Next Article in Journal
A Comparative Study on Violent Sloshing with Complex Baffles Using the ISPH Method
Next Article in Special Issue
A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging
Previous Article in Journal
Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
Previous Article in Special Issue
A Nonlinear Beamformer Based on p-th Root Compression—Application to Plane Wave Ultrasound Imaging
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(6), 903; https://doi.org/10.3390/app8060903

Speckle Reduction on Ultrasound Liver Images Based on a Sparse Representation over a Learned Dictionary

School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
*
Author to whom correspondence should be addressed.
Received: 20 April 2018 / Revised: 28 May 2018 / Accepted: 28 May 2018 / Published: 31 May 2018
(This article belongs to the Special Issue Ultrasound B-mode Imaging: Beamforming and Image Formation Techniques)
Full-Text   |   PDF [6208 KB, uploaded 31 May 2018]   |  

Abstract

Ultrasound images are corrupted with multiplicative noise known as speckle, which reduces the effectiveness of image processing and hampers interpretation. This paper proposes a multiplicative speckle suppression technique for ultrasound liver images, based on a new signal reconstruction model known as sparse representation (SR) over dictionary learning. In the proposed technique, the non-uniform multiplicative signal is first converted into additive noise using an enhanced homomorphic filter. This is followed by pixel-based total variation (TV) regularization and patch-based SR over a dictionary trained using K-singular value decomposition (KSVD). Finally, the split Bregman algorithm is used to solve the optimization problem and estimate the de-speckled image. The simulations performed on both synthetic and clinical ultrasound images for speckle reduction, the proposed technique achieved peak signal-to-noise ratios of 35.537 dB for the dictionary trained on noisy image patches and 35.033 dB for the dictionary trained using a set of reference ultrasound image patches. Further, the evaluation results show that the proposed method performs better than other state-of-the-art denoising algorithms in terms of both peak signal-to-noise ratio and subjective visual quality assessment. View Full-Text
Keywords: ultrasound; speckle reduction; medical image processing; sparse representation; K-singular value decomposition; dictionary learning; B-mode imaging ultrasound; speckle reduction; medical image processing; sparse representation; K-singular value decomposition; dictionary learning; B-mode imaging
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Jabarulla, M.Y.; Lee, H.-N. Speckle Reduction on Ultrasound Liver Images Based on a Sparse Representation over a Learned Dictionary. Appl. Sci. 2018, 8, 903.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top