Controllable Deep Learning Denoising Model for Ultrasound Images Using Synthetic Noisy Image

EasyChair Preprint 11949

Abstract

Medical ultrasound imaging has gained widespread prevalence in human muscle and internal organ diagnosis. However, defects in the circuitry during image acquisition, operating methods, defects in the image signal transmission process or other objective factors can lead to the occurrence of speckle noise and distortion in ultrasound images. These issues not only make it challenging for doctors to diagnose diseases but can also pose difficulties in image post-processing. While traditional denoising methods are time-consuming, they are also not effective in removing speckle noise while retaining image details, leading to potential misdiagnosis. Therefore, there is a significant need to accurately and quickly denoise medical ultrasound images to enhance image quality. In this paper, we propose a flexible and lightweight deep learning denoising method for ultrasound images. Initially, we utilize a considerable number of natural images to train the convolutional neural network for acquiring a pre-trained denoising model. Next, we employ the plane-wave imaging technique to generate simulated noisy ultrasound images for further transfer learning of the pre-trained model. As a result, we obtain a non-blind, lightweight, fast, and accurate denoiser. Experimental results demonstrate the superiority of our proposed method in terms of denoising speed, flexibility, and effectiveness compared to conventional convolutional neural network denoisers for ultrasound images.

Keyphrases: Noise transfer learning, Non-blind ultrasound image denoising, Plane-wave imaging, lightweight model

@booklet{EasyChair:11949,
  author    = {Mingfu Jiang and Chenzhi You and Mingwei Wang and Heye Zhang and Zhifan Gao and Dawei Wu and Tao Tan},
  title     = {Controllable Deep Learning Denoising Model for Ultrasound Images Using Synthetic Noisy Image},
  howpublished = {EasyChair Preprint 11949},
  year      = {EasyChair, 2024}}