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Two‐Dimensional Image Processing and Analysis

Kenneth R. Castleman¹

¹Perceptive Scientific Instruments, Inc., League City, Texas

Publication Name:

Current Protocols in Cytometry

Unit Number:

Unit 10.5

DOI:

10.1002/0471142956.cy1005s00

Online Posting Date:

May, 2001

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Abstract

Processing and analysis of image data require very different methods than those used in flow cytometry. Image processing is used to correct inaccuracies in an image and make it easier to interpret, whereas image analysis is used to extract quantitative data about a specimen. This unit provides an overview of image processing methods. Keywords: image processing; image analysis; overview Processing and analysis of image data require very different methods than those used in flow cytometry

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Introduction
Image Processing
Image Analysis
Image Processing Software
Literature Cited
Figures
Tables

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Figures

Figure 10.5.1

Gray-level histogram. (A) Cell image obtained by fluorescence in situ hybridization (FISH). (B) Corresponding gray-level histogram, in which the number of pixels in the image having each specific gray level (vertical) is plotted versus gray level (horizontal).

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Figure 10.5.2

Modulation transfer function (MTF) of a perfect lens. The MTF indicates the extent to which a lens passes image detail of different spatial frequencies. The cutoff frequency, f_c = 2NA/, improves with higher numerical aperture (NA) of the objective lens and with shorter wavelength illumination (). Here spatial frequency is referred to the specimen plane.

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Figure 10.5.3

Digital convolution. The output image is made up of scaled, weighted averages of the pixels in neighborhoods of the corresponding input pixels. The kernel matrix specifies the weights.

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Figure 10.5.4

Convolution. (A) Input image; (B) smoothed image (5 × 5); (C) enhanced image; (D) severely enhanced image.

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Figure 10.5.5

The three steps of pattern recognition.

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Figure 10.5.6

Cell classifier probability density functions (pdfs). The pdfs are p₁(x) and p₂(x); the prior probabilities are P₁ and P₂.(A) Hypothetical diameter pdfs for RBCs and WBCs; (B) pdfs scaled by a prior probability that two-thirds of the specimen are RBCs. T is the maximum-likelihood decision threshold, and the shaded area is P₂ times the probability that a WBC will be misclassified.

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Literature Cited

Literature Cited
	Baxes, G. A. 1994. Digital Image Processing: Principles and Applications. John Wiley & Sons, New York.
	Castleman, K. R. 1996. Digital Image Processing. Prentice-Hall, Englewood Cliffs, N. J.
	Gonzalez, R. C. and Woods, R. E. 1992. Digital Image Processing. Addison-Wesley, Reading, Mass.
	Jain, A. K. 1989. Fundamentals of Digital Image Processing. Prentice-Hall, Englewood Cliffs, N. J.
	Pratt, W. K. 1991. Digital Image Processing, 2nd ed. John Wiley & Sons, New York.
	Russ, J. C. 1995. The Image Processing Handbook, 2nd ed. CRC Press, Boca Raton, Fla.
Key Reference
	Castleman, 1996. See above 1996.
Describes in detail all operations discussed in this paper.