, V., Lin, W.C., Roysam, B., Szarowski, D.H., and Turner, J.N. 1995. Light microscopic images reconstructed by maximum-likelihood deconvolution. In Handbook of Biological Confocal Microscopy (J.B. Pawley, ed.) pp. 389-402. Plenum Press, New York.
	Holmes, T.J. and Liu, Y.-H. 1991. Acceleration of maximum-likelihood image restoration for fluorescence microscopy and other incoherent imagery. J. Opt. Soc. Amer. A-8:893-907.
	Houston, W. V. 1926. The fine structure and wave-length of the Balmer Lines. Astrophys. J. 64:81.
	Neifeld, M.A. 1998. Information, resolution and space-bandwidth product. Opt. Lett. 15:1477-1479.
	Rayleigh, J.W.S. 1879. Investigations in Optics with special reference to the Spectroscope. Phil. Mag. 8:261-274.
	Reichman, J. 2000. Handbook of Optical Filters for Fluorescence Microscopy. Chroma Technology Corp., Rockingham, Vt.
	Sparrow, C. M. 1916. On spectroscopic resolving power. Astrophys. J. 44:76.
	Stelzer, E.H.K. 1997. Contrast, resolution, pixelation, dynamic range and signal-to-noise ratio: Fundamental limits of resolution in fluorescence light microscopy. J. Microsc. 189:15-24.
	Van Dekker, A.J. and Bos, A. 1997. Resolution: A survey. J. Opt. Soc. Amer. A-14:547-557.
	Young, I.T. 1996. Quantitative microscopy. IEEE Eng. Med. Biol. 15:59-66.