Sparse Projections of Medical Images onto Manifolds

  • George H. Chen
  • Christian Wachinger
  • Polina Golland
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


Manifold learning has been successfully applied to a variety of medical imaging problems. Its use in real-time applications requires fast projection onto the low-dimensional space. To this end, out-of-sample extensions are applied by constructing an interpolation function that maps from the input space to the low-dimensional manifold. Commonly used approaches such as the Nyström extension and kernel ridge regression require using all training points. We propose an interpolation function that only depends on a small subset of the input training data. Consequently, in the testing phase each new point only needs to be compared against a small number of input training data in order to project the point onto the low-dimensional space. We interpret our method as an out-of-sample extension that approximates kernel ridge regression. Our method involves solving a simple convex optimization problem and has the attractive property of guaranteeing an upper bound on the approximation error, which is crucial for medical applications. Tuning this error bound controls the sparsity of the resulting interpolation function. We illustrate our method in two clinical applications that require fast mapping of input images onto a low-dimensional space.


Support Vector Support Vector Regression Error Tolerance Training Point Respiratory Gating 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • George H. Chen
    • 1
  • Christian Wachinger
    • 1
  • Polina Golland
    • 1
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA

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