Multiscale TV flow with applications to fast denoising and registration

Prashant Athavale; Robert Xu; Perry Radau; Adrian Nachman; Graham Wright

doi:10.1117/12.2007190

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13 March 2013 Multiscale TV flow with applications to fast denoising and registration

Prashant Athavale, Robert Xu, Perry Radau, Adrian Nachman, Graham Wright

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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86692K (2013) https://doi.org/10.1117/12.2007190
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Medical images consist of image structures of varying scales, with different scales representing different components. For example, in cardiac images, left ventricle, myocardium and blood pool are the large scale structures, whereas infarct and noise are represented by relatively small scale structures. Thus, extracting different scales in an image i.e. multiscale image representation, is a valuable tool in medical image processing. There are various multiscale representation techniques based on different image decomposition algorithms and denoising methods. Gaussian blurring with varying standard deviation can be considered as a multiscale representation, but it diffuses the image isotropically, thereby diffusing main edges. On the other hand, inverse scale representations based on variational formulations preserve edges; but they tend to be time consuming and thus unsuitable for real-time applications. In the present work, we propose a fast multiscale representation technique, motivated by successive decomposition of smooth parts based on total variation (TV ) minimization. Thus, we smooth a given image at an increasing scale, producing a multiscale TV representation. As noise is a small scale component of an image, we can effectively use the proposed method for denoising . We also prove that the denoising speed, up to the time-step, is determined by the user, making the algorithm well-suited for real-time applications. The proposed method inherits edge preserving property from total variation flow. Using this property, we propose a novel multiscale image registration algorithm, where we register corresponding scales in images, thereby registering images efficiently and accurately.

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Prashant Athavale, Robert Xu, Perry Radau, Adrian Nachman, and Graham Wright "Multiscale TV flow with applications to fast denoising and registration", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86692K (13 March 2013); https://doi.org/10.1117/12.2007190

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