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Linear Models for Visual Data Mining in Medical Images

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Foundations of Computational, IntelligenceVolume 6

Part of the book series: Studies in Computational Intelligence ((SCI,volume 206))

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Summary

Modern non-invasive imaging modalities such as Computer-Aided Tomography and Magnetic Resonance Imaging brought a new perspective to anatomic characterization, as they allowed detailed observation of in vivo structures. The amount of provided data became, however, progressively overwhelming. Data may be useless if we are not able to extract relevant information from them and to discard undesirable artifacts and redundancy. Ultimately, we aim to discover new knowledge from collected data, and to be able to statistically represent this knowledge in the form of prior distributions that may be used to validate new hypotheses, in addition to clinical and demographic information. In this chapter we propose an analysis of available methods for data mining in very high-dimensional sets of data obtained from medical imaging modalities. When applied to imaging studies, data reduction methods may be able to minimize data redundancy and reveal subtle or hidden patterns. Our analysis is concentrated on linear transformation models based on unsupervised learning that explores the relationships among morphologic variables, in order to find clusters with strong correlation. This clustering can potentially identify regions that have anatomic significance and thus lend insight to knowledge discovery and hypothesis testing. We illustrate this chapter with successful case studies related to the segmentation of neuroanatomic structures and the characterization of pathologies.

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References

  1. Bajcsy, R., Broit, C.: Matching of deformed images. In: VI International Conference on Pattern Recognition, pp. 351–353 (1982)

    Google Scholar 

  2. Bajcsy, R., Kovacic, S.: Multiresolution elastic matching. Computer Vision, Graphics and Image Processing 46, 1–21 (1989)

    Article  Google Scholar 

  3. Christensen, G., Rabbitt, R., Miller, M.: Deformable templates using large deformation kinematics. IEEE Transactions on Image Processing 5(10), 1435–1447 (1996)

    Article  Google Scholar 

  4. Cootes, T., Hill, A., Taylor, C.J., Haslam, J.: Use of active shape models for locating structures in medical images. Image and Vision Computing 12(6), 355–365 (1994)

    Article  Google Scholar 

  5. D’Agostino, R.B.: Small sample probability points for the D test of normality. Biometrika 59, 219–221 (1972)

    Article  Google Scholar 

  6. Davatzikos, C., Prince, J.: An active contour model for mapping the cortex. IEEE Transactions on Medical Imaging 14, 65–80 (1995)

    Article  Google Scholar 

  7. Davatzikos, C., Vaillant, M., Resnick, S., Prince, J., Letovsky, S., Bryan, R.: A computerized approach for morphological analysis of the corpus callosum. Journal of Computer Assisted Tomography 20(1), 88–97 (1996)

    Article  Google Scholar 

  8. Denenberg, V.H., Kertesz, A., Cowell, P.E.: A factor analysis of the human’s corpus callosum. Brain Research 548, 126–132 (1991)

    Article  Google Scholar 

  9. Duta, N., Sonka, M.: Segmentation and interpretation of mr brain images: An improved active shape model. IEEE Transactions on Medical Imaging 17(6), 1049–1062 (1995)

    Article  Google Scholar 

  10. Evans, A., Collins, D., Brown, E., Kelly, R., Petters, T.: A 3D statistical neuroanatomical models for 305 MRI volumes. In: IEEE Nuclear Science Symposium/Medical Imaging Conference, pp. 1813–1817 (1993)

    Google Scholar 

  11. Frackowiak, R., Friston, K., Frith, C., Dolan, R., Mazziotta, J.: Human Brain Function. Academic Press, San Diego (1997)

    Google Scholar 

  12. Gee, J.C.: On matching brain volumes. Pattern Recognition 32, 99–111 (1999)

    Article  Google Scholar 

  13. Grossman, M., McMillan, C., Moore, P., Ding, L., Glosser, G., Work, M., Gee, J.: What’s in a name: voxel-based morphometric analyses of MRI and naming difficulty in alzheimer’s disease, frontotemporal dementia and corticobasal degeneration. Brain 127, 1–22 (2004)

    Article  Google Scholar 

  14. Harman, H.: Modern Factor Analysis. University of Chicago Press, Chicago (1976)

    Google Scholar 

  15. Hotelling, H.: The relations of the new multivariate statistical methods to factor analysis. British Journal of Statistical Psychology 10, 69–79 (1957)

    Google Scholar 

  16. Jöreskog, K.: Some contributions to maximum likelihood factor analysis. Psychometrika 32, 443–482 (1967)

    Article  MATH  MathSciNet  Google Scholar 

  17. Jöreskog, K., Goldberger, A.S.: Factor analysis by generalized least squares. Psychometrika 37, 243–260 (1968)

    Article  Google Scholar 

  18. Jöreskog, K., Lawley, D.N.: New metrods in maximum likelihood factor analysis. British Journal of Mathematical and Statistical Psychology 21, 85–96 (1968)

    MathSciNet  Google Scholar 

  19. lawley, D.N.: The estimation of factor loadings by the method of maximum likelihood. Proceedings of the Royal Society of Edinburgh 60, 64–82 (1940)

    MATH  MathSciNet  Google Scholar 

  20. Le Briquer, L., Gee, J.C.: Design of a Statistical Model of Brain Shape. In: Duncan, J.S., Gindi, G. (eds.) IPMI 1997. LNCS, vol. 1230, pp. 477–482. Springer, Heidelberg (1997)

    Google Scholar 

  21. Lin, C.C., Muldholkar, G.S.: A simple test for normality against asymetric alternatives. Biometrika 67, 455–461 (1980)

    Article  MATH  MathSciNet  Google Scholar 

  22. Machado, A., Gee, J.C.: Atlas warping for brain morphometry. In: Hanson, K.M. (ed.) Proceedings of the SPIE Medical Imaging 1998: Image Processing, pp. 642–651. Bellingham, San Diego (1998)

    Google Scholar 

  23. Machado, A., Simon, T., Nguyen, V., McDonald-McGinn, D., Zackai, E., Gee, J.: Corpus callosum morphology and ventricular size in chromosome 22q11.2 deletion syndrome. Brain Research 1131, 197–210 (2007)

    Article  Google Scholar 

  24. Marcus, L.: Traditional Morphometrics. In: Rohlf, F.J., Bookstein, F.L. (eds.) Proceedings of the Michigan Morphometrics Workshop, Ann Arbor, The University of Michigan Museum of Zoology, pp. 77–122 (1990)

    Google Scholar 

  25. Martin, J., Pentland, A., Sclaroff, S., Kikinis, R.: Characterization of neuropathological shape deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(2), 97–112 (1998)

    Article  Google Scholar 

  26. Reyment, R., Jöreskog, K.: Applied Factor Analysis in the Natural Sciences. Cambridge University Press, Cambridge (1996)

    MATH  Google Scholar 

  27. Roweis, S., Ghahramani, Z.: A unifying review of linear gaussian models. Neural Computation 11(2), 305–345 (1999)

    Article  Google Scholar 

  28. Sclaroff, S., Pentland, A.: Modal matching for correspondence and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(6), 545–561 (1995)

    Article  Google Scholar 

  29. Simon, T.J., Bearden, C.E., McDonald-McGinn, D.M., Zackai: Visuospatial and numerical cognitive deficits in children with chromosome 22q11.2 deletion syndrome. Cortex 41, 145–155 (2005)

    Article  Google Scholar 

  30. Spearman, C.: General intelligence, objectively determined and measured. American Journal of Psychology 15, 201–293 (1904)

    Article  Google Scholar 

  31. Stievenart, J.L., Iba-Zizen, M.T., Tourbah, A., Lopez, A., Thibierge, M., Abanou, A., Canabis, A.: Minimal surface: A useful paradigm to describe the deeper part of the corpus callosum? Brain Research Bulletin 44(2), 117–124 (1997)

    Article  Google Scholar 

  32. Thompson, P.M., Moussai, J., Zohoori, S., Goldkorn, A., Khan, A., Mega, M.S., Small, G., Cummings, J., Toga, A.W.: Cortical variability and assymetry in normal aging and Alzheimer’s disease. Cerebral Cortex 8, 492–509 (1998)

    Article  Google Scholar 

  33. Thomson, G.H.: Hotelling’s method modified to give Spearman’s g. Journal of Educational Psychology 25, 366–374 (1934)

    Article  Google Scholar 

  34. Thurstone, L.L.: Multiple factor analysis. Psychological Reviews 38, 406–427 (1931)

    Article  Google Scholar 

  35. Witelson, S.F.: Hand and sex differences in the isthmus and genu of the human corpus callosum: a postmortem morphological study. Brain 112, 799–835 (1989)

    Article  Google Scholar 

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Authors and Affiliations

  1. Graduate Program on Electrical Engineering, Pontifical Catholic University of Minas Gerais, Av. Dom Jose Gaspar, 500, Belo Horizonte, MG, Brazil, 30535-610

    Alexei Manso Corrêa Machado

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  1. Alexei Manso Corrêa Machado
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Editors and Affiliations

  1. Machine Intelligence Research Labs, (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, P.O. Box 2259, 98071-2259, Washington, USA

    Ajith Abraham

  2. College of Business Administration, Quantitative and Information System Department, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

  3. Department of Computer Science, University of São Paulo, Caixa Postal 668, 13560-970, Sao Carlos, SP, Brazil

    André Ponce de Leon F. de Carvalho

  4. Dept. Computer Science, Technical University Ostrava, Tr. 17. Listopadu 15, 708 33, Ostrava, Czech Republic

    Václav Snášel

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Machado, A.M.C. (2009). Linear Models for Visual Data Mining in Medical Images. In: Abraham, A., Hassanien, AE., de Leon F. de Carvalho, A.P., Snášel, V. (eds) Foundations of Computational, IntelligenceVolume 6. Studies in Computational Intelligence, vol 206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01091-0_14

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  • DOI: https://doi.org/10.1007/978-3-642-01091-0_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01090-3

  • Online ISBN: 978-3-642-01091-0

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