A review on noise reduction methods for brain MRI images

Vaishali, S.; Rao, K. Kishan; Rao, G. Appa

doi:10.1109/spaces.2015.7058284

Cited by 38 publications

(9 citation statements)

References 24 publications

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“…What is meant by SNR is the ratio between the magnitude of the signal amplitude with the amplitude of noise [8] [26] This SNR is also strongly related to the amount of X-ray energy used per pixel in the image. If one attempts to reduce the pixel size (causing an increase in Spatial Resolution) and the dose level is kept the same, the number of X-rays per pixel is reduced.37 To calculate the SNR the Region of Interest (ROI) value is determined using the following equation:…”

Section: B Signal To Noise Ratio (Snr)mentioning

confidence: 99%

Improving Image Quality Using Filtering Weighted Median Filter Techniques on Magnetic Resonance Imaging (MRI) Breast Images

Annisa¹,

Latifah²,

Rusyadi³

2021

Proceedings of the International Conference on Health and Medical Sciences (AHMS 2020)

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Acquisition of MRI images takes a long time so that it can cause movements that create motion images in the image. In addition to the acquisition of MRI itself can produce noise, movement of the image can reduce the quality of the image. The image quality can be improved by using denoising post processing methods. One denoising method that can improve image quality is Weighted Median Filter (WMF). Data was taken from MRI Breast patient images and then given the application of WMF filtering technique. Denoising post-image acquisition becomes a cheaper and effective alternative. This research is expected to be able to improve the image quality on MRI Breast axial T2WI sequences with noise reduction.

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Section: B Signal To Noise Ratio (Snr)mentioning

confidence: 99%

Improving Image Quality Using Filtering Weighted Median Filter Techniques on Magnetic Resonance Imaging (MRI) Breast Images

Annisa¹,

Latifah²,

Rusyadi³

2021

Proceedings of the International Conference on Health and Medical Sciences (AHMS 2020)

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show abstract

“…For the detection of tumours, Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) are used. Corresponding medical equipment of these types brings a noticeable fraction of noise in the obtained images [34]. Therefore, noise suppression [35], [36] is a necessary step to improve the accuracy of image analysis.…”

Section: Tumor Detectionmentioning

confidence: 99%

Optimized Edge Detection Technique for Brain Tumor Detection in MR Images

2020

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Genetic algorithms (GAs) are intended to look for the optimum solution by eliminating the gene strings with the worst fitness. Hence, this paper proposes an optimized edge detection technique based on a genetic algorithm. A training dataset that consists of simple images and their corresponding optimal edge features is employed to obtain the optimum filter coefficients along with the optimum thresholding algorithm. Qualitative and quantitative performance analyses are investigated based on several well-known metrics. The performance of the proposed genetic algorithm-based cost minimization technique is compared to the classical edge detection techniques, fractional-order edge detection filters, and threshold-optimized fractional-order filters. As an application for the proposed algorithm, a strategy to detect the edges of the brain tumour from a patient's MR scan image of the brain is proposed. First, Balance Contrast Enhancement Technique (BCET) is applied to improve the image features to provide better characteristics of medical images. Then the proposed GA edge detection method is employed, with the appropriate training dataset, to detect the fine edges. A comparative analysis is performed on the number of MR scan images as well. The study indicates that the proposed GA edge detection method performs well compared to both classical and fractional-order edge detection methods.

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“…In the brain network, considering all the correlations may lead to the inclusion of noisy and spurious connections. The presence of noise in brain images is owing to the fact that measurement errors are likely to arise due to technological limitations, operator performance, equipment, environment, and other factors (Vaishali et al, 2015). Currently, Pearson's Correlation Coefficient (PCC) is the simplest and most widely-used method in constructing functional brain networks.…”

Section: Introductionmentioning

confidence: 99%

TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis

Jiang

Wen

et al. 2021

Neuroinform

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Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Graph convolutional networks (GCNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GCNs model for brain networks faces several challenges, including high dimensional and noisy correlation in the brain networks, limited labeled training data, and depth limitation of GCN learning. Generalization and interpretability are important in developing predictive models for clinical diagnosis. To address these challenges, we proposed an ensemble framework involving hierarchical GCN and transfer learning for sparse brain networks, which allows GCN to capture the intrinsic correlation among the subjects and domains, to improve the network embedding learning for disease diagnosis. Extensive experiments on two real medical clinical applications: diagnosis of Autism spectrum disorder (ASD) and diagnosis of Alzheimer's disease (AD) on both the ADNI and ABIDE databases, showing the effectiveness of the proposed framework. We achieved state-of-the-art accuracy and AUC for AD/ MCI and ASD/NC (Normal control) classification in comparison with studies that used functional connectivity as features or GCN models. The proposed TE-HI-GCN model achieves the best classification performance, leading to about 27.93% (31.38%) improvement for ASD and 16.86% (44.50%) for AD in terms of accuracy and AUC compared with the traditional GCN model. Moreover, the obtained clustering results show high correspondence with the previous neuroimaging derived evidence of within and between-networks biomarkers for ASD. The discovered subnetworks are used as evidence for the proposed TE-HI-GCN model. Furthermore, this work is the first attempt of transfer learning on the two related disorder domains to uncover the correlation among the two diseases with a transfer learning scheme.

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A review on noise reduction methods for brain MRI images

Cited by 38 publications

References 24 publications

Improving Image Quality Using Filtering Weighted Median Filter Techniques on Magnetic Resonance Imaging (MRI) Breast Images

Improving Image Quality Using Filtering Weighted Median Filter Techniques on Magnetic Resonance Imaging (MRI) Breast Images

Optimized Edge Detection Technique for Brain Tumor Detection in MR Images

TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis

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