Co-registration of white matter tractographies by adaptive-mean-shift and gaussian mixture modeling

Orly Zvitia, Arnaldo Mayer, Ran Shadmi, Shmuel Miron, Hayit K. Greenspan

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

In this paper, we present a robust approach to the registration of white matter tractographies extracted from diffusion tensor-magnetic resonance imaging scans. The fibers are projected into a high dimensional feature space based on the sequence of their 3-D coordinates. Adaptive mean-shift clustering is applied to extract a compact set of representative fiber-modes (FM). Each FM is assigned to a multivariate Gaussian distribution according to its population thereby leading to a Gaussian mixture model (GMM) representation for the entire set of fibers. The registration between two fiber sets is treated as the alignment of two GMMs and is performed by maximizing their correlation ratio. A nine-parameters affine transform is recovered and eventually refined to a twelve-parameters affine transform using an innovative mean-shift based registration refinement scheme presented in this paper. The validation of the algorithm on synthetic intrasubject data demonstrates its robustness to interrupted and deviating fiber artifacts as well as outliers. Using real intrasubject data, a comparison is conducted to other intensity based and fiber-based registration algorithms, demonstrating competitive results. An option for tracking-in-time, on specific white matter fiber tracts, is also demonstrated on the real data.

Original languageEnglish
Article number5223611
Pages (from-to)132-145
Number of pages14
JournalIEEE Transactions on Medical Imaging
Volume29
Issue number1
DOIs
StatePublished - Jan 2010
Externally publishedYes

Keywords

  • Brain
  • Diffusion tensor imaging (DTI)
  • Registration
  • Tractography

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