Electronic and Computer Image Enhancement in Light Microscopy

Abstract

Image processing techniques allow low contrast microscopic specimens to be observed and quantified. These routines can be applied to all types of light microscopes and are used to reduce noise and extract features of interest.

Keywords: image processing; contrast enhancement; digital filtering; edge detection; feature extraction

Figure 1.

Various digital masks applied to a microscopic image of a haemacytometer grid. Various masks are used to improve contrast or enhance particular features within image. (a) Unprocessed brightfield image taken with a 20× objective. (b) When a sharpening filter is applied to the image more detail is seen in the gradations vastly improving the image quality. (c) A Sobel filter approximates the first derivative of intensity with respect to distance. Areas which are uniform in intensity (i.e. no gradient) result in a black background (low grey value) while abrupt changes in intensity are returned as high grey values. (d) A Laplace transform calculates the second derivative of intensity with respect to distance. (e) A horizontal line filter only detects horizontal lines in the grid. (f) A vertical line filter only detects vertical lines in the grid.

Figure 2.

Examples of various image‐processing routines on light and fluorescence microscopic images of sand dollar eggs. (a) A differential interference contrast microscopic image of a fertilized sand dollar egg possesses both low‐contrast (plasma membrane and fertilization envelope) and high‐contrast (pigment granules in egg jelly) components. (b) Intensity profile of image from (a) that has been skewed 56% for display purposes. The height represents grey values at each pixel. Pseudocolour is used here to display depth (red is front, violet is back). (c) A Laplace filter applied to the image in (a) results in a sharpening of the plasma membrane, the fertilization envelope, and an outline of the pigment granules in the egg jelly. (d) Intensity profile of image from (c) showing that cell, fertilization envelope, and pigment granules are clearly highlighted. (e) Unfertilized sea urchin egg (Arbacia punctulata) loaded with the fluorescent indicator dye Calcium green 5N. Following background subtraction no labelling is apparent even though the dye has been loaded into the cell since Calcium levels are far below the Kd for this dye. (f) 7 min after sperm were added to the suspension the fluorescence intensity increases within the egg indicating an increase in intracellular calcium. (g) 10 min after the addition of sperm there was a significant increase in intracellular calcium levels across the entire egg. (h) Grey values were measured at different points across the microscopic field to quantify the increase in fluorescence with time. Images were taken once a second with the four points taken inside the egg showing a marked increase in grey value by 400 sec after sperm addition while the one point taken outside the egg showed no change after sperm addition.

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References

Cardullo RA and Alm EJ (1998) Introduction to image processing. In: Sluder G and Wolf DE (eds) Video Microscopy. San Diego, CA: Academic Press.

Further Reading

Inoué S and Spring K (1997) Video Microscopy, 2nd edn. New York: Plenum Press.

Russ JC (1990) Computer Assisted Microscopy: The Measurement and Analysis of Images. New York: Plenum Press.

Russ JC (1995) The Image Processing Handbook, 2nd edn. Boca Raton, FL: CRC Press.

Schotten D (1993) Electronic Light Microscopy. New York: Wiley‐Liss.

Sluder G and Wolf DE (1998) Video Microscopy. San Diego, CA: Academic Press.

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How to Cite close
Cardullo, Richard A(Apr 2001) Electronic and Computer Image Enhancement in Light Microscopy. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1038/npg.els.0002990]