Abstract
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.
Highlights
Purpose/Objective(s): Often in medical image processing, images must be spatially aligned to allow practioners to perform quantitative analyses of the images
Materials/Methods: Sample brain proton density slice and brain mid-sagittal slice images were obtained from the Insight Segmentation and Registration Toolkit (ITK), and known rigid and deformable transformations were applied to the images
Synthetic impulse and speckle noise was added to the images, and image registration simulations were conducted to determine the precise noise levels at which image registration using ordinary techniques fails
Summary
Purpose/Objective(s): Often in medical image processing, images must be spatially aligned to allow practioners to perform quantitative analyses of the images. The process of aligning images taken, for example, at different times, from different perspectives, or from different imaging devices is called image registration. Numerous successful image registration techniques have been published, ordinary techniques are shown to fail when one or more of the images to be registered contains significant levels of noise.
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