Multiscale Image Registration
A multiscale image registration technique is presented for the registration of medical images that contain significant levels of noise. An overview of the medical image registration problem is presented, and various registration techniques are discussed. Experiments using mean squares, normalized...
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| Format: | Article |
| Language: | English |
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AIMS Press
2006-01-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2006.3.389 |
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| _version_ | 1832590273083867136 |
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| author | Dana Paquin Doron Levy Eduard Schreibmann Lei Xing |
| author_facet | Dana Paquin Doron Levy Eduard Schreibmann Lei Xing |
| author_sort | Dana Paquin |
| collection | DOAJ |
| description | A multiscale image registration technique is presented for the registration of medical images that contain significant levels of noise. An overview of the medical image registration problem is presented, and various registration techniques are discussed. Experiments using mean squares, normalized correlation, and mutual information optimal linear registration are presented that determine the noise levels at which registration using these techniques fails. Further experiments in which classical denoising algorithms are applied prior to registration are presented, and it is shown that registration fails in this case for significantly high levels of noise, as well. The hierarchical multiscale image decomposition of E. Tadmor, S. Nezzar, and L. Vese [20] is presented, and accurate registration of noisy images is achieved by obtaining a hierarchical multiscale decomposition of the images and registering the resulting components. This approach enables successful registration of images that contain noise levels well beyond the level at which ordinary optimal linear registration fails. Image registration experiments demonstrate the accuracy and efficiency of the multiscale registration technique, and for all noise levels, the multiscale technique is as accurate as or more accurate than ordinary registration techniques. |
| format | Article |
| id | doaj-art-9c1c437fcac04637baa27b3d229046ab |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2006-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-9c1c437fcac04637baa27b3d229046ab2025-01-24T01:51:19ZengAIMS PressMathematical Biosciences and Engineering1551-00182006-01-013238941810.3934/mbe.2006.3.389Multiscale Image RegistrationDana Paquin0Doron Levy1Eduard Schreibmann2Lei Xing3Department of Mathematics, Stanford University, Stanford, CA 94305-2125Department of Mathematics, Stanford University, Stanford, CA 94305-2125Department of Mathematics, Stanford University, Stanford, CA 94305-2125Department of Mathematics, Stanford University, Stanford, CA 94305-2125A multiscale image registration technique is presented for the registration of medical images that contain significant levels of noise. An overview of the medical image registration problem is presented, and various registration techniques are discussed. Experiments using mean squares, normalized correlation, and mutual information optimal linear registration are presented that determine the noise levels at which registration using these techniques fails. Further experiments in which classical denoising algorithms are applied prior to registration are presented, and it is shown that registration fails in this case for significantly high levels of noise, as well. The hierarchical multiscale image decomposition of E. Tadmor, S. Nezzar, and L. Vese [20] is presented, and accurate registration of noisy images is achieved by obtaining a hierarchical multiscale decomposition of the images and registering the resulting components. This approach enables successful registration of images that contain noise levels well beyond the level at which ordinary optimal linear registration fails. Image registration experiments demonstrate the accuracy and efficiency of the multiscale registration technique, and for all noise levels, the multiscale technique is as accurate as or more accurate than ordinary registration techniques.https://www.aimspress.com/article/doi/10.3934/mbe.2006.3.389mrinoisectmutualinformation.image registrationmultiscale analysis |
| spellingShingle | Dana Paquin Doron Levy Eduard Schreibmann Lei Xing Multiscale Image Registration Mathematical Biosciences and Engineering mri noise ct mutualinformation. image registration multiscale analysis |
| title | Multiscale Image Registration |
| title_full | Multiscale Image Registration |
| title_fullStr | Multiscale Image Registration |
| title_full_unstemmed | Multiscale Image Registration |
| title_short | Multiscale Image Registration |
| title_sort | multiscale image registration |
| topic | mri noise ct mutualinformation. image registration multiscale analysis |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2006.3.389 |
| work_keys_str_mv | AT danapaquin multiscaleimageregistration AT doronlevy multiscaleimageregistration AT eduardschreibmann multiscaleimageregistration AT leixing multiscaleimageregistration |