An anatomical equivalence class based joint transformation-residual descriptor for morphological analysis

Sajjad Baloch, Ragini Verma, Christos Davatzikos

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

Abstract

Existing approaches to computational anatomy assume that a perfectly conforming diffeomorphism applied to an anatomy of interest captures its morphological characteristics relative to a template. However, biological variability renders this task extremely difficult, if possible at all in many cases. Consequently, the information not reflected by the transformation, is lost permanently from subsequent analysis. We establish that this residual information is highly significant for characterizing subtle morphological variations and is complementary to the transformation. The amount of residual, in turn, depends on transformation parameters, such as its degree of regularization as well as on the template. We, therefore, present a methodology that measures morphological characteristics via a lossless morphological descriptor, based on both the residual and the transformation. Since there are infinitely many [transformation, residual] pairs that reconstruct a given anatomy, which collectively form a nonlinear manifold embedded in a high-dimensional space, we treat them as members of an Anatomical Equivalence Class (AEC). A unique and optimal representation, according to a certain criterion, of each individual anatomy is then selected from the corresponding AEC, by solving an optimization problem. This process effectively determines the optimal template and transformation parameters for each individual anatomy, and removes respective confounding variation in the data. Based on statistical tests on synthetic 2D images and real 3D brain scans with simulated atrophy, we show that this approach provides significant improvement over descriptors based solely on a transformation, in addition to being nearly independent of the choice of the template.

Original languageEnglish (US)
Title of host publicationInformation Processing in Medical lmaging - 20th International Conference, IPMI 2007, Proceedings
PublisherSpringer Verlag
Pages594-606
Number of pages13
ISBN (Print)3540732721, 9783540732723
DOIs
StatePublished - 2007
Event20th International Conference on Information Processing in Medical lmaging, IPMI 2007 - Kerkrade, Netherlands
Duration: Jul 2 2007Jul 6 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4584 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Information Processing in Medical lmaging, IPMI 2007
CountryNetherlands
CityKerkrade
Period7/2/077/6/07

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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