HAMMER: Hierarchical attribute matching mechanism for elastic registration

Dinggang Shen, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

949 Citations (Scopus)


A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate very high accuracy in superposition of images from different subjects. There are two major novelties in the proposed algorithm. First, it uses an attribute vector, i.e., a set of geometric moment invariants (GMIs) that are defined on each voxel in an image and are calculated from the tissue maps, to reflect the underlying anatomy at different scales. The attribute vector, if rich enough, can distinguish between different parts of an image, which helps establish anatomical correspondences in the deformation procedure; it also helps reduce local minima, by reducing ambiguity in potential matches. This is a fundamental deviation of our method, referred to as the hierarchical attribute matching mechanism for elastic registration (HAMMER), from other volumetric deformation methods, which are typically based on maximizing image similarity. Second, in order to avoid being trapped by local minima, i.e., suboptimal poor matches, HAMMER uses a successive approximation of the energy function being optimized by lower dimensional smooth energy functions, which are constructed to have significantly fewer local minima. This is achieved by hierarchically selecting the driving features that have distinct attribute vectors, thus, drastically reducing ambiguity in finding correspondence. A number of experiments demonstrate that the proposed algorithm results in accurate superposition of image data from individuals with significant anatomical differences.

Original languageEnglish
Pages (from-to)1421-1439
Number of pages19
JournalIEEE Transactions on Medical Imaging
Issue number11
Publication statusPublished - 2002 Nov

Bibliographical note

Funding Information:
Manuscript received July 26, 2001; revised May 9, 2002. This work was supported in part by the National Institute of Health (NIH) under Grant NIH-R01AG14971 and under Contract AG-93-07. Images were acquired as part of the neuroimaging study of the Baltimore Longitudinal Study of Aging [49]. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was M. Sonka. Asterisk indicates corresponding author. *D. Shen was with the Center for Biomedical Image Computing, Department of Radiology, The Johns Hopkins University School of Medicine, 601 N. Caroline Street, Baltimore, MD 21287 USA. He is now with the Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104 USA (e-mail: dgshen@rad.upenn.edu).

Copyright 2008 Elsevier B.V., All rights reserved.


  • Attribute vectors
  • Average brain
  • Deformable registration
  • Geometric moment invariants
  • Hierarchical deformation mechanism
  • Multigrid formulation
  • Statistical atlases

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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