A Novel Approach for Medical Images Noise Reduction Based RBF Neural Network Filter

Salem, Mohammed; Djemal, Khalifa; Benyettou, M.

doi:10.17706/jcp.10.2.68-80

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Previously, non-medical images, such as images of human faces, vegetables, and birds have been denoised with neural network [ 12 , 21 , 22 ], and multilayer perceptron or convolutional neural network have achieved good performance for image denoising of non-medical images. For medical image denoising, there are several studies to use conventional neural network [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. On the other hand, literature survey of Litjens et al shows that, in deep learning with convolutional neural network or CAE, number of applications of image enhancement like image denoising is limited [32] .…”

Section: Discussionmentioning

confidence: 99%

Convolutional auto-encoder for image denoising of ultra-low-dose CT

Nishio

Nagashima

Hirabayashi

et al. 2017

Heliyon

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ObjectivesThe purpose of this study was to validate a patch-based image denoising method for ultra-low-dose CT images. Neural network with convolutional auto-encoder and pairs of standard-dose CT and ultra-low-dose CT image patches were used for image denoising. The performance of the proposed method was measured by using a chest phantom.Materials and methodsStandard-dose and ultra-low-dose CT images of the chest phantom were acquired. The tube currents for standard-dose and ultra-low-dose CT were 300 and 10 mA, respectively. Ultra-low-dose CT images were denoised with our proposed method using neural network, large-scale nonlocal mean, and block-matching and 3D filtering. Five radiologists and three technologists assessed the denoised ultra-low-dose CT images visually and recorded their subjective impressions of streak artifacts, noise other than streak artifacts, visualization of pulmonary vessels, and overall image quality.ResultsFor the streak artifacts, noise other than streak artifacts, and visualization of pulmonary vessels, the results of our proposed method were statistically better than those of block-matching and 3D filtering (p-values < 0.05). On the other hand, the difference in the overall image quality between our proposed method and block-matching and 3D filtering was not statistically significant (p-value = 0.07272). The p-values obtained between our proposed method and large-scale nonlocal mean were all less than 0.05.ConclusionNeural network with convolutional auto-encoder could be trained using pairs of standard-dose and ultra-low-dose CT image patches. According to the visual assessment by radiologists and technologists, the performance of our proposed method was superior to that of large-scale nonlocal mean and block-matching and 3D filtering.

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Section: Discussionmentioning

confidence: 99%

Convolutional auto-encoder for image denoising of ultra-low-dose CT

Nishio

Nagashima

Hirabayashi

et al. 2017

Heliyon

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Radial basis function network for aggregating predictions of k-nearest neighbors local models generated based on independent data sets

Marfo¹,

Przybyła–Kasperek²

2022

Procedia Computer Science

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A Comprehensive Review on Nature Inspired Neural Network based Adaptive Filter for Eliminating Noise in Medical Images

Kumar

Mishra

2020

CMIR

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Background: Various kind of medical imaging modalities are available for providing noninvasive view and for analyzing any pathological symptoms of human beings. Different noise may appear in those modalities at the time of acquisition, transmission, scanning, or at the time of storing. The removal of noises from the digital medical images without losing any inherent features is always considered a challenging task because a successful diagnosis relies on them. Numerous techniques have been proposed to fulfill this objective, and each having their own benefits and limitations. Discussion: In this comprehensive review article, more than 65 research articles are investigated to illustrate the applications of Artificial Neural Networks (ANN) in the field of biomedical image denoising. In particular, the zest of this article is to highlight the hybridized filtering model using nature-inspired algorithms and artificial neural networks for suppression of noise. Various other techniques, such as fixed filter, linear adaptive filters and gradient descent learning based neural network filter are also included. Conclusion: This article envisages how to train ANN using derivative free nature-inspired algorithms, and its performance in various medical images modalities and noise conditions.

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A Novel Approach for Medical Images Noise Reduction Based RBF Neural Network Filter

Cited by 4 publications

References 19 publications

Convolutional auto-encoder for image denoising of ultra-low-dose CT

Convolutional auto-encoder for image denoising of ultra-low-dose CT

Radial basis function network for aggregating predictions of k-nearest neighbors local models generated based on independent data sets

A Comprehensive Review on Nature Inspired Neural Network based Adaptive Filter for Eliminating Noise in Medical Images

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