Real-Time Nonlocal Means-Based Despeckling

In this paper, we propose a multiscale nonlocal means-based despeckling method for medical ultrasound. The multiscale approach leads to large computational savings and improves despeckling results over single-scale iterative approaches. We present two variants of the method. The first, denoted multi...

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Bibliographic Details
Published in:IEEE transactions on ultrasonics, ferroelectrics, and frequency control ferroelectrics, and frequency control, 2017-06, Vol.64 (6), p.959-977
Main Authors: Breivik, Lars Hofsoy, Snare, Sten Roar, Steen, Erik Normann, Solberg, Anne H. Schistad
Format: Article
Language:English
Subjects:
Acoustics
Algorithms
Anisotropic diffusion
Anisotropic magnetoresistance
Anisotropy
Computer simulation
despeckling
Disruption
Efficiency
Filtration
Frequency control
Ground truth
Heart - diagnostic imaging
Humans
Image Processing, Computer-Assisted - methods
Imaging
Iterative methods
Literature reviews
Methods
Models, Cardiovascular
multiscale
Multiscale analysis
Noise prediction
nonlocal means (NLM)
Phantoms, Imaging
Real time
Real-time systems
Robustness
Speckle
State of the art
Time Factors
Ultrasonic imaging
Ultrasonic methods
Ultrasonic testing
Ultrasonography - methods
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Summary:In this paper, we propose a multiscale nonlocal means-based despeckling method for medical ultrasound. The multiscale approach leads to large computational savings and improves despeckling results over single-scale iterative approaches. We present two variants of the method. The first, denoted multiscale nonlocal means (MNLM), yields uniform robust filtering of speckle both in structured and homogeneous regions. The second, denoted unnormalized MNLM (UMNLM), is more conservative in regions of structure assuring minimal disruption of salient image details. Due to the popularity of anisotropic diffusion-based methods in the despeckling literature, we review the connection between anisotropic diffusion and iterative variants of NLM. These iterative variants in turn relate to our multiscale variant. As part of our evaluation, we conduct a simulation study making use of ground truth phantoms generated from clinical B-mode ultrasound images. We evaluate our method against a set of popular methods from the despeckling literature on both fine and coarse speckle noise. In terms of computational efficiency, our method outperforms the other considered methods. Quantitatively on simulations and on a tissue-mimicking phantom, our method is found to be competitive with the state-of-the-art. On clinical B-mode images, our method is found to effectively smooth speckle while preserving low-contrast and highly localized salient image detail.
ISSN:0885-3010
1525-8955
DOI:10.1109/TUFFC.2017.2686326