Speckle Noise Reduction in Ultrasound Images using Denoising Auto-encoder with Skip Connection

• URL: http://arxiv.org/abs/2403.02750v1
• Date: Tue, 5 Mar 2024 08:08:59 GMT
• Title: Speckle Noise Reduction in Ultrasound Images using Denoising Auto-encoder with Skip Connection
• Authors: Suraj Bhute, Subhamoy Mandal, Debashree Guha
• Abstract summary: Ultrasound images often contain speckle noise which can lower their resolution and contrast-to-noise ratio. This can make it more difficult to extract, recognize, and analyze features in the images. Researchers have proposed several speckle reduction methods, but no single method takes all relevant factors into account.
• Score: 0.19116784879310028
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Ultrasound is a widely used medical tool for non-invasive diagnosis, but its images often contain speckle noise which can lower their resolution and contrast-to-noise ratio. This can make it more difficult to extract, recognize, and analyze features in the images, as well as impair the accuracy of computer-assisted diagnostic techniques and the ability of doctors to interpret the images. Reducing speckle noise, therefore, is a crucial step in the preprocessing of ultrasound images. Researchers have proposed several speckle reduction methods, but no single method takes all relevant factors into account. In this paper, we compare seven such methods: Median, Gaussian, Bilateral, Average, Weiner, Anisotropic and Denoising auto-encoder without and with skip connections in terms of their ability to preserve features and edges while effectively reducing noise. In an experimental study, a convolutional noise-removing auto-encoder with skip connection, a deep learning method, was used to improve ultrasound images of breast cancer. This method involved adding speckle noise at various levels. The results of the deep learning method were compared to those of traditional image enhancement methods, and it was found that the proposed method was more effective. To assess the performance of these algorithms, we use three established evaluation metrics and present both filtered images and statistical data.

Related papers

• Neighboring Slice Noise2Noise: Self-Supervised Medical Image Denoising from Single Noisy Image Volume [12.077993066353294]
  We propose a novel self-supervised medical image denoising method, Neighboring Slice Noise2Noise (NS-N2N) NS-N2N only requires a single noisy image volume obtained from one medical imaging procedure to achieve high-quality denoising of the image volume itself.
  arXiv  Detail & Related papers  (2024-11-16T16:24:28Z)
• Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference [0.4452997112759916]
  Block-Matching Steered-Mixture of Experts with Multi-Model Inference and Autoencoder (BM-SMoE-AE) This study introduces a novel denoising algorithm, Block-Matching Steered-Mixture of Experts with Multi-Model Inference and Autoencoder (BM-SMoE-AE)
  arXiv  Detail & Related papers  (2024-02-20T06:00:42Z)
• Deep Ultrasound Denoising Using Diffusion Probabilistic Models [5.828784149537374]
  Previous denoising methods often remove the speckles, which could be informative for radiologists and also for quantitative ultrasound. Herein, a method based on the recent Denoising Diffusion Probabilistic Models (DDPM) is proposed. It iteratively enhances the image quality by eliminating the noise while preserving the speckle texture.
  arXiv  Detail & Related papers  (2023-06-12T21:53:32Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Speckle2Speckle: Unsupervised Learning of Ultrasound Speckle Filtering Without Clean Data [48.81200490360736]
  In ultrasound imaging the appearance of homogeneous regions of tissue is subject to speckle. Most conventional filtering techniques are fairly hand-crafted and often need to be finely tuned to the present hardware, imaging scheme and application. We propose a deep-learning based method for speckle removal without these limitations.
  arXiv  Detail & Related papers  (2022-07-31T09:17:32Z)
• Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training [73.60883490436956]
  We propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification. We employ the self-ensemble model with a noisy label filter to efficiently select the clean and noisy samples. We also design a novel global and local representation learning scheme to implicitly regularize the networks to utilize noisy samples.
  arXiv  Detail & Related papers  (2022-05-10T07:50:08Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• De-Noising of Photoacoustic Microscopy Images by Deep Learning [0.9786690381850356]
  Photoacoustic microscopy (PAM) imaging suffers from noise due to the maximum permissible exposure of laser intensity, attenuation of ultrasound in the tissue, and the inherent noise of the transducer. We propose a deep learning-based method to remove complex noise from PAM images without mathematical priors and manual selection of settings for different input images.
  arXiv  Detail & Related papers  (2022-01-12T05:13:57Z)
• Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation [72.0276067144762]
  We discuss and define the two common types of label noise in medical images. We propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task.
  arXiv  Detail & Related papers  (2021-02-28T14:56:45Z)
• A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images [0.0]
  We define a universal deep learning framework for real-time denoising of ultrasound images. We analyse and compare state-of-the-art methods for the smoothing of ultrasound images. Then, we propose a tuned version of the selected state-of-the-art denoising methods.
  arXiv  Detail & Related papers  (2021-01-22T14:18:47Z)
• Improving Blind Spot Denoising for Microscopy [73.94017852757413]
  We present a novel way to improve the quality of self-supervised denoising. We assume the clean image to be the result of a convolution with a point spread function (PSF) and explicitly include this operation at the end of our neural network.
  arXiv  Detail & Related papers  (2020-08-19T13:06:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.