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Abstract

In this paper we propose a new method for shape analysis based on the depth-ordering of shapes. We use this depth-ordering to non-parametrically define depth with respect to a normal control population. This allows us to quantify differences with respect to “normality”. We combine this approach with a permutation test allowing it to test for localized shape differences. The method is evaluated on a synthetically generated striatum dataset as well as on a real caudate dataset.

Keywords

Personality Disorder Shape Analysis Test Shape Band Depth Schizotypal Personality Disorder 
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.

References

  1. 1.
    Davies, R., Twining, C., Taylor, C.: Statistical Models of Shape: Optimisation and Evaluation. Springer, London (2008)Google Scholar
  2. 2.
    Gao, Y., Bouix, S.: Synthesis of realistic subcortical anatomy with known surface deformations. In: Levine, J.A., Paulsen, R.R., Zhang, Y. (eds.) MeshMed 2012. LNCS, vol. 7599, pp. 80–88. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Hong, Y., Davis, B., Marron, J.S., Kwitt, R., Niethammer, M.: Weighted functional boxplot with application to statistical atlas construction. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 584–591. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Levitt, J.J., Styner, M., Niethammer, M., Bouix, S., Koo, M.S., Voglmaier, M.M., Dickey, C.C., Niznikiewicz, M.A., Kikinis, R., McCarley, R.W., Shenton, M.E.: Shape abnormalities of caudate nucleus in schizotypal personality disorder. Schizophrenia Research 110(1-3), 127–139 (2009)CrossRefGoogle Scholar
  5. 5.
    Loncaric, S.: A survey of shape analysis techniques. Pattern Recognition 31(8), 983–1001 (1998)CrossRefGoogle Scholar
  6. 6.
    López-Pintado, S., Romo, J.: On the concept of depth for functional data. Journal of the American Statistical Association 104, 718–734 (2009)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Miller, M.I.: Computational anatomy: Shape, growth, and atrophy comparison via diffeomorphisms. NeuroImage 23, S19–S33 (2004)Google Scholar
  8. 8.
    Styner, M., Lieberman, J.A., Pantazis, D., Gerig, G.: Boundary and medial shape analysis of the hippocampus in schizophrenia. Medical Image Analysis 8(3), 197–203 (2004)CrossRefGoogle Scholar
  9. 9.
    Sun, Y., Genton, M.: Functional boxplots. Journal of Computational and Graphical Statistics 20, 316–334 (2011)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Sun, Y., Genton, M.G., Nychka, D.W.: Exact fast computation of band depth for large functional datasets: How quickly can one million curves be ranked? Stat. 1, 68–74 (2012)CrossRefGoogle Scholar
  11. 11.
    Whitaker, R.T., Mirzargar, M., Kirby, R.M.: Contour boxplots: A method for characterizing uncertainty in feature sets from simulation ensembles. IEEE Transactions on Visualization and Computer Graphics 19(12), 2713–2722 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yi Hong
    • 1
  • Yi Gao
    • 3
  • Marc Niethammer
    • 1
    • 2
  • Sylvain Bouix
    • 4
  1. 1.University of North Carolina (UNC) at Chapel HillUSA
  2. 2.Biomedical Research Imaging CenterUNC-Chapel HillUSA
  3. 3.University of Alabama at BirminghamBirminghamUSA
  4. 4.Psychiatry Neuroimaging Laboratory, Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA

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