Ms. Xue Ren Profile

Xue Ren

at Pai Chai Univ

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Proceedings Article | 29 March 2016 Paper

A comparative study of the effects of using normalized patches for penalized likelihood tomographic reconstruction

Xue Ren, Soo-Jin Lee

Proceedings Volume 9783, 97834N (2016) https://doi.org/10.1117/12.2216471

KEYWORDS: Tomography, Image denoising, Image restoration, Computed tomography, Single photon emission computed tomography, Positron emission tomography, Surface plasmons, Switching, Control systems, Monte Carlo methods, Switches, Binary data, Expectation maximization algorithms

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Patch-based regularization methods, which have proven useful not only for image denoising, but also for tomographic reconstruction, penalize image roughness based on the intensity differences between two nearby patches. However, when two patches are not considered to be similar in the general sense of similarity but still have similar features in a scaled domain after normalizing the two patches, the difference between the two patches in the scaled domain is smaller than the intensity difference measured in the standard method. Standard patch-based methods tend to ignore such similarities due to the large intensity differences between the two patches. In this work, for patch-based penalized likelihood tomographic reconstruction, we propose a new approach to the similarity measure using the normalized patch differences as well as the intensity-based patch differences. A normalized patch difference is obtained by normalizing and scaling the intensity-based patch difference. To selectively take advantage of the standard patch (SP) and normalized patch (NP), we use switching schemes that can select either SP or NP based on the gradient of a reconstructed image. In this case the SP is selected for restoring large-scaled piecewise-smooth regions, while the NP is selected for preserving the contrast of fine details. The numerical experiments using software phantom demonstrate that our proposed methods not only improve overall reconstruction accuracy in terms of the percentage error, but also reveal better recovery of fine details in terms of the contrast recovery coefficient.

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