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Ivo Dinov
Title Quantitative Comparison and Analysis of Image Registration Using Frequency-Adaptive Wavelet Shrinkage
Author(s) Dinov ID, Mega MS, Thompson PM, Woods RP, Sumners DWL, Sowell EL, Toga AW
Publication date Jan 01 1970
Publisher IEEE-TITB
Number of pages 12
Summary Wavelet Analysis of Image Registration (WAIR). All software available at:
Comments 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. The goals of deforming and mapping data into a reference space are to simplify the visualization and interpretation of the data across subjects, and to achieve more tractable, theoretically sound and robust quantitative data analysis.

Despite the large number of image-registration (warping) techniques developed recently in the literature only few studies have been undertaken to numerically characterize and compare various alignment methods. In this paper we introduce a new approach foranalyzing image registration, based on a selective wavelet reconstruction technique using a frequency adaptive wavelet shrinkage.

We employ a wavelet analysis of image registration (WAIR) quantizer to study various affine and non-affine warping methods applied to groups of stereotaxic human brain structural (magnetic resonance imaging, MRI) and functional (positron emission tomography, PET, single photon emission computed tomography, SPECT) data. Depending upon the aim of the image-registration we present several warp classification schemes.

The new WAIR technique, and toolkit, are tested on sets of PET and MRI volumetric data. Two affine and four non-affine registration methods are applied to align the data into a common anatomical space. Our method uses a concise representation of the native and resliced (pre- and post-warp) data in compressed wavelet spaceto assess goodness 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.

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