Processing of Medical Image Sequences

  • W. Spiesberger
  • M. Tasto
Chapter
Part of the Springer Series in Information Sciences book series (SSINF, volume 5)

Abstract

Processing medical images has become a large and important application area of digital image processing techniques. Such techniques have been applied to a wide variety of images, namely X-ray images (a review of methods is given in [7.1]), scintigrams, ultrasonic and thermographic images, and microscopic image processing (as presented in [7.1]). A more general description of medical image processing is contained in [7.2].

As in other application areas, time sequence processing has developed relatively late, due to technical problems such as lack of memory and processing power, and probably due to a lack of concepts as well. However, the need has existed for some time [7.3].

Recent activities in time sequence processing in the medical field can be roughly classified into three categories:
  1. a)

    The determination of size, shape, volume, etc. of certain organs as a function of time. The input is a time sequence of images, and the output usually is a sequence of numbers or low-dimensional vectors, representing the time-behaviour of the measured values. These problems will be discussed in Sect.7.1.

     
  2. b)

    Functional imaging. Again the input is a time sequence of images, the output is a single image showing in condensed form the functioning, e.g. the velocity of the blood, etc., in human organs. This topic is presented in Sec t.7.2.

     
  3. c)

    Image enhancement. The input is a time sequence of images, and the output may be either a single image or a sequence of images, whose quality has beer improved with respect to noise, blurring, etc. In Sect.7.3 we will discuss a specific example of generating a single picture from a sequence showing improved noise and blurring properties.

     

Finally, we shall briefly discuss image sequences that are not time sequences. Although work in this area is mostly in the beginning state, we fee it necessary to at least shortly review it here, because these may have some interesting cross-connection to time sequences. Examples to be discussed will be sequences with respect to spatial coordinates, and frequency characteristics of the image-generating signal (Sect.7.4).

Since most activities as well as our own experience have been in the X-ray field, we will concentrate on X-ray images.

Keywords

Boundary Point Functional Image Search Range Boundary Detection Picture Element 
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.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 7.1
    A. Rosenfeld (ed.): Digital Picture Analysis, Topics in Applied Physics, Vol.11 (Springer, Berlin, Heidelberg, New York 1976)MATHGoogle Scholar
  2. 7.2
    M. Tasto: “Maschinelle Bildverarbeitung in der Medizin”, in Erfassung und maschinelle Verarbeitung von Bilddaten, ed. by H. Kazmierczak (Springer, Berlin, Heidelberg, New York 1980)Google Scholar
  3. 7.3
    H.H. Nagel: “Analysis Techniques for Image Sequences”, Proc. 4th Int. Joint Conf. on Pattern Recognition, Kyoto, Japan, Nov., 1978, pp.186–211Google Scholar
  4. 7.4
    P.H. Heintzen (ed.): Roentgen-, Cine-, and Videodensitometry (Thieme, Stuttgart 1971)Google Scholar
  5. 7.5
    P.H. Heintzen, J.H. Bürsch (eds.): Roentgen-Video-Techniques (Thieme, Stuttgart 1978)Google Scholar
  6. 7.6
    W.K. Wiscomb: “A Hardware System for Man-Machine Interaction in the Study of Left Ventricular Dynamics”, in P.H. Heintzen (ed.): Roentgen-, Cine-, and Videodensitometry (Thieme, Stuttgart 1971)Google Scholar
  7. 7.7
    GRAFOMED, Preliminary technical description, Philips Corp.Google Scholar
  8. 7.8
    H.R. Schelbert, H. Kreuzer, J. Dittrich, H. Reitsma, P. Spiller: Videometrische Ventrikeiflächenbestimmung mit haibhautomati scher Korrektur des Bildhintergrundes. Res. Exp. Med. 158, 66–74 (1972)CrossRefGoogle Scholar
  9. 7.9
    B.G. Trenholm, D.A. Winter, G.D. Reimer, D. Mymin, E.L. Landsdown, G.P. Sharma: Automated ventricular volume calculations from single plane images. Radiology 112, 299–304 (1974)Google Scholar
  10. 7.10
    C.J. Slager, S.H.C. Reiber, J.C.H. Schuurbiers, G.T. Meester: “Automated Detection of Left Ventricular Contour Concept and Application”, in P.H. Heintzen, J.H. Bürsch (eds.): Roentgen-Video-Techniques (Thieme, Stuttgart 1978)Google Scholar
  11. 7.11
    CK. Chow, T. Kaneko: “Computer Calculation of Left Ventricular Volumes from a Cineangiogram”, Proc. Quantitative Imaging in the Biomed. Sciences, SPIE, May 1971Google Scholar
  12. 7.12
    M. Tasto: “Motion extraction for left-ventricular volume measurement”. IEEE Trans. BME-22, 207–213 (1974)Google Scholar
  13. 7.13
    M. Tasto: “Guided Boundary Detection for Left Ventricular Volume Measurement”, Proc. 1st Int. Joint Conf. on Pattern Recognition, Washington D.C., Oct., 1973Google Scholar
  14. 7.14
    W. Spiesberger, M. Tasto: The automatic measurement of medical X-ray photographs. Philips Tech. Rev. 35, 170–180 (1975)Google Scholar
  15. 7.15
    M. Tasto, M. Felgendreher, W. Spiesberger, P. Spiller: “Comparison of Manual versus Computer Determination of Left Ventricular Boundaries from X-Ray Cineangiograms”, in P.H. Heintzen, J.H. Bürsch (eds.): Roentgen-Video-Techniques (Thieme, Stuttgart 1978)Google Scholar
  16. 7.16
    S. Eimo, U. Kuhawara, U. Fujta, S. Sasayama, C. Kawai: “Automatic Processing of Cineangiography Images of Left Ventricle”, Proc. 4th Int. Joint Conf. on Pattern Recognition, Kyoto, Japan, Nov., 1978, pp.740–742Google Scholar
  17. 7.17
    A. Rosenfeld: Picture Processing by Computer (Academic, New York 1969)MATHGoogle Scholar
  18. 7.18
    N.R. Silverman: “Videodensitometry and Videokymography for Evaluation of Myocardial Contractibility”, in P.H. Heintzen, J.H. Bürsch (eds.): Roentgen-Video-Techniques (Thieme, Stuttgart 1978)Google Scholar
  19. 7.19
    H.C. Smith, R.L. Frye, G.D. Davis, J.R. Pluth, R.E. Sturm, E.H. Wood: “Simultaneous Indicator Dilution Curves at Selected Sites in the Coronary Circulation and Determination of Blood Flow in Coronary Artery-Saphenous Vein Crafts by Roentgen-Videodensitometry”, in P.H. Heintzen (ed.): Roentgen-, Cine-, and Videodensitometry (Thieme, Stuttgart 1971), pp.152–157Google Scholar
  20. 7.20
    D. Decker, E. Epple, F. Heuck, M. Nagel, G. Polony: Anwendung des Mehr-fachdetektor-Meßverfahrens in der Cine-Densitometrie. Biomed. Tech. 20, Ergänzungsband, 265–266 (1975)Google Scholar
  21. 7.42
    R.D. Penn, M.G. Belanger, W.A. Yasnoff: Ventricular Volume in Man Computed from CAT Scans. Ann. Neurol. 3, 216–223 (1978)CrossRefGoogle Scholar
  22. 7.43
    H.U. Lemke, H.S. Stiehl, H. Scharnweber, D. Jackel: “Applications of Picture Processing, Image Analysis and Computer Graphics Techniques to Cranial CT Scans”, Proc. IEEE Conf. on Computer-Aided Analysis of Radiological Images, Newport Beach, CA, June, 1979Google Scholar
  23. 7.44
    H.S. Stiehl: “Automated Processing and Analysis of Cranial Computed Tomograms”, Institut f. tech. Informatik, COMPACT Tech. Rpt., Technische Universität Berlin (1979)Google Scholar
  24. 7.45
    R.A. Schulz, P.M. Joseph, S.K. Hilal: Frontal and Lateral Views of the brain reconstructed from EMI axial slices. Radiology 125, 701–710 (1977)Google Scholar
  25. 7.46
    H.K. Huang, J.C. Mazziotta: Heart imaging from computerized tomography. Comput. Tomography 2, 37–44 (1978)CrossRefGoogle Scholar
  26. 7.47
    R.S. Ledley, C.M. Park: “Molded Picture Representation of Whole Body Organs Generated from CT Scan Sequences”, Proc. 1st Annual Symposium on Computer Application in Medical Care, Washington D.C., Oct., 1977, pp.363–367Google Scholar
  27. 7.48
    M. Schlindwein, B. Vering: Three-dimensional display of human organs from computed tomograms. Biomed. Tech. 24, Ergänzungsband, 219–220 (1979)CrossRefGoogle Scholar
  28. 7.49
    B.I. Soroka, R.K. Bajcsy: “A Program for Describing Complex Three-Dimensional Objects Using Generalized Cylinders as Primitives”, Proc. IEEE Comp. Science Conf. on Pattern Recognition and Image Processing, Chicago, May/June, 1978, pp.331–339Google Scholar
  29. 7.50
    H. Fuchs, Z.M. Kedem, S.P. Uselton: Optimal surface reconstruction from planar contours. Commun. ACM 20, 693–702 (1977)MathSciNetMATHCrossRefGoogle Scholar
  30. 7.51
    H.N. Christiansen, T.W. Sederberg: “Conversion of Complex Contour Line Definitions Into Polygonal Element Mosaics”, Proc. 5th Annual Conf. on Computer Graphics and Image Processing, Atlanta, GA, Aug., 1978Google Scholar
  31. 7.52
    E. Artzy, G.T. Herman: “Boundary Detection in Three Dimensions with a Medical Application”; Tech. Rpt. No. MIPG9, Medical Image Processing Group, State University of New York at Buffalo (1978)Google Scholar
  32. 7.53
    H.K. Liu: Two- and three-dimensional boundary detection. Comput. Graph. Image Proc. 6, 123–134 (1977)CrossRefGoogle Scholar
  33. 7.54
    G.T. Herman, H.K. Liu: “Dynamic Boundary Surface Detection”, Proc. Symposium on Computer-Aided Diagnosis of Medical Images, Coronado, CA, Nov., 1976Google Scholar
  34. 7.55
    M.G. Ort, C.A. Mistretta, F. Kelcz: An improved technique for enhancing small periodic contrast changes in television fluoroscopy. Opt. Eng. 12, 169–175 (1973)Google Scholar
  35. 7.56
    F. Kelcz, C.A. Mistretta: Absorption-edge fluoroscopy using a three-spectrum technique. Med. Phys. 3, 159–168 (1976)CrossRefGoogle Scholar
  36. 7.57
    R.A. Kruger: Digital k-edge-subtraction radiography. Radiology 125, 243–245 (1977)Google Scholar
  37. 7.58
    J. Heinzerling, M. Schlindwein: X-ray absorption-edge imaging with nonlinear reconstruction. Biomed. Tech. 24, Erga’nzungsband, 193–194 (1979)CrossRefGoogle Scholar
  38. 7.59
    S.Ch. Pang, S. Gena: Corrections for X-ray polychromatic effects on three-dimensional reconstruction. IEEE Trans. NS-23, 623 (1976)Google Scholar
  39. 7.60
    R.A. Rutherford et al.: “Quantitative Information from EMI Scanner”, Tech. Rpt., Dept., of Medical Biophysics, Univ. of Manchester (1974/75)Google Scholar
  40. 7.61
    A. Makovski et al,: Energy-dependent reconstruction in X-ray computerized tomography. Comput. Biol. Med. 1, 325–336 (1976)CrossRefGoogle Scholar
  41. 7.62
    R. Rieckeheer: Biochromatic CT pictures from dual spectrum scanning. Medita, Sonderheft I, 79–81 (1978)Google Scholar
  42. 7.63
    G. Kowalski, R. Rieckeheer, W. Wagner: New means for picture formation in computer tomography. Acta Electron. 22, 51–63 (1979)Google Scholar
  43. 7.64
    M. Tasto: Parallel array processors for digital image processing. Opt. Acta 24, 391–406 (1977)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1981

Authors and Affiliations

  • W. Spiesberger
  • M. Tasto

There are no affiliations available

Personalised recommendations