Parametric Image Restoration Using Consensus: An Application to Nonstationary Noise Filtering

González-Jaime, Luis; Nachtegeal, Mike; Kerre, Etienne; Vegas-Sánchez-Ferrero, Gonzalo; Aja‐Fernández, Santiago

doi:10.1007/978-3-642-38628-2_42

Cited by 1 publication

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be seen as an extension of the consensus framework for image processing proposed in two previous works: González-Jaime et al (2013), where a general non-stationary Gaussian model where assumed; and in González-Jaime et al (2014), where the uncertainty to deal with is the model of the noise that corrupts the image. The former approach deals with non-stationary noise in a similar way we do in this paper, while the latter considers stationary noise.…”

Section: Introductionmentioning

confidence: 99%

Spatially-variant noise filtering in magnetic resonance imaging: A consensus-based approach

González-Jaime

Vegas-Sánchez-Ferrero

Kerre

et al. 2016

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

In order to accelerate the acquisition process in multiple-coil Magnetic Resonance scanners, parallel techniques were developed. These techniques reduce the acquisition time via a subsampling of the k-space and a reconstruction process. From a signal and noise perspective, the use of a acceleration techniques modify the structure of the noise within the image. In the most common algorithms, like SENSE, the final magnitude image after the reconstruction is known to follow a Rician distribution for each pixel, just like single coil systems. However, the noise is spatially non-stationary, i.e. the variance of noise becomes x-dependent. This effect can also be found in magnitude images due to other processing inside the scanner. In this work we propose a method to adapt well-known noise filtering techniques initially designed to deal with stationary noise to the case of spatially variant Rician noise. The method copes with inaccurate estimates of variant noise patterns in the image, showing its robustness in realistic cases. The method employs a consensus strategy in conjunction with a set of aggregation functions and a penalty function. Multiple possible outputs are generated for each pixel assuming different unknown input parameters. The consensus approach merges them into a unique filtered image. As a filtering technique, we have selected the Linear Minimum Mean Square Error (LMMSE) estimator for Rician data, which has been used to test our methodology due to its simplicity and robustness. Results with synthetic and in vivo data confirm the good behavior of our approach.

show abstract

Section: Introductionmentioning

confidence: 99%