Direct Image Reconstruction-Segmentation, as Motivated by Electron Microscopy

  • H.Y. Liao
  • G.T. Herman
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)


Our aim is to produce a tessellation of space into small voxels and, based on only a few tomographic projections of an object, assign to each voxel a label that indicates one of the components of interest constituting the object. Examples of application are in the areas of electron microscopy, industrial nondestructive testing, cardiac imaging, etc. Current approaches first reconstruct the density distribution from the projections and then segment (label) this distribution. We instead postulate a low-level prior knowledge regarding the underlying distribution of label images and then directly estimate the label image based on the prior and the projections. In particular, we show, in the binary (i.e., two labels) case, that the marginal posterior mode estimator outperforms the widely known maximum a posteriori probability estimator. As measured by label misclassification in the reconstructions, our direct labeling method was experimentally proved (in the binary case) to be superior to current approaches. However, when a detectability measure was used, its relative performance was less satisfactory. We discuss possible improvements.


Markov Chain Monte Carlo Gibbs Distribution Label Image Algebraic Reconstruction Technique Annealing Schedule 
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

© Birkhäuser Boston 2007

Authors and Affiliations

  • H.Y. Liao
    • 1
  • G.T. Herman
    • 2
  1. 1.Institute for Mathematics and Its Applications, University of MinnesotaMinnesotaUSA
  2. 2.Dept. of Computer ScienceThe Graduate Center, City Univ. of New YorkNew YorkUSA

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