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Image Processing and 2‐D Morphometry
Publication Name:
Current Protocols in Cytometry
Unit Number:
Unit 10.11
DOI:
10.1002/0471142956.cy1011s13
Online Posting Date:
May, 2001 Abstract
Image analysis and image processing are becoming commonplace operations in the cytometry environment. This very practical and useful unit describes the basics of image processing, including image enhancement, feature identification, and classification and segmentation. Written by an author with strong background in electron microscopy and image analysis, the unit is well furnished with figures and examples. Anyone needing a start in doing image analysis will find this very easy and useful reading, with suitable references for those wanting to move a step further.
Figures
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Figure 10.11.1(A) Simultaneous contrast illusion. The gray diamond against a white background will appear darker than the gray diamond against a black background. (B) Mach band effect. There appears to be a lighter and then a darker band at the transition to a darker gray-scale band. -
Figure 10.11.2Example of different spatial frequencies. Panel (A) has a higher spatial frequency than panel (B). To preserve the detail in panel A would require a higher sampling rate than for panel B. -
Figure 10.11.3(A) Image digitized at 100 dpi. (B) Same image digitized at 300 dpi. Both images were made from a photographic print of an electron microscopic image of a mitochondrion. The sampling rate in A was insufficient to preserve the detail in the image and information was lost. -
Figure 10.11.4Example of histogram sliding. (A) Original image. (B) Brightened image. -
Figure 10.11.5Example of histogram sliding with histogram stretching. (A) Original image. -
Figure 10.11.6The gamma function. (A) A gamma function of 1. (B) A gamma function of 1.8, which brightens the pixels of the darker grayscale bands. -
Figure 10.11.7(A) Original image, showing noise (gray specks). (B) Image after applying a 5 × 5 median filter to remove noise. -
Figure 10.11.8An HSI image may be represented as a double cone. The cone axis is intensity and ranges from black to white. The radius of the cone is the degree of color saturation, and the perimeter is the color hue. -
Figure 10.11.9Top: RGB images. The area of interest contains some diffuse blue staining that could not be segmented by threshold (shown by arrows). Bottom: Converting the image to HSI and then thresholding only the saturation band (lower left) results in an accurate discrimination of the area of interest. -
Figure 10.11.10Identification of areas of interest by the computer. Because of boundaries touching one another (arrows), an inaccurate count will result (see Fig. -
Figure 10.11.11Excluding areas touching the region of interest, the computer incorrectly identifies only 14 objects owing to boundaries touching one another. -
Figure 10.11.12After converting the image to binary format and applying a watershed filter to separate object boundaries, 22 objects are identified. -
Figure 10.11.13Objects can also be counted in an unbiased manner by using a counting frame. Objects that touch the solid line or fall outside the boundary of the dashed line are not counted. This method may be used to determine numerical density (objects/area). -
Figure 10.11.14A point counting grid with a counting frame may be used to determine the area of objects. If an object or its boundary falls in the quadrant to the upper right of the counting point (+) it is tallied.
Literature Cited
| Literature Cited | |
| Gittes, F. 1990. Estimating mean particle volume and number from random sections by sampling profile boundaries. J. Microsc. 158:1-18. | |
| Gundersen, H.J.G. 1988. The nucleator. J. Microsc. 151:3-21. | |
| Gundersen, H.J.G. and Jensen, E.B. 1987. The efficiency of systematic sampling in stereology and its prediction. J. Microsc. 147:229-263. | |
| Gundersen, H.J.G., Bagger, P., Bendtsen, T.F., Evans, S.M., Korbo, L., Marcussen, N., Moller, A., Nielsen, K., Nyengaard, J.R., Pakkenberg, B., Sorensen, F.B., Vesterby, A., and West, M.J. 1988a. The new stereological tools: dissector, fractionator, and point sampled intercepts and their use in pathological research and diagnosis. Acta Pathol. Microbiol. Immunol. Scand. 96:857-881. | |
| Gundersen, H.J.G., Bendtsen, T.F., Korbo, L., Marcussen, N., Moller, A., Nielsen, K., Nyengaard, J.R., Pakkenberg, B., Sorensen, F.B., Vesterby, A., and West, M.J. 1988b. Some new, simple and efficient stereological methods and their use in pathological research and diagnosis. Acta Pathol. Microbiol. Immunol. Scand. 96:379-394. | |
| Jensen, E.B. 1991. Recent developments in the stereological analysis of particles. Ann. Inst. Statist. Math. 43:455-468. | |
| Weibel, E.R. 1979. Stereological Methods. Practical Methods for Biological Morphometry. Academic Press, London. | |
| Key References | |
| Jähne, B. 1993. Digital Image Processing: Concepts, Algorithms, and Scientific Applications, 2nd ed. SpringerVerlag, New York. | |
| General textbook on digital imaging covering more advanced concepts. | |
| Robinson, J.P. and Turek, J.J. 1999. Microscope image processing and analysis. Encyclopedia of Electronics and Engineering, Vol. 13 9-21. John Wiley Sons, New York. | |
| General basic reference for digital imaging. Includes background on confocal and electron microscopy. | |
| Serra, J. 1993. Image Analysis and Mathematical Morphology. Academic Press, London. | |
| Foundational text for understanding morphological operations in digital imaging. | |
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