Signal Dependent Rician Noise Denoising Using Nonlinear Filter

Aarya, Isshaa; Jiang, Danchi; Gale, Timothy J.

doi:10.7763/lnse.2013.v1.74

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…The lysozyme-protected copper nanocluster (Lys-Cu NC) and the tryptophan-capped copper nanocluster (Trp-Cu NC) were synthesized following a previously reported protocol. 20,43 4.4. Effect of Denaturant on the PL Properties of the NC and the Pure Protein Scaffold.…”

Section: Methodsmentioning

confidence: 99%

Temperature-Induced Luminescence Intensity Fluctuation of Protein-Protected Copper Nanoclusters: Role of Scaffold Conformation vs Nonradiative Transition

Sebastian,

Aarya

et al. 2024

ACS Omega

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Protein-scaffolded atomically precise metal nanoclusters (NCs) have emerged as a promising class of biofriendly nanoprobes at the forefront of modern research, particularly in the area of sensing. The photoluminescence (PL) intensity of several nanoclusters showed a systematic temperature-dependent fluctuation, but the mechanism remains ambiguous and is poorly understood. We tried to shed some light on this mechanistic aspect by testing a couple of hypotheses: (i) conformational fluctuation of the protein scaffold-mediated PL intensity fluctuation and (ii) PL intensity fluctuation due to the variation in the radiative and nonradiative transition rates. Herein, the PL intensity of the lysozyme-capped copper nanocluster (Lys-Cu NC) showed excellent temperature dependency; upon increasing the temperature, the PL intensity gradually decreased. However, contrasting effects can be seen when the nanocluster is exposed to a chemical denaturant (guanidine hydrochloride (GdnHCl)); the PL intensity increased with the increase in the GdnHCl concentration due to the change in the ionic strength of the medium. This discrepancy clearly suggests that the thermal PL intensity fluctuation cannot be explained by a change in the scaffold conformation. Furthermore, upon closer investigation, we observed a 2-fold increase in the nonradiative decay rate of the Lys-Cu NC at the elevated temperature, which could reasonably explain the decrease in the PL intensity of the nanocluster at the higher temperature. Additionally, from the result, it was evident that the protein scaffold−metal core interaction played a key role here in stabilizing each other; hence, the scaffold structure remained unaffected even in the presence of chemical denaturants.

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Section: Methodsmentioning

confidence: 99%

Temperature-Induced Luminescence Intensity Fluctuation of Protein-Protected Copper Nanoclusters: Role of Scaffold Conformation vs Nonradiative Transition

Sebastian,

Aarya

et al. 2024

ACS Omega

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“…In the case of linear filters, the noise is reduced by changing the image element value of weighted-mean neighborhood pixels and it creates a poor quality of image. 4 The nonlinear filter is processed within the filter windows. The neighboring pixels are organized along the basis of sample attributes of a windowpane.…”

Section: Introductionmentioning

confidence: 99%

Removal of noise in MRI images using a block difference‐based filtering approach

Nagarajan

Priya

2019

Int J Imaging Syst Tech

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Magnetic resonance imaging (MRI) images are frequently sensitive to certain types of noises and artifacts. The denoising of MRI images is essential for improving visual quality and reliability of the quantitative analysis of diagnosis and treatment. In this article, a new block difference‐based filtering method is proposed to denoise the MRI images. First, the normal MRI image is degraded by a certain percentage of noise. The block difference between the intensity of the normal and noisy MRI is computed, and then it is compared with the intensity of the blocks of the normal MRI image. Based on the comparison, the pixel weights are updated to each block of the denoised MRI image. Observational results are brought out on the BrainWeb and BraTS datasets and evaluated by performance metrics such as peak signal‐to‐noise ratio, structural similarity index measures, universal quality index, and root mean square error. The proposed method outperforms the existing denoising filtering techniques.

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“…Many denoising filters for Rician noise removal have been reported in the literature. Probability density function (PDF) of Rician noise [20] In the image regions where the signal is present and SNR ≥ 3, the noise distribution approximates a Gaussian distribution. Thus, the problem of Rician noise in the brain MRI is often simplified in practice by assuming the Gaussian distribution for the noise, [21].…”

Section: Mri Image Denoisingmentioning

confidence: 99%

Automatic Roi Extraction in Noisy Medical Images

Renukalatha¹,

Kalyanasundaram²

2017

IJIVP

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Accurate segmentation of medical images is pivotal in medical image analysis as it favors the detection and quantification of abnormalities present in human anatomical structures. Since medical images are complex and sometimes noisy, effective extraction of the regions of abnormalities is a tedious process. Many semi-automatic segmentation algorithms with appreciable segmentation accuracy do exist in literature. However, these techniques are iterative, computationally expensive, involve human intervention demanding initial parameter settings and moreover, each one of them is specific to a particular modality. In addition, presence of noise further degrades the quality of the processed image. There is no general algorithm to extract the key regions from all types of noisy medical images. This paper proposes an automatic Region of Interest (ROI) extraction algorithm to detect the important regions in noisy medical images of different modalities using statistical moments. The proposed approach estimates an optimal threshold value automatically using statistical moments through histogram decomposition technique. Initially, the medical image database is preprocessed followed by ROI extraction and the performance of the proposed approach is compared with other techniques to verify its robustness.

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Signal Dependent Rician Noise Denoising Using Nonlinear Filter

Cited by 8 publications

References 13 publications

Temperature-Induced Luminescence Intensity Fluctuation of Protein-Protected Copper Nanoclusters: Role of Scaffold Conformation vs Nonradiative Transition

Temperature-Induced Luminescence Intensity Fluctuation of Protein-Protected Copper Nanoclusters: Role of Scaffold Conformation vs Nonradiative Transition

Removal of noise in MRI images using a block difference‐based filtering approach

Automatic Roi Extraction in Noisy Medical Images

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