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

TitleQuantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage.
Publication TypeJournal Article
Year of Publication2002
AuthorsDinov, ID, Mega MS, Thompson PM, Woods RP, Sumners DWL, Sowell EL, Toga AW
JournalIEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society
Date Published2002 Mar
KeywordsBrain, Humans, Magnetic Resonance Imaging

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 selective-wavelet 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.

Alternate JournalIEEE Trans Inf Technol Biomed