Mathematical Methods Applied to Image Processing in Medicine

  • J. M. S. Prewitt
Part of the Lecture Notes in Medical Informatics book series (LNMED, volume 9)


Image processing is a mathematical and engineering discipline which has as its domain digitized pictures, that is, pictures which have been spatially sampled and photometrically quantitized by an electro-optical scanning device and have been recorded on a medium such as magnetic tape, magnetic disc or computer memory core. Each spatially discrete image element is called a pixel and with each pixel is associated a photometric quantity such as brightness, transmittance, extinction or optical density. Processing refers to mathematical and logical operations performed on these image pixels in order to extract desired information. Medical images of interest arise from blood smears showing white cells and showing red cells, Papanicolaou cervical smears, radiographs, electron micrographs, scintigrams, etc.


Feature Selection Projection Data Feature Selection Algorithm Gradient Magnitude Lawrence Berkeley Laboratory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anderson, T.W. (1958). “An Introduction to Multivariate Statistical” Wiley, New York.Google Scholar
  2. Elias, H. (1971). Three-dimensional structure identified from single sections. Science 174, 993.CrossRefGoogle Scholar
  3. Prewitt, J.M.S. (1970b). Selection of variables and prediction of performance in decision-theoretic appraoches to diagnosis. Proc. Conf. Computer Applications Radiol. University of Missouri-Columbia, September 1970.Google Scholar
  4. Prewitt, J.M.S. (1971) Diagnostic and predictive applications for cluster analysis. J. Inform. Med., (IRIA, Institut d’Informatique et d’Automatique) Rocquencourt, France.Google Scholar
  5. Prewitt, J.M.S. (1972a) Experiments with statistical and quasi-statistical methods in diagnosis. In “Computer Diagnosis and Diagnosis Methods” (J. Jacques, ed.) Chapter 17, C.C. Thomas, Springfield, 111Google Scholar
  6. Prewitt, J.M.S. (1972b). A versatile hierarchical clustering algorithm with objective function and objective measure. Comput. Programs Biomedicine, 2: 297–314.CrossRefGoogle Scholar
  7. Prewitt,J.M.S. (1972d). Parametric and Nonparametric Recognition by Computer: An Application to Leukocyte Image Processing. In “Advances in Computers”, Vol. 12 ( M.Rubinoff, ed.) Academic Press, New York, page 295–414.Google Scholar
  8. Prewitt, J.M.S., and Mendelsohn, M.L. (1966a) A general approach to image analysis by parameter extraction. Proc. Conf. Uses Computer Radiol. Univ. Missouri- Columbia, pp. A2–A41.Google Scholar
  9. Prewitt, J.M.S., and Mendelsohn M.L. (1966b) The analysis of cell images. Ann. N.Y. Acad. Sci. 128, 1035.CrossRefGoogle Scholar
  10. Rao, C.R. (1952) “Advanced Statistical Methods in Biometrie Research”. Wiley, New York.Google Scholar
  11. Shepp, L.A. and Kruskal, J.B. (1978) “Computerized Tomography: The New Medical X-ray Technology”. American Mathematical Monthly 85 (6): 420–439.CrossRefMATHMathSciNetGoogle Scholar
  12. IEEE Transactions on Nuclear Science, Vol. NS-26, No. 2, Part 2, April, 1979. Special issue for the workshop on Physics and Engineering in Computerized Tomography.Google Scholar
  13. Prewitt, J.M.S. (1976) “New Vistas in Medical Reconstruction Imagery” in Digital Pro¬cessing of Biomedical Images-, Plenum, New York. pp. 133–160.Google Scholar
  14. Bud, T.F., Goldberg, G.T., Huesman, R.H. “Mission Computed Tomography” in Image Re-construction from Projections: Implementation and Application, G.T. Herman, Editor, Springer-Verlag, New York, 1979. pp. 147–246.Google Scholar
  15. Prewitt, J.M.S., “Contemporary Medical Microscopy: The Advent of Intelligent Microscopes”. IEEE Transactions on Nuclear Science, Vol. NS-27, No. 3, June, 1980: 1207–1217. (1980a)CrossRefGoogle Scholar
  16. Prewitt, J.M.S., “Object Enhancement and Extraction” in Picture Processing and Psycho-pictorics, Academic Press, New York. pp. 75–149 (1970a)Google Scholar
  17. Prewitt, J.M.S., “Micrographia Moderna”. IEEE Transactions on Pattern Analysis and Machine Intelligence, September, 1980, in press.Google Scholar


  1. Budinger, T.F., and Gullberg, G.T., “Three-dimensional Reconstruction in Nuclear Medicine by Iterative Least-Squares and Fourier Transform Techniques, Lawrence Berkeley Laboratory Report 2146 (1974).Google Scholar
  2. Cormack, A.M., “Reconstruction of Densities from their Projections with Applications to Radiological Physics, “Physics in Medicine and Biology” 18: 195 - 207, (1973).CrossRefGoogle Scholar
  3. Cormack, A.M., “Representation of a Function by its Line Integrals, with some Radio¬logical Applications, I,” J. Appl.Physics, 34: 2722–2727, (1963).CrossRefMATHGoogle Scholar
  4. Cormack, A.M., “Representation of a Function by its Line Integrals with some Radio¬logical Applications, II,” J. Appl.Physics 35: 2908–2913 (1964).CrossRefMATHGoogle Scholar
  5. Gordon, R., and Herman, G.T., “Three Dimensional Reconstruction from Projections: A Review of Algorithms,” International Review of Cytology 38;. 111–151, (1974).CrossRefGoogle Scholar
  6. Hounsfield, G.N, G.N., “A Method of an Apparatus for Examination of a Body by Radiation such as X or Gamma Radiation,” The Patent Office, London, Patent Specification 1283915 (1972).Google Scholar
  7. IEEE Transactions on Nuclear Science, NS-21(3) (1974).Google Scholar
  8. Radon, J., “Uber die Bestimmung von Funktionen durch ihre integral werte längs gewisser Mannigfaltigkeiten” (On the determination of functions from their integrals along certain manifolds), Berichte Sachsische Physische Klasse 69: 262–279, (1917).Google Scholar
  9. Prewitt, J.M.S., “Algorithms in Computerized Tomography” in Medical Imaging Techniques, Plenum, New York, pp. 287–312, (1979).Google Scholar

Copyright information

© Online Conferences Ltd., Uxbridge, England 1981

Authors and Affiliations

  • J. M. S. Prewitt

There are no affiliations available

Personalised recommendations