Real‐time removal of impulse noise from MR images for radiosurgery applications

HosseinKhani, Zohreh; Hajabdollahi, Mohsen; Karimi, Nader; Najarian, Kayvan; Emami, Ali; Shirani, Shahram; Samavi, Shadrokh; Soroushmehr, S. M. Reza

doi:10.1002/cta.2591

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…In the context of Pap smear images, the presence of noise can significantly impact the accuracy and reliability of differentiating between nuclear and non-nuclear features, leading to potential misclassifications and decreased performance in cytoplasm and nucleus segmentation. Examples of different types of noise are shown in Figure 3 [17][18][19][20]. Biomedical images commonly exhibit different types of noise, including Poisson noise, speckle noise, Gaussian noise, and impulse noise.…”

Section: Noise In Pap Smear Imagesmentioning

confidence: 99%

A Robust Deep Learning Approach for Accurate Segmentation of Cytoplasm and Nucleus in Noisy Pap Smear Images

Nazir,

Sarwar,

Saini

et al. 2023

Computation

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Cervical cancer poses a significant global health burden, affecting women worldwide. Timely and accurate detection is crucial for effective treatment and improved patient outcomes. The Pap smear test has long been a standard cytology screening method, enabling early cancer diagnosis. However, to enhance quantitative analysis and refine diagnostic capabilities, precise segmentation of the cervical cytoplasm and nucleus using deep learning techniques holds immense promise. This research focuses on addressing the primary challenge of achieving accurate segmentation in the presence of noisy data commonly encountered in Pap smear images. Poisson noise, a prevalent type of noise, corrupts these images, impairing the precise delineation of the cytoplasm and nucleus. Consequently, segmentation boundaries become indistinct, leading to compromised overall accuracy. To overcome these limitations, the utilization of U-Net, a deep learning architecture specifically designed for automatic segmentation, has been proposed. This approach aims to mitigate the adverse effects of Poisson noise on the digitized Pap smear slides. The evaluation of the proposed methodology involved a dataset of 110 Pap smear slides. The experimental results demonstrate that the proposed approach successfully achieves precise segmentation of the nucleus and cytoplasm in noise-free images. By preserving the boundaries of both cellular components, the method facilitates accurate feature extraction, thus contributing to improved diagnostic capabilities. Comparative analysis between noisy and noise-free images reveals the superiority of the presented approach in terms of segmentation accuracy, as measured by various metrics, including the Dice coefficient, specificity, sensitivity, and intersection over union (IoU). The findings of this study underline the potential of deep-learning-based segmentation techniques to enhance cervical cancer diagnosis and pave the way for improved quantitative analysis in this critical field of women’s health.

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Section: Noise In Pap Smear Imagesmentioning

confidence: 99%

A Robust Deep Learning Approach for Accurate Segmentation of Cytoplasm and Nucleus in Noisy Pap Smear Images

Nazir,

Sarwar,

Saini

et al. 2023

Computation

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“…According to the type of filter, it can be divided into linear filters and nonlinear filters. Images generally have such a property: the pixel value at any position in an image is closely related to the pixel value in the neighborhood where the pixel is located, such as spatial correlation or similar pixel values [ 18 ].…”

Section: Related Researchmentioning

confidence: 99%

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

Yao

2020

Computational and Mathematical Methods in Medicine

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In order to improve the resolution of magnetic resonance (MR) image and reduce the interference of noise, a multifeature extraction denoising algorithm based on a deep residual network is proposed. First, the feature extraction layer is constructed by combining three different sizes of convolution kernels, which are used to obtain multiple shallow features for fusion and increase the network’s multiscale perception ability. Then, it combines batch normalization and residual learning technology to accelerate and optimize the deep network, while solving the problem of internal covariate transfer in deep learning. Finally, the joint loss function is defined by combining the perceptual loss and the traditional mean square error loss. When the network is trained, it can not only be compared at the pixel level but also be learned at a higher level of semantic features to generate a clearer target image. Based on the MATLAB simulation platform, the TCGA-GBM and CH-GBM datasets are used to experimentally demonstrate the proposed algorithm. The results show that when the image size is set to 190 × 215 and the optimization algorithm is Adam, the performance of the proposed algorithm is the best, and its denoising effect is significantly better than other comparison algorithms. Especially under high-intensity noise levels, the denoising advantage is more prominent.

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Perceptually Motivated Generative Model for Magnetic Resonance Image Denoising

Aetesam

Maji

2022

J Digit Imaging

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Real‐time removal of impulse noise from MR images for radiosurgery applications

Cited by 4 publications

References 36 publications

A Robust Deep Learning Approach for Accurate Segmentation of Cytoplasm and Nucleus in Noisy Pap Smear Images

A Robust Deep Learning Approach for Accurate Segmentation of Cytoplasm and Nucleus in Noisy Pap Smear Images

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

Perceptually Motivated Generative Model for Magnetic Resonance Image Denoising

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