Histogram-Based Optical Flow for Motion Estimation in Ultrasound Imaging

  • Authors:

  • Daniel Tenbrinck;Sönke Schmid;Xiaoyi Jiang;Klaus Schäfers;Jörg Stypmann

  • Affiliations:

  • Dept. of Mathematics and Computer Science, University of Münster, Münster, Germany;Dept. of Mathematics and Computer Science, University of Münster, Münster, Germany;Dept. of Mathematics and Computer Science, University of Münster, Münster, Germany;European Institute for Molecular Imaging, University of Münster, Münster, Germany;Dept. of Cardiology and Angiology, University Hospital of Münster, Münster, Germany

  • Venue:
  • Journal of Mathematical Imaging and Vision
  • Year:
  • 2013

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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.