ENHANCEMENT OF LOW-DOSE CT SCANS
In this paper the problem of enhancement of low-dose CT scans was considered. In particular, popular pre-processing algorithms (such as anisotropic diffusion filter, non-local means filter, mean-shift filter) were tested and analyzed. The assessment of image quality improvement was performed based on the artificially generated artifacts, similar to those appearing in low-dose CT scans . Their effectiveness was investigated using the image quality measures, such as the mean square error and the structural similarity index.
computer tomography (CT); low-dose; x-ray; hydrocephalus; segmentation
Buades,A., Coll, B. Morel J.M.: A non-local algorithm for image denoising. Computer Vision and Pattern Recognition, 2, 2005, s. 60-65.
Dorin C., Meer P: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 2002, s. 603–619.
Perona P., Malik J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (7), 1990, s. 629–639.
Sasi C., Jagan A., Kaur J., Jyoti D., Rao D.S.: Image quality assessment techniques on spatial domain, International Journal Of Computer Science & Technology, 2 (3), 2011, s. 177-184
Tian J., Chen L., Ma L., A wavelet-domain non-parametric statistical approach for image denoising. IEICE Electronics Express, 7, 2010, s. 1409-1415.
Węgliński T., Fabijańska A.: Enhancement of low-dose CT brain scans using graph-based anisotropic interpolation. Image Processing & Communications Challenges 5, Advances in Intelligent Systems and Computing, 233, 2014, s. 29-36
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