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Joint Motion Estimation and Layer Segmentation in Transparent Image Sequences—Application to Noise Reduction in X-Ray Image Sequences

Abstract

This paper is concerned with the estimation of the motions and the segmentation of the spatial supports of the different layers involved in transparent X-ray image sequences. Classical motion estimation methods fail on sequences involving transparent effects since they do not explicitly model this phenomenon. We propose a method that comprises three main steps: initial block-matching for two-layer transparent motion estimation, motion clustering with 3D Hough transform, and joint transparent layer segmentation and parametric motion estimation. It is validated on synthetic and real clinical X-ray image sequences. Secondly, we derive an original transparent motion compensation method compatible with any spatiotemporal filtering technique. A direct transparent motion compensation method is proposed. To overcome its limitations, a novel hybrid filter is introduced which locally selects which type of motion compensation is to be carried out for optimal denoising. Convincing experiments on synthetic and real clinical images are also reported.

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Correspondence to Vincent Auvray.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Auvray, V., Bouthemy, P. & Liénard, J. Joint Motion Estimation and Layer Segmentation in Transparent Image Sequences—Application to Noise Reduction in X-Ray Image Sequences. EURASIP J. Adv. Signal Process. 2009, 647262 (2009). https://doi.org/10.1155/2009/647262

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Keywords

  • Image Sequence
  • Noise Reduction
  • Motion Estimation
  • Parametric Motion
  • Joint Motion
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