Skip to main content

Biomedical Data Mining, Spatial

  • Reference work entry
Encyclopedia of GIS

Synonyms

Polynomials, orthogonal; Polynomials; Zernike; Zernike polynomials; Wavelets

Definition

The use of biomedical data in object classification presents several challenges that are well-suited to knowledge discovery and spatial modeling methods. In general, this problem consists of extracting useful patterns of information from large quantities of data with attributes that often have complex interactions. Biomedical data are inherently multidimensional and therefore difficult to summarize in simple terms without losing potentially useful information. A natural conflict exists between the need to simplify the data to make it more interpretable and the associated risk of sacrificing information relevant to decision support.

Transforming spatial features in biomedical data quantifies, and thereby exposes, underlying patterns for use as attributes in data mining exercises [14]. To be useful, a data transformation must faithfully represent the original spatial features. Orthogonal...

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

Access this chapter

Institutional subscriptions

Recommended Reading

  1. Born, M.,Wolf E.: Principles of optics: electromagnetic theory of propagation, interference and diffraction of light, 6th edn. Pergamon Press, Oxford, New York (1980)

    Google Scholar 

  2. Daubechies, I.: Ten Lectures on Wavelets. Soc. Indust. Appl. Math., Philadelphia (1992)

    Google Scholar 

  3. Hoekman, D.H., Varekamp, C.: Observation of tropical rain forest trees by airborne high-resolution interferometric radar. Geosci. Remote Sensing, IEEE Trans on, 39(3),584–594 (2001)

    Article  Google Scholar 

  4. Iskander, D.R., Collins, M.J., Davis, B.: Optimal modeling of corneal surfaces with zernike polynomials. IEEE Trans Biomed. Eng. 48(1),87–95 (2001)

    Article  Google Scholar 

  5. Iskander, D.R., Collins, M.J., Davis, B., Franklin, R.: Corneal surface characterization: How many zernike terms should be used? (ARVO) abstract. Invest. Ophthalmol. Vis. Sci. 42(4),896 (2001)

    Google Scholar 

  6. Kiely, P.M., Smith, G., Carney, L.G.: The mean shape of the human cornea. J. Modern Opt. 29(8),1027–1040 (1982)

    Google Scholar 

  7. Laine, A.F.: Wavelets in temporal and spatial processing of biomedical images. Annu. Rev. Biomed. Eng. 02, 511–550 (2000)

    Article  Google Scholar 

  8. Mallat, S.: A Wavelet Tour of Signal Processing, 2nd edn. Academic, New York (1999)

    MATH  Google Scholar 

  9. Mandell, R.B.: A guide to videokeratography. Int. Contact Lens Clin. 23(6),205–28 (1996)

    Article  Google Scholar 

  10. Marsolo, K., Parthasarathy, S., Twa, M.D., Bullimore, M.A.: A model-based approach to visualizing classification decisions for patient diagnosis. In: Proceedings of the Conference on Artificial Intelligence in Medicine (AIME), Aberdeen, 23–27 July 2005

    Google Scholar 

  11. Marsolo, K., Twa, M., Bullimore, M.A., Parthasarathy, S.: Spatial modeling and classification of corneal shape. IEEE Transactions on Information Technology in Biomedicine (2006)

    Google Scholar 

  12. Platzman, L., Bartholdi, J.: Spacefilling curves and the planar travelling salesman problem. J. Assoc. Comput. Mach. 46, 719–737 (1989)

    Article  MathSciNet  Google Scholar 

  13. Schwiegerling, J., Greinvenkamp, J.E., Miller, J.M.: Representation of videokeratoscopic height data with zernike polynomials. J. Opt. Soc. Am. A 12(10),2105–2113 (1995)

    Article  Google Scholar 

  14. Teh, C.-H., Chin, R.T.: On image analysis by the methods of moments. IEEE Trans. on Pattern. Anal. Mach. Intell. 10(4):496–513 (1988)

    Article  MATH  Google Scholar 

  15. Thibos, L.N., Applegate, R.A., Schwiegerling, J.T., Webb, R.: Standards for reporting the optical aberrations of eyes. In: TOPS, Santa Fe, NM (1999) OSA

    Google Scholar 

  16. Twa, M.D., Parthasarathy, S., Raasch, T.W., Bullimore, M.A.: Automated classification of keratoconus: A case study in analyzing clinical data. In: SIAM Int'l Conference on Data Mining, San Francisco, CA, 1–3 May 2003

    Google Scholar 

  17. Zernike, F.: Beugungstheorie des Schneidenverfahrens und seiner verbesserten Form, der Phasenkontrastmethode. Physica, 1, 689–704 (1934)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag

About this entry

Cite this entry

Marsolo, K., Twa, M., Bullimore, M., Parthasarathy, S. (2008). Biomedical Data Mining, Spatial. In: Shekhar, S., Xiong, H. (eds) Encyclopedia of GIS. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35973-1_102

Download citation

Publish with us

Policies and ethics