Enhancement of Bone Fracture Image Using Filtering Techniques Authors: Muhammad Luqman Bin Muhd Zain, Irraivan

Elamvazuthi and Mumtaj Begam Matthew Dunning

Fibula Fracture

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.

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.

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

Start

Original Image

Contrast Enhancement

Average FilteringMedian Filtering Weiner Filtering

Result (Output Image)

Histogram

End

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

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.

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.