Quantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage

Ivo D. Dinov, Michael S. Mega, Paul M. Thompson, Roger P. Woods, De Witt L. Sumners, Elizabeth L. Sowell, Arthur W. Toga

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

In the field of template-based medical image analysis, image registration and normalization are frequently used to evaluate and interpret data in a standard template or reference atlas space. Despite the large number of image-registration (warping) techniques developed recently in the literature, only a few studies have been undertaken to numerically characterize and compare various alignment methods. In this paper, we introduce a new approach for analyzing image registration based on a selectivewavelet reconstruction technique using a frequency-adaptive wavelet shrinkage. We study four polynomial-based and two higher complexity nonaffine warping methods applied to groups of stereotaxic human brain structural (magnetic resonance imaging) and functional (positron emission tomography) data. Depending upon the aim of the image registration, we present several warp classification schemes. Our method uses a concise representation of the native and resliced (pre- and post-warp) data in compressed wavelet space to assess quality of registration. This technique is computationally inexpensive and utilizes the image compression, image enhancement, and denoising characteristics of the wavelet-based function representation, as well as the optimality properties of frequency-dependent wavelet shrinkage.

Original languageEnglish
Pages (from-to)73-85
Number of pages13
JournalIEEE Transactions on Information Technology in Biomedicine
Volume6
Issue number1
DOIs
StatePublished - Mar 2002
Externally publishedYes

Keywords

  • Brain imaging
  • Magnetic resonance imaging (MRI)
  • Positron emission tomography (PET)
  • Quality of image registration
  • Wavelets

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