Generalized total variation-based MRI Rician denoising model with spatially adaptive regularization parameters

Liu, Ryan Wen; Shi, Lin; Huang, Wenhua; Xu, Jing; Yu, Simon C.H.; Wang, Defeng

doi:10.1016/j.mri.2014.03.004

Cited by 113 publications

(75 citation statements)

References 59 publications

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“…The denoising of the original image was implemented by the TGV method [43], which is a powerful image pre-processing tool that has been extensively used in image processing community [44][45][46]. The TGV regularisation has the capability of representing image characteristics up to an arbitrary order of differentiation (piecewise constant, piecewise affine, piecewise quadratic etc.).…”

Section: Image Analysis Algorithmmentioning

confidence: 99%

<strong>A portable image-based cytometer for rapid malaria detection and quantification</strong>

Yang

Subramanian

Duan

et al. 2017

Proceedings of the 7th International Multidisciplinary Conference on Optofluidics 2017

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Increasing resistance by malaria parasites to currently used antimalarials across the developing world warrants timely detection and classification so that appropriate drug combinations can be administered before clinical complications arise. However, this is often challenged by low levels of infection (referred to as parasitemia) and presence of predominantly young parasitic forms in the patients' peripheral blood. Herein, we developed a simple, inexpensive and portable image-based cytometer that detects and numerically counts Plasmodium falciparum infected red blood cells (iRBCs) from Giemsa-stained smears derived from infected blood. Our cytometer is able to classify all parasitic subpopulations by quantifying the area occupied by the parasites within iRBCs, with high specificity, sensitivity and negligible false positives (~0.0025%). Moreover, we demonstrate the application of our image-based cytometer in testing anti-malarial efficacy against a commercial flow cytometer and demonstrate comparable results between the two methods. Collectively, these results highlight the possibility to use our image-based cytometer as a cheap, rapid and accurate alternative for antimalarial testing without compromising on efficiency and minimal processing time. With appropriate filters applied into the algorithm, to rule out leukocytes and reticulocytes, our cytometer may also be used for field diagnosis of malaria.

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Section: Image Analysis Algorithmmentioning

confidence: 99%

<strong>A portable image-based cytometer for rapid malaria detection and quantification</strong>

Yang

Subramanian

Duan

et al. 2017

Proceedings of the 7th International Multidisciplinary Conference on Optofluidics 2017

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“…Compared with these models, TV is much more efficient to be solved, making TV-based methods remain active in image and vision studies [17][18][19][20][21][22][23][24]. Moreover, TV may be complementary with the other models, and thus proper combination of them can lead to better performance [25,26].…”

Section: Introductionmentioning

confidence: 99%

A Coordinate Descent Method for Total Variation Minimization

Deng

Ren

Xiao³

et al. 2017

Mathematical Problems in Engineering

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Total variation (TV) is a well-known image model with extensive applications in various images and vision tasks, for example, denoising, deblurring, superresolution, inpainting, and compressed sensing. In this paper, we systematically study the coordinate descent (CoD) method for solving general total variation (TV) minimization problems. Based on multidirectional gradients representation, the proposed CoD method provides a unified solution for both anisotropic and isotropic TV-based denoising (CoDenoise). With sequential sweeping and small random perturbations, CoDenoise is efficient in denoising and empirically converges to optimal solution. Moreover, CoDenoise also delivers new perspective on understanding recursive weighted median filtering. By incorporating with the Augmented Lagrangian Method (ALM), CoD was further extended to TV-based image deblurring (ALMCD). The results on denoising and deblurring validate the efficiency and effectiveness of the CoD-based methods.

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“…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

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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.

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Generalized total variation-based MRI Rician denoising model with spatially adaptive regularization parameters

Cited by 113 publications

References 59 publications

<strong>A portable image-based cytometer for rapid malaria detection and quantification</strong>

<strong>A portable image-based cytometer for rapid malaria detection and quantification</strong>

A Coordinate Descent Method for Total Variation Minimization

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

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