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Most of abdominal CT images include Gaussian noise, and CT scans form a blurry vision because of the internal fat tissue inside of abdomen. These two handicaps (noise and fat tissue) constitute an impediment in front of an accurate abdominal organ & tumour segmentation. Also segmentation techniques generally fall into error on segmentation of close grayscale regions. Therefore, denoising and enhancement...
The liver CT play an important role in the clinical diagnosis and treatment of liver disease. In this paper, we present a improved approach which introduce more information of texture image in the original NL-means algorithm. The improved algorithm keeps the excellent denioising function and preserving with edge, detail, especially the texture. The artificial artifact was also restrained effectively...
The Medical community uses several image acquisition techniques for diagnosing and suggesting the corresponding therapies. Therefore the obtained images from clinical examinations should be treated to assist doctors in results interpretation. In this paper, we focus on denoising task in order to determine the benefits and drawbacks for each algorithm. For this, we used as database, images acquired...
Functional properties of living tissues appear in PET, whereas structural information at significantly higher resolution and better image quality is provided by other modalities, such as CT or MRI. We illustrate how structural information of matched anatomic images can be used as priors in the total variation denoising and blind deconvolution of functional PET images. Experiments on phantom images...
Due to the rapid increase in use of CT imaging and the recently-heightened awareness of radiation-induced cancer, improving the diagnostic quality of low dose CT has become increasingly important. One potential method is to increase the signal-to-noise ratio of low dose images through denoising. Non-local means is a promising approach; however, it has many potentially adjustable parameters and application-specific...
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