Li Xiaofeng, Wang Yanwei, Zhao Yuanyuan, Wei Yanbo
Department of Information Engineering, Heilongjiang International University, Harbin, China.
School of Mechanical Engineering, Harbin Institute of Petroleum, Harbin, China.
Front Physiol. 2022 Apr 13;13:880966. doi: 10.3389/fphys.2022.880966. eCollection 2022.
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.
超声医学成像技术的快速发展极大地拓宽了超声的应用范围，其已广泛应用于乳腺疾病的筛查、诊断等方面。然而，乳腺超声图像中存在的过多斑点噪声会大大降低图像分辨率，影响对患者病情的观察和判断。因此，研究图像斑点噪声抑制尤为重要。在本文中，我们提出了一种基于三维（3D）深度学习的乳腺超声图像快速斑点噪声抑制算法。首先，根据乳腺超声图像的灰度值，利用对数变换和指数变换增强输入乳腺超声图像的对比度，采用引导滤波算法增强腺体超声图像的细节，并使用空间高通滤波算法抑制乳腺超声图像的过度锐化，完成乳腺超声图像的预处理，提高图像清晰度；其次，将预处理后的乳腺超声图像输入到3D卷积云神经网络图像斑点噪声抑制模型中；最后，将边缘敏感项引入到3D卷积云神经网络中，在抑制乳腺超声图像斑点噪声的同时保留图像边缘信息。实验表明，在训练的第100次迭代时，均方误差和误识率均降至1.2%以下，3D卷积云神经网络训练良好，超声图像斑点噪声抑制的信噪比大于60dB，峰值信噪比大于65dB，边缘保留指数值超过0.45的实验阈值，斑点噪声抑制时间短，边缘信息保留良好，图像细节清晰可见。斑点噪声抑制时间短，边缘信息保留良好，图像细节清晰可见，可应用于乳腺超声诊断领域。
