Medical image restoration

One of the arguably most important applications where we use image restoration is medical imaging. We are talking about almost all kinds of medical imaging (radiography,  magnetic resonance imaging (MRI), tomography and so on). 

Why we said it is “arguably most important”? Most important because everybody (doctors, engineers, patients) can recognize the very importance of the good medical image in diagnostic (and control of disease progression) process. Arguably, because there are other very important applications where we use image restoration so it is hard to make a fair comparison. Our goal here is not to try to do that. 

Here, like in many of our previous topics, we will try to introduce the problem itself through several examples. 

• Some of the artifacts in medical images arise because of the very device that we are using for taking images. Probably, the best representative of this problem is the presence of noise. Beginners in image processing soon discover that a denoising step is often required before any relevant information can be extracted from an image. That is because noise can “cover” some important structure in the image or for example destroy existing edges in the image, important for a proper diagnostic process. 

Noisy and denoised image of the brain [1] 

• The other example can be existing of the blur in medical images. Blur occurs because of the device we are using (like out-of-focus blur) but also because of some other effects that sometimes we cannot control such as movements of a patient. For many medical images, it is not recommended to repeat a process of taking the image because of safety (radiography radiation), time (long duration of a procedure itself) or even money, so doctors need to use what they have. And sometimes the only thing they have is the image with the blurry artifacts. 

Deblurring real-degraded Brain CT images: (C1) naturally blurred full-size CT images; (C2) zoomed-in portions; (C3) images from top to bottom are deblurred by Zohair filter [2]

References:

[1] P. Coupé, P. Yger, S. Prima, P. Hellier, C. Kervrann, C. Barillot, An Optimised Blockwise NonLocal Means Denoising Filter for 3-D Magnetic Resonance Images, IEEE Transactions on Medical Imaging, 27(4):425–441, 2008.

[2] Z. Al-Ameen, G. Sulong, A novel Zohair filter for deblurring computed tomography medical images. International Journal of Imaging Systems and Technology 25(3), 2015.