Mathematical Biosciences and Engineering, 2006, 3(2): 389-418. doi: 10.3934/mbe.2006.3.389.

Primary: 68U10; Secondary: 92C55, 62P10, 94A08.

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Multiscale Image Registration

1. Department of Mathematics, Stanford University, Stanford, CA 94305-2125
2. Department of Radiation Oncology, Stanford University, Stanford, CA 94305-5947

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.
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Keywords MRI; noise; CT; mutual information.; image registration; multiscale analysis

Citation: Dana Paquin, Doron Levy, Eduard Schreibmann, Lei Xing. Multiscale Image Registration. Mathematical Biosciences and Engineering, 2006, 3(2): 389-418. doi: 10.3934/mbe.2006.3.389


This article has been cited by

  • 1. Jinpeng Zhang, Qian Wang, Guorong Wu, Dinggang Shen, , Medical Imaging and Augmented Reality, 2016, Chapter 38, 415, 10.1007/978-3-319-43775-0_38

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