Hierarchical algorithm for limited-angle reconstruction

Jerry L. Prince, Alan S. Willsky

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


The authors describe and demonstrate a hierarchical reconstruction algorithm for use in noisy and limited-angle or sparse-angle tomography. The algorithm estimates the object's mass, center of mass, and convex hull from the available projections, and uses this information, along with fundamental mathematical constraints, to estimate a full set of smoothed projections. The mass and center of mass are estimated using a maximum-likelihood (ML) estimator derived from the principles of consistency of the Radon transform. The convex hull estimate is produced by first estimating the positions of support lines of the object from each available projection and then estimating the overall convex hull using ML or maximum a posteriori (MAP) techniques. The position of two support lines from a single projection is estimated using either a generalized-likelihood-ratio technique for estimating jumps in linear systems or a support-width penalty method that uses Akaike's model-order estimation technique.

Original languageEnglish (US)
Pages (from-to)1468-1471
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - Dec 1 1989
Externally publishedYes
Event1989 International Conference on Acoustics, Speech, and Signal Processing - Glasgow, Scotland
Duration: May 23 1989May 26 1989

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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