Ultrasound Image Enhancement Paper Review

1. Ultrasound Image Enhancement Using
Structure-Based Filtering
Ultrasonics in Medicine (BMEG – 6316)
Journal Name: Hindawi Publishing Corporation
Computational and Mathematical Methods in
Medicine
Publishing Date: June 19, 2014
Authors: Shyh-Kuang Ueng, Guan-Zhi Chen &
Cho-Li Yen
December 2022
Center of Biomedical Engineering
Addis Ababa Institute of Technology (AAiT)
Addis Ababa University , Ethiopia
Reviewers:
Sakata Abera , Solomon Assefa &
Obseni Legesse
Paper Review
Course Instructor: Gizeaddis L. (Ph.D.)

2. Introduction
• Explained as ultrasound images are susceptible to noises
produced by echoes from a homogeneous structures in a
tissue during scanning.
• Interference produces speckle pattern in the ultrasound
image.
• Speckles deteriorate tissue boundary and make tissue
boundaries rough – Loss in information.
• Proposed procedures and technique used to get speckle
free image.
2
Sakata Abera ,Solomon Assefa & Obseni Legesse

3. Methods
• Proposed procedures and techniques used to get
speckle free image .
• Detailed experimental studies
• Statistical & Mathematical Computation – several
despeckling stages.
• Reduces noises produced by homogeneous tissue boundaries.
• Gives a better noise reduction and preserve edge.
• Developed different techniques to smooth noises and preserve
image features.
3
Sakata Abera ,Solomon Assefa & Obseni Legesse

4. Methods
• Referred different papers and methodologies to solve the
problem
• Gaussian Filters – Suppress speckle, but blurs edge
• Adaptive Gaussian filters
• Adaptive Median filters – produce unnatural
patterns on image
• They proposed structure-based despeckling method for
ultrasound data.
• Divided despeckle method in to several stages of pixel sizes.
4
Sakata Abera ,Solomon Assefa & Obseni Legesse

5. Methods
• Used different mathematical and statistical computations.
• They used Eigen system of a Hessian matrix to measure the
strength and orientation of specific image sections.
• Classify the image structures based on their pixel size.
• Feasible filters are adaptively selected to suppress speckle.
• Heterogeneous despeckling strategy - Pixels of different
types are smoothed by using different filters so that
speckles in uniform regions are reduced and tissue
boundaries and edges are preserved.
5
Sakata Abera ,Solomon Assefa & Obseni Legesse

6. Methods
6
The flowchart of the proposed despeckle method
Sakata Abera ,Solomon Assefa & Obseni Legesse

7. Result
• The final result employs combination of the
following filters.
• 2D median filter
• 1D Gaussian filter and
• 2D Gaussian filter
• Test results show that the despeckle method used
reduces speckles in uniform areas and enhances
tissue boundaries and spots.
7
Sakata Abera ,Solomon Assefa & Obseni Legesse

8. Discussion
• Six test images, two ultrasound image and four grey scale
images are filtered.
• The resulted images filtered are compared visually,
computed and corrected with developed mathematical
correction parameters.
• Peak signal-to-noise ratio (PSNR) and structural similarity
index measure (SSIM) values are used to evaluate the
filtered results.
8
Sakata Abera ,Solomon Assefa & Obseni Legesse

9. Discussion
• The proposed method produces the best PSNR
values in most cases.
• Compared with the other filters, the proposed
method usually produces better SSIM values.
9
Sakata Abera ,Solomon Assefa & Obseni Legesse

10. Conclusion
• In this paper, they presented procedures of
despeckling ultrasound data.
• Their method is capable of reducing speckles in
homogeneous tissue regions.
• Preserving edges, enhancing region boundaries in
heterogeneous regions and removes multiplicative
noises for grey-level images based on the proposed
statistical mathematical results presented.
10
Sakata Abera ,Solomon Assefa & Obseni Legesse

11. Gap Analysis
• If all of the tests fail, it cannot identify the structure
type, and thus the pixel is classified as unknown
typed.
• Three passes of the despeckling pipeline are
required to reduce speckles.
• Extra filtering methods are required for some
images with a different noise level.
11
Sakata Abera ,Solomon Assefa & Obseni Legesse

12. ☺
Sakata Abera ,Solomon Assefa & Obseni Legesse
12