Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning

Xiaofeng Li; Yanwei Wang; Yuanyuan Zhao; Yanbo Wei

doi:10.3389/fphys.2022.880966

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning

Front Physiol. 2022 Apr 13:13:880966. doi: 10.3389/fphys.2022.880966. eCollection 2022.

Authors

Xiaofeng Li¹, Yanwei Wang², Yuanyuan Zhao³, Yanbo Wei⁴

Affiliations

¹ Department of Information Engineering, Heilongjiang International University, Harbin, China.
² School of Mechanical Engineering, Harbin Institute of Petroleum, Harbin, China.
³ Heilongjiang Provincial Hospital, Harbin, China.
⁴ School of Automatic Control Engineering, Harbin Institute of Petroleum, Harbin, China.

Abstract

The rapid development of ultrasound medical imaging technology has greatly broadened the scope of application of ultrasound, which has been widely used in the screening, diagnosis of breast diseases and so on. However, the presence of excessive speckle noise in breast ultrasound images can greatly reduce the image resolution and affect the observation and judgment of patients' condition. Therefore, it is particularly important to investigate image speckle noise suppression. In the paper, we propose fast speckle noise suppression algorithm in breast ultrasound image using three-dimensional (3D) deep learning. Firstly, according to the gray value of the breast ultrasound image, the input breast ultrasound image contrast is enhanced using logarithmic and exponential transforms, and guided filter algorithm was used to enhance the details of glandular ultrasound image, and spatial high-pass filtering algorithm was used to suppress the excessive sharpening of breast ultrasound image to complete the pre-processing of breast ultrasound image and improve the image clarity; Secondly, the pre-processed breast ultrasound images were input into the 3D convolutional cloud neural network image speckle noise suppression model; Finally, the edge sensitive terms were introduced into the 3D convolutional cloud neural network to suppress the speckle noise of breast ultrasound images while retaining image edge information. The experiments demonstrate that the mean square error and false recognition rate all reduced to below 1.2% at the 100th iteration of training, and the 3D convolutional cloud neural network is well trained, and the signal-to-noise ratio of ultrasound image speckle noise suppression is greater than 60 dB, the peak signal-to-noise ratio is greater than 65 dB, the edge preservation index value exceeds the experimental threshold of 0.45, the speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible. The speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible, which can be applied to the field of breast ultrasound diagnosis.

Keywords: bootstrap filtering algorithm; breast ultrasound image; convolutional cloud network; image speckle suppression; three-dimensional deep learning.