A novel diffusivity function-based image denoising for MRI medical images

Kollem, Sreedhar; Reddy, Katta Rama Linga; Rao, D. Srinivasa

doi:10.1007/s11042-023-14457-3

Cited by 15 publications

(6 citation statements)

References 29 publications

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“…Compared to Moreno López et al [10], who used unsupervised machine learning to denoise with a standard deviation σ � 50, our method also achieves a higher SSIM value. Comparing with the study by Kollem et al [14], it is evident that our method ofers better accuracy and is a promising choice for denoising in images, even though the specifc noise type is not explicitly defned in their study [15].…”

Section: Calculation Time Using Our Proposed Model Inmentioning

confidence: 58%

“…Te most recent study by Kollem et al [14] introduced a novel method using the difusivity function to process noise for medical MRI images. Te image data in this study are medical MRI images and are assumed to be originally noise-free images but with added Poisson noise.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the information from the research works mentioned above, our study has several important strengths. First, compared to previous studies such as Chang et al [8], Tripathi et al [9], Wang et al [12], and Kollem et al [14], our method employs BM3D in combination with a proposed model designed to handle high-level Gaussian noise up to 50%. Tis increases the accuracy of the denoising process and achieves a relatively high SSIM value of 0.998.…”

Section: Calculation Time Using Our Proposed Model Inmentioning

confidence: 99%

See 2 more Smart Citations

Denoising Method for MRI Images Using Modified BM3D Filter with Complex Network and Artificial Neural Networks

Han,

Nguyen Van,

Nguyen Huu

2024

Journal of Electrical and Computer Engineering

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Noise is an undesirable and disturbing effect that degrades the quality of an image. The importance of noise reduction in images and its wide-ranging applications are essential. Most popular image noise filters rely on static parameters that are often challenging to fine-tune. Dynamically adapting these static parameters for image noise filters is a critical area of research. In this study, a combination model between the features of complex networks and artificial neural networks is proposed to automatically find the noise reduction parameter of the block-matching and 3D filtering method. Experimental results on the black and white MRI image set have shown that the model correctly predicted the parameters of the BM3D filter and removed the noise in the images of those MRI images. The model gave high denoising results with PSNR of 51.94 and SSIM of 0.998.

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Section: Calculation Time Using Our Proposed Model Inmentioning

confidence: 58%

Section: Related Workmentioning

confidence: 99%

Section: Calculation Time Using Our Proposed Model Inmentioning

confidence: 99%

See 1 more Smart Citation

Denoising Method for MRI Images Using Modified BM3D Filter with Complex Network and Artificial Neural Networks

Han,

Nguyen Van,

Nguyen Huu

2024

Journal of Electrical and Computer Engineering

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“…The first class consists of mathematical measures such as the widely used mean squared error (MSE) [1], peak signal to noise ratio (PSNR) [2], root mean squared error (RMSE) [3], mean absolute error (MAE) [4], and signal-to-noise ratio (SNR) [5], which have been used because of their simplicity in calculation and having low computational complexity [2].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Generative Adversarial Networks Using a Category of Fuzzy-Based Structural Similarity Indices for Constructing Datasets in Meteorology

Farhadinia,

Ahangari,

Heydari

2024

Mathematics

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Machine learning and image processing are closely related fields that have undergone major development and application in recent years. Machine learning algorithms are being used to develop sophisticated techniques for analyzing and interpreting images, such as object detection, image classification, and image segmentation. One important aspect of image processing is the ability to compare and measure the similarity between different images by providing a way to quantify the similarity between images using various features such as contrast, luminance, and structure. Generally, the flexibility of similarity measures enables fine-tuning the comparison process to achieve the desired outcomes. This is while the existing similarity measures are not flexible enough to address diverse and comprehensive practical aspects. To this end, we utilize triangular norms (t-norms) to construct an inclusive class of similarity measures in this article. As is well-known, each t-norm possesses distinctive attributes that allow for novel interpretations of image similarities. The proposed class of t-norm-based structural similarity measures offers numerous options for decisionmakers to consider various issues and interpret results more broadly in line with their objectives. For more details, in the Experiments section, the proposed method is applied to grayscale and binarized images and a specific experiment related to meteorology. Eventually, the presented diverse case studies confirm the efficiency and key features of the t-norm-based structural similarity.

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“…The Artificial Intelligence (AI) area has been a revolutionary way for classification, regression, anomaly detection, and other tasks in image databases. In this context, image denoising plays a vital space in the image processing field, with important goals especially in the medical areas [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Image Denoising Methods: an Application in Fetal MRI

Negreiros,

Giraldi,

Werner

et al. 2023

Anais Do XVIII Workshop De Visão Computacional (WVC 2023)

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The process of image denoising in magnetic resonance imaging (MRI) is more and more common and important in the medical area. However, it is usual that state-of-the-art deep learning methods require pair images (clean and noisy ones) to train the models which poses limitations in practice. In this sense, this work applied two recent techniques that do not need a clean image to train the models and reached good results for denoising tasks. We applied the NOISE2NOISE (N2N) and the NOISE2VOID (N2V) learning approaches and compared the results for denoising tasks using a fetal MRI dataset. The results showed that the N2N method outperformed the N2V one, considering the Peak Signal-to-Noise Ratio (PSNR), Root Mean Squared Error (RMSE) evaluation metrics, and visual analysis.

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A novel diffusivity function-based image denoising for MRI medical images

Cited by 15 publications

References 29 publications

Denoising Method for MRI Images Using Modified BM3D Filter with Complex Network and Artificial Neural Networks

Denoising Method for MRI Images Using Modified BM3D Filter with Complex Network and Artificial Neural Networks

Utilizing Generative Adversarial Networks Using a Category of Fuzzy-Based Structural Similarity Indices for Constructing Datasets in Meteorology

Self-Supervised Image Denoising Methods: an Application in Fetal MRI

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