Data-Adaptive Similarity Measures for B-mode Ultrasound Images Using Robust Noise Models

Research output: Contribution to journalArticleScientificpeer-review


Ultrasound imaging (UI) is characterized by the presence of multiplicative speckle noise and various acquisition artefacts. Designing ultrasound (US) similarity measures thus requires a particular attention. In the specific context of motion estimation, incorporating US characteristics does not only benefit traditional methods but also learning-based approaches, which are highly sensitive to the quality of training data. Deriving similarity measures from a maximum likelihood (ML) perspective allows us to take these specificities into account. As opposed to the classical Rayleigh modelling, the proposed similarity measures incorporate more realistic scattering conditions, such as, varying speckle densities and shadowing. Specifically, the deviations from the Rayleigh statistics are modelled using the t -distribution for the complex radio-frequency (RF) signals and the Nakagami-Gamma (NG) compound model for the echo amplitudes. Furthermore, the model parameters are learnt patch-wise, which leads to data-adaptive similarity measures. The proposed criteria are investigated in the context of motion estimation using synthetic, phantom, as well as 2D and 3D in vivo images. The experimental results show an improvement in performance and robustness in comparison to the classical Rayleigh-based approach and state-of-the-art similarity measures.
Original languageEnglish
Article number9115251
Pages (from-to)1244 - 1254
Number of pages11
JournalIEEE Journal of Selected Topics in Signal Processing
Issue number6
Publication statusPublished - Oct 2020
MoE publication typeA1 Journal article-refereed


  • similarity measures
  • ultrasound
  • heavy-tailed distributions
  • motion estimation
  • t-distribution
  • Nakagami-Gamma distribution
  • data-adaptive


Dive into the research topics of 'Data-Adaptive Similarity Measures for B-mode Ultrasound Images Using Robust Noise Models'. Together they form a unique fingerprint.

Cite this