Feature-based VS. Intensity-based brain image registration: Comprehensive comparison using mutual information

L. A. Teverovskiy, O. T. Carmichael, H. J. Aizenstein, N. Lazar, Y. Liu

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

4 Scopus citations

Abstract

We propose a mutual information-based method for quantitative evaluation of the deformable registration algorithms at three levels: global, voxel-wise and anatomical structure. We compare two fully deformable registration algorithms: feature-based HAMMER and a set of intensity-based algorithms (FEMDemons) in the ITK package. Evaluation is carried out using the AAL template image with 116 labeled anatomical structures and a set of 59 MR brain images: 20 normal controls (CTL), 20 Alzheimer's disease patients (AD) and 19 mild cognitive impairment patients (MCI). We show that both HAMMER and FEM-Demons perform significantly better than an affine registration algorithm, FLIRT, at all three levels. At the global level, FEM-Demons outperforms HAMMER on the images of AD and MCI patients. At the local and anatomical levels, FEM-Demons and HAMMER dominate each other on different brain regions.

Original languageEnglish (US)
Title of host publication2007 4th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
Pages576-579
Number of pages4
DOIs
StatePublished - 2007
Event2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States
Duration: Apr 12 2007Apr 15 2007

Publication series

Name2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings

Other

Other2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
Country/TerritoryUnited States
CityArlington, VA
Period4/12/074/15/07

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • General Medicine

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