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Valdes-Amaro, Daniel; Bhalerao, Abhir (2009)
Publisher: Springer Berlin Heidelberg
Languages: English
Types: Part of book or chapter of book
Subjects: QA, QH301, TA
Understanding the biological variability of anatomical objects, is essential for statistical shape analysis and to distinguish between healthy and pathological structures. Statistical Shape Modelling (SSM) can be used to analyse the shapes of sub-structures aiming to describe their variation across individual objects and between groups of them [1]. However, when the shapes exhibit; self-similarity or are intrinsically fractal, such as often encountered in biomedical problems, global shape models result in highly non-linear shape spaces and it can be difficult; to determine a compact set; of modes of variation. In this work, we present, a method for local shape, modelling and analysis that uses Diffusion Maps [2] for non-linear, spectral clustering to build a set of linear shape spaces for such analysis. The method uses a curvature scale-space (CSS) description of shape to partition them into sets of self-similar parts and these are then linearly mixed to more compactly model the global shape.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Training models of shape from sets of examples. In: Proc. British Machine Vision Conference, Springer (1992) 266-275
    • 2. Lafon, S., Lee, A.B.: Diuffsion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization. IEEE Transactions Pattern Analysis and Machine Intelligence 28(9) (September 2006) 1393-1403
    • 3. Grenander, U., Miller, M.I.: Computational anatomy: An emerging discipline. Quarterly of Applied Mathematics 56 (1998) 617-694
    • 4. Ashburner, J., Csernansky, J.G., Davatzikos, C., Fox, N.C., Frisoni, G.B., Thompson, P.M.: Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet Neurology 2 (2003) 79-88
    • 5. Shen, D., Herskovits, E.H., Davatzikos, C.: An adaptive-focus statistical shape model for segmentation and shape modeling of 3-d brain structures. IEEE Transactions on Medical Imaging 202 (2001) 257-270
    • 6. Fischl, B., Liu, A., Dale, A.M.: Automated manifold surgery: Constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Transactions on Medical Imaging 2 (2001) 70-80
    • 7. Styner, M., Gerig, G., Lieberman, J., Jones, D., Weinberger, D.: Statistical shape analysis of neuroanatomical structures based on medial models. Medical Image Analysis 7 (2003) 207-220
    • 8. Xue, H., Srinivasan, L., Jiang, S., Rutherford, M., Edwards, A.D., Rueckert, D., Hajnal, J.V.: Automatic segmentation and reconstruction of the cortex from neonatal mri. NeuroImage 38 (2007) 461-477
    • 9. Valdes-Amaro, D., Bhalerao, A.: Local Shape Modelling for Brain Morphometry using Curvature Scale Space. In McKenna, S., Hoey, J., eds.: Proceedings of the 12th Annual Conference on Medical Image Understanding and Analysis 2008, British Machine Vision Association (July 2008) 64-68
    • 10. Rajpoot, N.M., Arif, M., Bhalerao, A.H.: Unsupervised learning of shape manifolds. In: British Machine Vision Conference. (2007) 312-321
    • 11. Mokhtarian, F., Bober, M.: Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization. Kluwer Academic Publishers, Norwell, MA, USA (2003)
    • 12. Aubert-Broche, B., Griffin, M., Pike, G.B., Evans, A.C., Collins, D.L.: Twenty new digital brain phantoms for creation of validation image data bases. IEEE Transactions on Medical Imaging 25 (2006) 1410-14163
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