Quantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage
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, on...
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| Veröffentlicht in: | IEEE journal of biomedical and health informatics 2002-03, Vol.6 (1), p.73-85 |
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| creator | Dinov, I.D. Mega, M.S. Thompson, P.M. Woods, R.P. Sumners, D.L. Sowell, E.L. Toga, A.W. |
| description | 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. |
| doi_str_mv | 10.1109/4233.992165 |
| format | Article |
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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. 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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.</description><subject>Biomedical imaging</subject><subject>Brain</subject><subject>Brain - anatomy & histology</subject><subject>Brain - physiopathology</subject><subject>Frequency</subject><subject>Humans</subject><subject>Image analysis</subject><subject>Image coding</subject><subject>Image reconstruction</subject><subject>Image registration</subject><subject>Magnetic analysis</subject><subject>Magnetic Resonance Imaging</subject><subject>Mathematical models</subject><subject>Polynomials</subject><subject>Representations</subject><subject>Shrinkage</subject><subject>Warpage</subject><subject>Warping</subject><subject>Wavelet</subject><subject>Wavelet analysis</subject><issn>1089-7771</issn><issn>2168-2194</issn><issn>1558-0032</issn><issn>2168-2208</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0s9rFDEUB_Agiq3VkzcPMnhQoUzNy-8cpWgtFETQ8_Bm8mZNOzuzJjMt-9-bdRcFD-0hJJDP-5LwHmMvgZ8BcP9BCSnPvBdg9CN2DFq7mnMpHpczd7621sIRe5bzNeegNMin7AjAS6O9P2Y33xYc5zjjHG-p6qb1BlPM01jhGMrCYZtjrqa-ahPGsYprXFGVaBXznEpNgUuO46rqE_1aaOy2NQbc_Am7w1saaK7yzxTHm1L3nD3pccj04rCfsB-fP30__1Jffb24PP94VXdKu7kODgIKNCYY07YOUfXO9QZNC9ihdSSUQe8dKtuK0LbSB9BAMoBSVvZSnrB3-9xNmsqj8tysY-5oGHCkacmNcw64AYAi394rLWinJJgHoXAajJLqYWiNMcLtEt_fC8FYKIFWuELf_EevpyWV3uy-opQHL2xBp3vUpSnnRH2zSaVbadsAb3Zz0uzmpNnPSdGvD5FLu6bwzx4Go4BXexCJ6O_1ofo3i5XAwQ</recordid><startdate>20020301</startdate><enddate>20020301</enddate><creator>Dinov, I.D.</creator><creator>Mega, M.S.</creator><creator>Thompson, P.M.</creator><creator>Woods, R.P.</creator><creator>Sumners, D.L.</creator><creator>Sowell, E.L.</creator><creator>Toga, A.W.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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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. 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| subjects | Biomedical imaging Brain Brain - anatomy & histology Brain - physiopathology Frequency Humans Image analysis Image coding Image reconstruction Image registration Magnetic analysis Magnetic Resonance Imaging Mathematical models Polynomials Representations Shrinkage Warpage Warping Wavelet Wavelet analysis |
| title | Quantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage |
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