Title :
Optimizing non-local means for denoising low dose CT
Author :
Kelm, Zachary S. ; Blezek, Daniel ; Bartholmai, Brian ; Erickson, Bradley J.
Author_Institution :
Dept. of Physiol. & Biomed. Eng., Mayo Clinic, Rochester, MN, USA
fDate :
June 28 2009-July 1 2009
Abstract :
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 areas of improvement. The filter uses a weighted average of similar regions to denoise each image pixel. Though the classic formulation uses only patches from the image being filtered, these patches can, in principle, be drawn from other images. In CT images, patches can be drawn from neighboring slices. We used that potential to increase the peak signal-to-noise ratio (PSNR) by over 4 dB when denoising low dose phantom CT images, and quantitatively demonstrated the filter´s sensitivity to adjustment of each of its parameters.
Keywords :
computerised tomography; diagnostic radiography; image denoising; medical image processing; CT imaging; low dose CT diagnostic quality; low dose CT image denoising; low dose images; nonlocal means; radiation induced cancer; signal to noise ratio; Biomedical imaging; Computed tomography; Image reconstruction; Imaging phantoms; Information filtering; Information filters; Noise reduction; PSNR; Pixel; Signal to noise ratio; Non-local means; denoising; image processing; low dose CT; optimal parameters;
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-3931-7
Electronic_ISBN :
1945-7928
DOI :
10.1109/ISBI.2009.5193134