Current Issue : July - September Volume : 2014 Issue Number : 3 Articles : 5 Articles
Existing fractional-order Perona-Malik Diffusion (FOPMD) algorithms are defined as fully spatial fractional-order derivatives\n(FSFODs). However, we argue that FSFOD is not the best way for diffusion since different parts of spatial derivative play different\nroles in Perona-Malik diffusion (PMD) and derivative orders should be decided according to their roles. Therefore, we adopt a\nnovel fractional-order diffusion scheme, named external fractional-order gradient vector Perona-Malik diffusion (EFOGV-PMD),\nby only replacing integer-order derivatives of ââ?¬Å?externalââ?¬Â gradient vector to their fractional-order counterpartswhile keeping integerorder\nderivatives of gradient vector for diffusion coefficients since the ability of edge indicator for 1-order derivative is demonstrated\nboth in theory and applications. Here ââ?¬Å?externalââ?¬Â indicates the spatial derivatives except for the derivatives used in diffusion\ncoefficients. In order to demonstrate the power of the proposed scheme, some real sinograms of low-dosed computed tomography\n(LDCT) are used to compare the different performances. These schemes include PMD, regularized PMD (RPMD), and FOPMD.\nExperimental results show that the new scheme has good ability in edge preserving, is convergent quickly, has good stability for\niteration number, and can avoid artifacts, dark resulting images, and speckle effect....
For decades, computed tomography (CT) images have been widely used to discover valuable anatomical information. Metallic\nimplants such as dental fillings cause severe streaking artifacts which significantly degrade the quality of CT images. In this paper,\nwe propose a new method for metal-artifact reduction using complementary magnetic resonance (MR) images. The method\nexploits the possibilities which arise from the use of emergent trimodality systems.The proposed algorithm corrects reconstructed\nCT images. The projected data which is affected by dental fillings is detected and the missing projections are replaced with data\nobtained from a corresponding MR image. A simulation study was conducted in order to compare the reconstructed images with\nimages reconstructed through linear interpolation, which is a common metal-artifact reduction technique.The results show that\nthe proposedmethod is successful in reducing severe metal artifacts without introducing significant amount of secondary artifacts....
Purpose. Dual-energy CT imaging tends to suffer from much lower signal-to-noise ratio than single-energy CT. In this paper, we\npropose an improved anticorrelated noise reduction (ACNR)method without causing cross-contamination artifacts. Methods. The\nproposed algorithm diffuses both basis material density images (e.g., water and iodine) at the same time using a novel correlated\ndiffusion algorithm. The algorithm has been compared to the original ACNR algorithm in a contrast-enhanced, IRB-approved\npatient study. Material density accuracy and noise reduction are quantitatively evaluated by the percent density error and the\npercent noise reduction. Results. Both algorithms have significantly reduced the noises of basis material density images in all\ncases. The average percent noise reduction is 69.3% and 66.5% with the ACNR algorithmand the proposed algorithm, respectively.\nHowever, the ACNR algorithm alters the original material density by an average of 13% (or 2.18mg/cc) with a maximum of 58.7%\n(or 8.97mg/cc) in this study. This is evident in the water density images as massive cross-contaminations are seen in all five clinical\ncases. On the contrary, the proposed algorithm only changes the mean density by 2.4% (or 0.69mg/cc) with amaximum of 7.6% (or\n1.31mg/cc). The cross-contamination artifacts are significantly minimized or absent with the proposed algorithm. Conclusion. The\nproposed algorithm can significantly reduce image noise present in basis material density images from dual-energy CT imaging,\nwith minimized cross-contaminations compared to the ACNR algorithm....
Aswe all know, any practical computed tomography (CT) projection datamore or less contains noises.Hence, itwill be inconvenient\nfor the postprocessing of a reconstructed 3D image even when the noise in the projection data is white. The reason is that the\nnoise in the reconstructed image may be nonwhite. X-ray transform can be applied to the three dimensional (3D) CT, depicting\nthe relationship between material density and ray projection. In this paper, nontensor product relationship between the two\ndimensional (2D) mother wavelet and 3D mother wavelet is obtained by taking X-ray transform projection of 3D mother wavelet.\nWe proved that the projection of the 3Dmother wavelet is a 2Dmother wavelet if the 3Dmother wavelet satisfies certain conditions.\nSo, the 3D wavelet transformof a 3D image can be implemented by the 2D wavelet transformof its X-ray transformprojection and\nit will contribute to the reduction complexity and computation time during image processing.What is more, it can also avoid noise\ntransfer and amplification during the processing of CT image reconstruction....
The electronic structure of the Co-doped indium tin oxide (ITO) diluted magnetic semiconductors (DMSs) were investigated\ntheoretically from first principles, using the fully relativistic Dirac linear muffin-tin orbital band structure method. The X-ray\nabsorption spectra (XAS) and X-ray magnetic circular dichroism (XMCD) spectra at the Co ??3, In??2, Sn??2, andO?? edges were\ninvestigated theoretically from first principles. The origin of the XMCD spectra in these compounds was examined.The calculated\nresults are compared with available experimental data....
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