Nonlocal transform-domain denoising of volumetric data with groupwise adaptive variance estimation

Maggioni, Matteo; Foi, Alessandro

doi:10.1117/12.912109

Cited by 36 publications

(20 citation statements)

References 14 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To highlight this point we have compared the proposed method with a related recently proposed local noise estimation method (Maggioni and Foi, 2012). This method, based on the Discrete Cosine Transform (DCT), uses high frequency components of a local set of patches to locally estimate the noise level.…”

Section: Spatially Varying Gaussian Noisementioning

confidence: 99%

MRI noise estimation and denoising using non-local PCA

Manjón

Coupé²,

Buades³

2015

Medical Image Analysis

151

122

View full text Add to dashboard Cite

This paper proposes a novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images. The proposed method is a two-stage approach that first filters the noisy image using a non local PCA thresholding strategy by automatically estimating the local noise level present in the image and second uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed method internally estimates the amount of local noise presents in the images that enables applying it automatically to images with spatially varying noise levels and also corrects the Rician noise induced bias locally. The proposed approach has been compared with related state-of-the-art methods showing competitive results in all the studied cases.3

show abstract

Section: Spatially Varying Gaussian Noisementioning

confidence: 99%

MRI noise estimation and denoising using non-local PCA

Manjón

Coupé²,

Buades³

2015

Medical Image Analysis

151

122

View full text Add to dashboard Cite

show abstract

“…However noise introduced by OCT propagates through the downscaled I, which may prevent the initial evolution from evolving to fill the hole cavity. Therefore we denoise the I for the smallest scale s 1 using a Wiener filter by [28], [29], which is effective at removing the speckle noise encountered in OCT imaging [30], and the result is shown in Fig. 4a.…”

Section: D Multi-scale Lgdfmentioning

confidence: 99%

Multi-Scale Segmentation and Surface Fitting for Measuring 3-D Macular Holes

Nasrulloh

Willcocks

Jackson

et al. 2018

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Macular holes are blinding conditions, where a hole develops in the central part of retina, resulting in reduced central vision. The prognosis and treatment options are related to a number of variables, including the macular hole size and shape. High-resolution spectral domain optical coherence tomography allows precise imaging of the macular hole geometry in three dimensions, but the measurement of these by human observers is time-consuming and prone to high inter- and intra-observer variability, being characteristically measured in 2-D rather than 3-D. We introduce several novel techniques to automatically retrieve accurate 3-D measurements of the macular hole, including: surface area, base area, base diameter, top area, top diameter, height, and minimum diameter. Specifically, we introduce a multi-scale 3-D level set segmentation approach based on a state-of-the-art level set method, and we introduce novel curvature-based cutting and 3-D measurement procedures. The algorithm is fully automatic, and we validate our extracted measurements both qualitatively and quantitatively, where our results show the method to be robust across a variety of scenarios. Our automated processes are considered a significant contribution for clinical applications.

show abstract

“…Recently, some estimation methods have adopted a non-parametric approach to estimate these non-stationary noise maps. These methods do not rely on a specific processing pipeline; the only requirement is that a statistical model has to be adopted for the acquisition noise: Gaussian, (Goossens et al (2006);Pan et al (2012); Aja-Fernández et al (2015); Maggioni and Foi (2012)), Rician (Delakis et al (2007);Liu et al (2014); Aja-Fernández et al (2015); Borrelli et al (2014); Manjón et al (2015)), or nc-χ (Tabelow et al (2015); Pieciak et al (2016)). …”

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

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

Nonlocal transform-domain denoising of volumetric data with groupwise adaptive variance estimation

Cited by 36 publications

References 14 publications

MRI noise estimation and denoising using non-local PCA

MRI noise estimation and denoising using non-local PCA

Multi-Scale Segmentation and Surface Fitting for Measuring 3-D Macular Holes

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

Contact Info

Product

Resources

About