Skip to main content
SpringerLink
Log in

Confirmatory aspects in factor analysis of image sequences

  • 7. Factor Analysis
  • Conference paper
  • First Online:
Information Processing in Medical Imaging (IPMI 1991)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 511))

Abstract

Confirmatory approach in factor analysis of image sequences is specified by an employment of considerable initial information in the processes of factor extraction and rotation and by the possibility to verify hypotheses assumed in advance. Confidence interval for factor contribution is introduced and its utility in an assessment of factor significance demonstrated. Based on a partial apriori knowledge of resulting factor image, the method for a multiple subtraction of images is derived and its noise-rejection properties demonstrated. Quantitative transformation of factor curves into the compartmental scheme is described and the method is applied to a dynamic radionuclide study of renal function.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Barber DC (1980). The use of principal components in the quantitative analysis of gamma camera dynamic studies. Phys.Med.Biol. 25:283–292.

    Google Scholar 

  • Barber DC and Nijran KS (1982). Factor analysis of dynamic radionuclide studies. In: Nuclear Medicine and Biology. Raynaud C (ed), Pergamon Press, Paris, pp. 31–34.

    Google Scholar 

  • Blaufox MD (1989). Compartment analysis of the radiorenogram. In: Evaluation of renal function and disease with radionuclides. Blaufox MD (ed), Karger, Basel, pp. 98–107.

    Google Scholar 

  • Britton KE and Nimmon CC (1989). The measurement of renal transit times by deconvolution analysis. In: Evaluation of renal function and disease with radionuclides. Blaufox MD (ed), Karger, Basel, pp. 108–116.

    Google Scholar 

  • Carlsen O, Kvinesdal B and Nathan E (1986). Quantitative evaluation of iodine-123 hippuran gamma camera renography in normal children. J.Nucl.Med. 27:117–127.

    Google Scholar 

  • DiPaola R, Bazin JP, Aubry F, Aurengo A, Cavailloles F, Herry JY and Kahn E. (1982). Handling of dynamic sequences in nuclear medicine. IEEE Trans.Nucl.Sci. NS-29:1310–1321.

    Google Scholar 

  • Hannequin P, Liehn JC and Valeyre J (1989). The determination of the number of statistically significant factors in factor analysis of dynamic structures. Phys.Med.Biol. 34:1213–1227.

    Google Scholar 

  • Harman HH (1960). Modern factor analysis. The University of Chicago Press, Chicago, IL.

    Google Scholar 

  • Houston AS (1988). The use of cluster analysis and constrained optimisation techniques in factor analysis of dynamic structures. In: Mathematics and Computer Science. Viergever MA and Todd-Pokropek AE (eds), Springer-Verlag Berlin, Heidelberg, pp. 491–503.

    Google Scholar 

  • Joereskog KG (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika 34:183–202.

    Google Scholar 

  • Malinowski ER (1977a). The theory of error in factor analysis. Anal.Chem. 49:606–612.

    Google Scholar 

  • Malinowski ER (1977b). Determination of the number of factors and the experimental error in a data matrix. Anal.Chem. 49:612–617.

    Google Scholar 

  • Martel AL and Barber DC (1989). A new approach to dynamic study analysis. In: Information Processing in Medical Imaging. Proceedings of XI-th International Conference, June 19–23, 1989, Berkeley, CA.

    Google Scholar 

  • Mueller-Schauenburg W (1973). A new method for multi-compartment pharmacokinetic analysis: the eigenvector decomposition principle. Eur.J.Clin.Pharmacol. 6:203–206.

    Google Scholar 

  • Mueller-Schauenburg W (1976). On some applications of the eigenvector decomposition principle in pharmacokinetic analysis. In: Mathematical Models in Medicine. Berger J et al (eds), Springer-Verlag Berlin, Heidelberg, New York, pp.226–242.

    Google Scholar 

  • Nijran KS and Barber DC (1986). Factor analysis of dynamic function studies using a priori physiological information. Phys.Med.Biol. 31:1107–1117.

    Google Scholar 

  • Nijran KS and Barber DC (1988). The importance of constraints in factor analysis of dynamic structures. In: Information Processing in Medical Imaging. deGraaf CN and Viergever MA (eds), Plenum Press, New York, pp. 521–529.

    Google Scholar 

  • Oster ZH, Som P, Bazin JP, DiPaola M, Raynaud C, Atkins HL and DiPaola R (1989). The role of factor analysis in the evaluation of new radiopharmaceuticals. Nucl.Med.Biol. 16:85–89.

    Google Scholar 

  • Oster ZH, Som P, Raynaud C, Atkins HL, Bazin JP, DiPaola M and DiPaola R (1987). Factor analysis for in-vivo evaluation of radiopharmaceuticals. J.Nucl.Med. 28:A 519.

    Google Scholar 

  • Rescigno A, Thakur AK, Brill AB and Mariani G (1990). Tracer kinetics: a proposal for unified symbols and nomenclature. Phys.Med.Biol. 35:449–465.

    Google Scholar 

  • Šámal M, Kárný M, Sůrová H, Pěnička P, Maříková E and Dienstbier Z (1989). On the existence of an unambiguous solution in factor analysis of dynamic studies. Phys.Med.Biol. 34:223–228.

    Google Scholar 

  • Šámal M, Kárný M, Sůrová H and Zajfček J (1990). Feature extraction from NMR images using factor analysis. In: Tissue Characterisation in MR imaging. Higer HP and Bielke G (eds), Springer-Verlag Berlin, Heidelberg, pp.161–164.

    Google Scholar 

  • Šámal M, Sůrová H, Kárný M, Maříková E, Pěnička P and Dienstbier Z (1988). The reality and meaning of physiological factors. In: Information Processing in Medical Imaging. deGraaf CN and Viergever MA (eds). Plenum Press, New York, pp. 499–519.

    Google Scholar 

  • Todd-Pokropek A and Gagnon D (1989). Image reconstruction using energy information: convergence criteria. Nucl.Med.Commun. 10:248.

    Google Scholar 

  • Todd-Pokropek A and Gagnon D (1990). The use of principal component analysis methods for manipulating images acquired over a wide energy window. In: Radioactive Isotopes in Clinical Medicine and Research. Hoefer R and Bergmann H (eds), Schattauer Verlag, Stuttgart, in press.

    Google Scholar 

  • VanDaele M, Joosten J, Devos P, Vandecruys A, Willems JL and DeRoo M (1991). A new vertex-finding algorithm for the oblique rotation step in factor analysis. Phys.Med.Biol. 36:77–85.

    Google Scholar 

  • Wellner U (1986). Kinetic models of metabolism. Nucl.-Med. 25:138–141.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Charles University, Salmovská 3, 120 00, Prague 2, Czechoslovakia

    M Šámal & D Zahálka

  2. Czechoslovak Academy of Sciences, Salmovská 3, 120 00, Prague 2, Czechoslovakia

    M. Kárný

Authors
  1. M Šámal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Kárný
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D Zahálka
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Alan C. F. Colchester David J. Hawkes

Rights and permissions

Reprints and permissions

Copyright information

© 1991 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Šámal, M., Kárný, M., Zahálka, D. (1991). Confirmatory aspects in factor analysis of image sequences. In: Colchester, A.C.F., Hawkes, D.J. (eds) Information Processing in Medical Imaging. IPMI 1991. Lecture Notes in Computer Science, vol 511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033768

Download citation

  • DOI: https://doi.org/10.1007/BFb0033768

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54246-9

  • Online ISBN: 978-3-540-47521-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation