This document discusses different filtering techniques to enhance bone fracture images from ultrasounds. It analyzes median, average, and Wiener filtering by applying each technique to ultrasound images and comparing the results. Specifically, it contrasts the ability of each filter to reduce speckle noise while preserving image edges. The analysis found that while median and Wiener filtering both reduced speckle effectively, Wiener filtering best maintained the integrity of image edges. Therefore, the document concludes the Wiener filter is the most effective approach for enhancing bone fracture ultrasounds.

1. Enhancement of Bone Fracture
Image Using Filtering Techniques
 Authors: Muhammad Luqman Bin Muhd Zain, Irraivan
Elamvazuthi and Mumtaj Begam
 Matthew Dunning

2. Fibula Fracture

3. Problem & Why It’s Important
 The issue is that when most ultrasounds are done for
fractures/diseases the picture comes out to be unclear due to
speckles in the image.
 The importance is that because of these speckles, it does not
allow fast interpretations to be made.
 Sometimes if fast interpretations cannot be made, if a person
has a disease or condition that is life threatening, they cannot
get treatment right away.

4. State of the Art
 Ultrasounds are non-invasive, portable and do not require
ionizing radiations; however the images are complex.
 There have been multiple papers produced with different
methods of removing the speckle.
 Particle Swarm Optimization technique
 Wavelet Thresholding (Weighted Variance)
 Novel Bayesian Multiscale Method
 A gap based on this paper is that originally, they did not know
what type of filtering would be the best result.

5. Approach to problem
 The approach to the problem was to use three different
filtering techniques (median, average and Weiner filtering).
 The original image would go through a contrast enhancement
and then either median filtering, average filtering or a Weiner
filter.
 The pictures of the images going through each filter were
compared for speckle
 The data of the image was then shown in a histogram to
compare the intensity values, value of pixels

6. Start
Original Image
Contrast
Enhancement
Average FilteringMedian Filtering Weiner Filtering
Result (Output
Image)
Histogram
End

7. Different Filters (Math)
 Median Filtering: Used the function medfilt2, to filter the
contrasted photo, it replaces each value based upon the
neighbors of the value. Great way to remove noise.
 Average Filtering: Created a matrix (B) composed of ones,
size of image. Then I divided B/size of image. Then I took the
contrasted photo and multiplied it by B.
 Weiner Filter: restores the image due to a blur, or linear
motion.
 Contrast: Used function imadjust(picture). Maps grayscale
from original image; new matrix

8. Discussion
 This method has filled the gap on which method works the
best, it allows the ultrasound to go through a different filter
and the results of the histogram show which one does the
best job.

11. Results
 E) Median reduces speckle noise while keeping edges
 F) Average reduces speckle and image edges
 G) Wiener reduces speckle but edges remained intact
 Paper discussed that Median and Wiener were close;
however Wiener was picked as the best filter because of how
its edges remained intact.