DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting

Yangming Ou, Christos Davatzikos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations


A general-purpose deformable registration algorithm referred to as "DRAMMS" is presented in this paper. DRAMMS adds to the literature of registration methods that bridge between the traditional voxel-wise methods and landmark/feature-based methods. In particular, DRAMMS extracts Gabor attributes at each voxel and selects the optimal components, so that they form a highly distinctive morphological signature reflecting the anatomical context around each voxel in a multi-scale and multi-resolution fashion. Compared with intensity or mutual-information based methods, the high-dimensional optimal Gabor attributes render different anatomical regions relatively distinctively identifiable and therefore help establish more accurate and reliable correspondence. Moreover, the optimal Gabor attribute vector is constructed in a way that generalizes well, i.e., it can be applied to different registration tasks, regardless of the image contents under registration. A second characteristic of DRAMMS is that it is based on a cost function that weights different voxel pairs according to a metric referred to as "mutual- saliency", which reflects the uniqueness (reliability) of anatomical correspondences implied by the tentative transformation. As a result, image voxels do not contribute equally to the optimization process, as in most voxel-wise methods, or in a binary selection fashion, as in most landmark/feature-based methods. Instead, they contribute according to a continuously-valued mutual-saliency map, which is dynamically updated during the algorithm's evolution. The general applicability and accuracy of DRAMMS are demonstrated by experiments in simulated images, inter-subject images, single-/multi-modality images, and longitudinal images, from human and mouse brains, breast, heart, and prostate.

Original languageEnglish (US)
Title of host publicationInformation Processing in Medical Imaging - 21st International Conference, IPMI 2009, Proceedings
Number of pages13
StatePublished - 2009
Externally publishedYes
Event21st International Conference on Information Processing in Medical Imaging, IPMI 2009 - Williamsburg, VA, United States
Duration: Jul 5 2009Jul 10 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5636 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other21st International Conference on Information Processing in Medical Imaging, IPMI 2009
Country/TerritoryUnited States
CityWilliamsburg, VA

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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