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Patient-Specific Manifold Embedding of Multispectral Images Using Kernel Combinations

  • Veronika A. M. Zimmer
  • Roger Fonolla
  • Karim Lekadir
  • Gemma Piella
  • Corné Hoogendoorn
  • Alejandro F. Frangi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)

Abstract

This paper presents a framework that optimizes kernel-based manifold embedding for the characterization of multispectral image data. The hypothesis is that data manifolds corresponding to high-dimensional images can have varying characteristics and types of nonlinearity. As a result, kernel functions must be selected from a wide range of transformations and tuned on an image- and patient-basis. To this end, we introduce a new measure to assess the quality of the kernel transformations that takes into account both local and global relationships in nonlinear manifolds. Furthermore, the calculated measures for each kernel are used to combine the different kernel transformations further highlight the tissue constituents in all regions of the image. Validation with phantom and real multispectral image data shows improvement in the visualization and characterization of the tissue constituents.

Keywords

Multispectral Image Class Separation Kernel Principal Component Analysis Tissue Constituent Kernel Transformation 
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.

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References

  1. 1.
    Chen, L., Buja, A.: Local multidimensional scaling for nonlinear dimension reduction, graph drawing and proximity analysis. Journal of the American Statistical Association 104(485), 209–219 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Hofmann, T., Schölkopf, B., Smola, A.J.: Kernel Methods in Machine Learning. The Annals of Statistics 36(3), 1171–1220 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Hordley, S., Finalyson, G., Morovic, P.: A Multispectral Image Database and its Application to Image Rendering Across Illumination. In: Proc. 3rd Int. Conf. on Image and Graphics (ICIG), Hong-Kong, China, pp. 394–397 (2004), http://www2.cmp.uea.ac.uk/Research/compvis/MultiSpectralDB.htm
  4. 4.
    Kouropteva, O., Okun, O., Pietkäinen, M.: Incremental locally linear embedding. Pattern Recognition 38(10), 1764–1767 (2005)CrossRefzbMATHGoogle Scholar
  5. 5.
    Lee, J.A., Verleysen, M.: Quality assessment of dimensionality reduction: Rank-based criteria. Neurocomputing 72(7-9), 1431–1443 (2009)CrossRefGoogle Scholar
  6. 6.
    Maaten, L.V.D., Postma, E., Herik, H.V.D.: Dimensionality reduction: a comparative review. Tech. Rep. TiCC-TR 2009-05, Tilburg University, Tilburg, The Netherlands (2009)Google Scholar
  7. 7.
    Menze, B., Jakab, A., Bauer, S., Reyes, M., Prastawa, M., van Leemput, K.: Challenge on Multimodal Brain Tumor Segmentation (MICCAI-BRATS). In: Proc. MICCAI 2012 (2012), http://www2.imm.dtu.dk/projects/BRATS2012
  8. 8.
    Tanabe, H., Ho, T.B., Nguyen, C.H., Kawasaki, S.: Simple but effective methods for combining kernels in computational biology. In: IEEE Int. Conf. on Research, Innovation and Vision for the Future (RIVF), pp. 71–78 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Veronika A. M. Zimmer
    • 1
  • Roger Fonolla
    • 1
  • Karim Lekadir
    • 1
  • Gemma Piella
    • 1
  • Corné Hoogendoorn
    • 1
  • Alejandro F. Frangi
    • 1
    • 2
  1. 1.CISTIB, Information and Communication Technologies DepartmentUniversitat Pompeu FabraBarcelonaSpain
  2. 2.CISTIB, Department of Mechanical EngineeringThe University of SheffieldUnited Kingdom

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