Noise modeling and variance stabilization of a computed radiography (CR) mammography system subject to fixed-pattern noise
Borges, Lucas R.; Brochi, Marco A.C.; Xu, Zhongwei; Foi, Alessandro; Vieira, Marcelo A.C.; Azevedo-Marques, Paulo M. (2020-11-11)
Borges, Lucas R.
Brochi, Marco A.C.
Vieira, Marcelo A.C.
Azevedo-Marques, Paulo M.
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited
Julkaisun pysyvä osoite on
In this work we model the noise properties of a computed radiography (CR) mammography system by adding an extra degree of freedom to a well-established noise model, and derive a variance-stabilizing transform (VST) to convert the signal-dependent noise into approximately signal-independent. The proposed model relies on a quadratic variance function, which considers fixed-pattern (structural), quantum and electronic noise. It also accounts for the spatial-dependency of the noise by assuming a space-variant quantum coefficient. The proposed noise model was compared against two alternative models commonly found in the literature. The first alternative model ignores the spatial-variability of the quantum noise, and the second model assumes negligible structural noise. We also derive a VST to convert noisy observations contaminated by the proposed noise model into observations with approximately Gaussian noise and constant variance equals to one. Finally, we estimated a look-up table that can be used as an inverse transform in denoising applications. A phantom study was conducted to validate the noise model, VST and inverse VST. The results show that the space-variant signal-dependent quadratic noise model is appropriate to describe noise in this CR mammography system (errors< 2.0% in terms of signal-to-noise ratio). The two alternative noise models were outperformed by the proposed model (errors as high as 14.7% and 9.4%). The designed VST was able to stabilize the noise so that it has variance approximately equal to one (errors< 4.1%), while the two alternative models achieved errors as high as 26.9% and 18.0%, respectively. Finally, the proposed inverse transform was capable of returning the signal to the original signal range with virtually no bias.
- TUNICRIS-julkaisut