A robust point-matching algorithm for autoradiograph alignment

Anand Rangarajan, Haili Chui, Eric Mjolsness, Suguna Pappu, Lila Davachi, Patricia Goldman-Rakic, James Duncan

Research output: Contribution to journalArticlepeer-review


We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the one-to-one correspondences (or homologies) between point features extracted from the images and rejecting non-homologies as outliers. In this way, we attempt to account for the local, natural and artifactual differences between the autoradiograph slices. We have used the resulting automated algorithm on a set of left prefrontal cortex autoradiograph slices, specifically demonstrated its ability to perform point outlier rejection, validated its robustness property using synthetically generated spatial mappings and provided an anecdotal visual comparison with the well-known iterated closest-point (ICP) algorithm. Visualization of a stack of aligned left prefrontal cortex autoradiograph slices is also provided.

Original languageEnglish (US)
Pages (from-to)379-398
Number of pages20
JournalMedical Image Analysis
Issue number4
StatePublished - 1997


  • 3-D reconstruction
  • Alignment
  • Correspondence
  • Deterministic annealing
  • Linear assignment problem
  • Outlier rejection
  • Permutation matrix
  • Point matching
  • Primate autoradiographs
  • Registration
  • Robustness
  • Similarity transform
  • Softassign
  • Spatial mapping

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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