Speckle Noise Reduction in Ultrasound Images using Denoising Auto-encoder with Skip Connection
This addresses the problem of impaired diagnosis and analysis in medical imaging for doctors and computer-assisted systems, but it is incremental as it builds on existing denoising auto-encoders by adding skip connections.
The paper tackled speckle noise reduction in ultrasound images by comparing traditional methods with a denoising auto-encoder with skip connections, finding that the deep learning method was more effective in improving images of breast cancer, as evidenced by better performance on three evaluation metrics.
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.