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Full-Text Articles in Engineering
Denoising Medical Imagery Using A Novel Framework, Samuel Peter Kozaitis, Jeetkumar M. Mehta, S. Ponkia
Denoising Medical Imagery Using A Novel Framework, Samuel Peter Kozaitis, Jeetkumar M. Mehta, S. Ponkia
Electrical Engineering and Computer Science Faculty Publications
We proposed a novel framework that allows a method optimized for white noise to be used for denoising CT imagery. We considered low-dose x-ray CT imagery where lowering the dose of x-rays results in an increase in quantum noise. We first denoised an image independently several times using different parameters. Then, we selected pixels from those denoised images to form a final composite image. We produced results using blockmatching denoising, but in principle other methods could work within this framework, as well. The proposed method was able to better reproduce regions of low-contrast than the conventional BM3D approach.
Blood Vessel Segmentation In Magnetic Resonance Angiography Imagery, Samuel Peter Kozaitis, Raghu Chandramohan
Blood Vessel Segmentation In Magnetic Resonance Angiography Imagery, Samuel Peter Kozaitis, Raghu Chandramohan
Electrical Engineering and Computer Science Faculty Publications
Small blood vessels may be difficult to detect in magnetic resonance angiography due to the lack of blood flow caused by disease or injury. Our method, which uses a block-matching denoising approach to segment blood vessels, works well in the presence of noise. We examined extended regions of an image to determine whether they contained blood vessels by fitting a Gaussian mixture model to a region's histogram. Then, dissimilar regions were denoised separately. This approach was beneficial in low-contrast settings. It can be used to detect higher-order blood vessels that may be difficult to detect under normal conditions.