How can I reduce/replace Metal artifacts on medical images?
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Mohammed Njiti
on 28 Sep 2023
Commented: Mohammed Njiti
on 27 Oct 2023
Artefact=imread('C:\Users\USER\Desktop\My Research\artefact1.jpg');
Artefact=rgb2gray(Artefact);
Artefact=imadjust(Artefact);
thresh = adaptthresh(Artefact,"ForegroundPolarity","dark");
Artefactmask = imbinarize(Artefact,thresh);
se = strel('disk',2);
Artefactmask = imclose(Artefactmask,se);
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Accepted Answer
prabhat kumar sharma
on 27 Oct 2023
Hi Mohammed,
I understand you are facing the issue with removal of metal artifacts.
To reduce or replace metal artifacts on medical images, you can try different approaches. Here are a few techniques you can consider:
1. Metal Artifact Reduction (MAR) algorithms: MAR algorithms are specifically designed to reduce metal artifacts in medical images. These algorithms typically involve manipulating the original x-ray projection data before reconstructing the image. Various techniques, such as interpolation, segmentation, and statistical modeling, are used to estimate and correct the artifacts caused by metal objects.
2. Image post-processing techniques: After reconstructing the image, you can apply post-processing techniques to further reduce or replace metal artifacts. Some common approaches include:
- Thresholding and masking: You can use thresholding techniques, such as adaptive thresholding or global thresholding, to segment the metal regions in the image. Then, apply a binary mask to remove or replace the pixels within the metal regions.
- Image inpainting: Inpainting techniques can be used to fill in the missing or corrupted regions caused by metal artifacts. These techniques estimate the missing pixel values based on the surrounding information.
You can refer this documentation for Inpainting: https://mathworks.com/help/images/ref/inpaintexemplar.html
- Image filtering: Applying appropriate filters, such as median filtering or guided filtering, can help reduce noise and smooth out the artifacts in the image.
You can refer this documentation to know more on image filtering: https://mathworks.com/help/images/linear-filtering.html
- Image reconstruction: If the metal artifact area is small, you can consider using image reconstruction techniques, such as patch-based or super-resolution methods, to enhance the details and reduce the artifacts.
You can refer this documentation to know more on image reconstruction: https://mathworks.com/discovery/image-reconstruction.html
Artifacts removal is the current research area, and it is really challenging, lot of new techniques are arising, I hope the above solution will help in your case!
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