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Histogram-Based Optical Flow for Motion Estimation in Ultrasound Imaging

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Abstract

Motion estimation on ultrasound data is often referred to as ‘Speckle Tracking’ in clinical environments and plays an important role in diagnosis and monitoring of cardiovascular diseases and the identification of abnormal cardiac motion. The impact of physical effects in the process of data acquisition raises many problems for conventional image processing techniques. The most significant difference to other medical data is its high level of speckle noise, which has completely different characteristics from other noise models, e.g., additive Gaussian noise. In this paper we address the problem of multiplicative speckle noise for motion estimation techniques that are based on optical flow methods and prove that the influence of this noise leads to wrong correspondences between image regions if not taken into account. To overcome these problems we propose the use of local statistics and introduce an optical flow method which uses histograms as discrete representations of local statistics for motion analysis. We show that this approach is more robust under the presence of speckle noise than classical optical flow methods.

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Acknowledgements

We thank Rico Lehmann for supporting us with his extension of the US speckle phantom and Olga Friesen for her helpful hints on statistics. Additionally, we thank Martin Burger and Alex Sawatzky for fruitful discussions on physical noise models. This study was supported by the Deutsche Forschungsgemeinschaft (DFG), SFB 656 MoBil, Münster, Germany (projects B3, C3) and the IZKF Münster Core Unit “Small animal ultrasound: imaging and therapy” (ECHO).

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Correspondence to Daniel Tenbrinck.

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D. Tenbrinck and S. Schmid contributed equally to this work.

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Tenbrinck, D., Schmid, S., Jiang, X. et al. Histogram-Based Optical Flow for Motion Estimation in Ultrasound Imaging. J Math Imaging Vis 47, 138–150 (2013). https://doi.org/10.1007/s10851-012-0398-z

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