Binary Image Editing

  • John C. Russ

Abstract

The binary pixel-based representation that results from discrimination of a grey scale image may not perfectly delineate all of the features present. This may be due to noise or other imperfections in the original image, to inadequate processing methods, or to something inherent in the image itself such as touching objects. It may also happen than the original image covers an area that is larger than the region which we desire to measure. A simple example is the determination of the number and size distribution of spots on a leaf produced by aerial spraying. It is comparatively easy to obtain images of one or more leaves from which the spots may be measured, but the image will generally be rectangular or square, and the leaves are irregular in shape. Counting should be performed only within the leaf area. Similarly, some microscopes have a limiting aperture for illumination that is more or less circular, and measurements should be performed only within that region. Many other examples ranging from counting of cell colonies within a petri dish to trees in orchards viewed in aerial photos present a similar problem (Figure 6-1).

Keywords

Dust Anisotropy Graphite Sulfide Ferrite 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ball and McCartney (1981) The measurement of atomic number and composition in an SEM using backscattered detectors Journal of Microscopy 124, 57–68CrossRefGoogle Scholar
  2. S. Beucher, C. Lantejoul (1979) Use of Watersheds in Contour Detection Proc. Int’l Workshop on Image Processing, CCETT, Rennes, FranceGoogle Scholar
  3. D.S. Bright, D.E. Newbury (1986) Euclidean distance mapping for shape characterization of alloy grain boundaries Microbeam Analysis 1986 (A.D. Romig, W.F. Chambers, ed.) San Francisco Press 521-524Google Scholar
  4. J.-L. Chermant, M. Coster, J.-P. Jernot, J.-L. Dypain (1981) Morphological Analysis of Sintering J. Microscopy 121 89–98CrossRefGoogle Scholar
  5. M. Coster, J-L. Chermant (1985) Prècis D Analyse D’lmages Èditions du Centre National de la Recherche Scientifique, ParisGoogle Scholar
  6. P.E. Danielsson (1980) Euclidean Distance Mapping Computer Graphics and Image Processing 14 227–248CrossRefGoogle Scholar
  7. P. Davy (1981) Interpretation of phases in a material J. Microscopy 121 3–12CrossRefGoogle Scholar
  8. R. Ehrlich, S.K. Kennedy, S.J. Crabtree, R.L. Cannon (1984) Petrographic Image Analysis: I. Analysis of Reservoir Pore Complexes J. Sedimentary Petrology 54 # 4 1365–1378Google Scholar
  9. A.G. Fabbri (1984) Image Processing of Geological Data, Van Nostrand Reinhold, NYGoogle Scholar
  10. J. Feder (1988) Fractals Plenum Press, New York NYGoogle Scholar
  11. A.G. Flook (1978) Use of dilation logic on the Quantimet to achieve fractal dimension characterization of texture and structured profiles, Powder Technology 21,295–298CrossRefGoogle Scholar
  12. C.R. Giardina, E.R. Dougherty (1988) Morphological Methods in Image and Signal Processing Prentice Hall, Englewood Cliffs NJGoogle Scholar
  13. J.P. Jernot (1982) Thése de Doctorat és-Science, Université de CaenGoogle Scholar
  14. B.H. Kaye (1978) Sequential mosaic amalgamation as a strategy for evaluating fractal dimensions of a fineparticle profile, Report #21 Institute for Fineparticle Research, Laurentian Univ.Google Scholar
  15. B.H. Kaye (1984) Multifractal description of a rugged fineparticle profile, Particle Characterization 1,14–21CrossRefGoogle Scholar
  16. B.H. Kaye (1986) Image analysis procedures for characterizing the fractal dimension of fineparticles, Proc. Particle Technol. Conf. NürnbergGoogle Scholar
  17. Koch, von, H. (1904) Sur une courbe continue sans tangente, obtenue par une construction geometrique elementaire Arkiv für Matematik, Astronomie och Fysik 1, 681Google Scholar
  18. C. Lantejoul, S. Beucher (1981) On the use of the geodesic metric in image analysis, J. Microscopy 121, 39CrossRefGoogle Scholar
  19. B. Lay (1985) Morphology an image processing software package IEEE Workshop on Computer Architecture for Pattern Analysis and Image Data Base Management, p. 463-469Google Scholar
  20. S. Levialdi (1983) Neighborhood Operators: An Outlook in Pictorial Data Analysis (R.M. Haralick, ed.) Proc. 1982 Nato Advanced Study Inst., Bonas, France, Springer Verlag New York, F4, 1–4Google Scholar
  21. B.B. Mandelbrot (1982) The Fractal Geometry of Nature, W.H. Freeman, San FranciscoGoogle Scholar
  22. G. Matheron (1975) Random Sets and Integral Geometry Wiley. NY 1975Google Scholar
  23. G.A. Moore, L.L. Wyman (1963) Quantitative Metallography with a Digital Computer: Application to a Nb-Sn Superconducting Wire Jour. Res. NBS 67 A 127–147Google Scholar
  24. T. Pavlidis (1979) Filling Algorithms for Raster Graphics Computer Graphics and Image Processing 10 126–141CrossRefGoogle Scholar
  25. T. Pavlidis (1980) A Thinning Algorithm for Discrete Binary Images Computer Graphics and Image Processing 13 142–157CrossRefGoogle Scholar
  26. T. Pavlidis (1982) Algorithms for Graphics and Image Processing, Computer Science Press, Rockville MDGoogle Scholar
  27. A. Rosenfeld, J.L. Pfaltz (1966) Sequential Operations in Digital Picture Processing J. ACM 13, p. 471–494CrossRefGoogle Scholar
  28. J.C. Russ (1984) Implementing a new skeletonizing method J. Microscopy 136 p. RP7CrossRefGoogle Scholar
  29. J.C. Russ (1986) Practical Stereologv Plenum, New YorkGoogle Scholar
  30. J.C. Russ, J.C. Russ (1984) Enhancement and Combination of X-ray maps and Electron Images, Microbeam Analysis 1984. San Francisco Press, 161Google Scholar
  31. J.C. Russ, J.C. Russ (1986) Automatic editing of binary images for feature isolation and measurement, Microbeam Analysis 1986 (A.D. Romig, W.F. Chambers, ed.) San Francisco Press, 505Google Scholar
  32. J. Ch. Russ, J.C. Russ (1988) Improved implementation of a convex segmentation algorithm Acta Stereologica7#l 33–40Google Scholar
  33. W.O. Saxton, T.L. Koch (1982) Interactive image processing with an off-line minicomputer: organization, performance and applications J. of Microscopy 127 69–83CrossRefGoogle Scholar
  34. H. Schwarz, H.E. Exner (1980) Implementation of the concept of fractal dimensions on a semi-automatic image analyzer, Powder Tech. 27,207CrossRefGoogle Scholar
  35. K.L. Scrivener, P.L. Pratt (1987) Observations of Cements and Concretes by Backscattered Electron Imaging Proc. RMS 22 # 3 167Google Scholar
  36. J. Serra (1982) Image Analysis and Mathematical Morphology, Academic Press, LondonGoogle Scholar
  37. B. Shapiro, J. Pisa, J. Sklansky (1981) Skeleton Generation from X,Y Boundary Sequences Computer Graphics and Image Processing 15 136–153CrossRefGoogle Scholar
  38. D. Vollath (1986) Some fundamental considerations to precede image analysis Bull. Mater. Sci. 8/2 169–182Google Scholar

Copyright information

© Plenum Press, New York 1990

Authors and Affiliations

  • John C. Russ
    • 1
  1. 1.North Carolina State UniversityRaleighUSA

Personalised recommendations