基于改进CNN的弱边缘超声图像分割方法
Segmentation Method for Weak Edge Ultrasound Images Based on Improved CNN
朱彦华1
作者信息
- 1. 广东药科大学附属第一医院设备科,广州 510090
- 折叠
摘要
为解决弱边缘超声图像分割难度大的问题,提出基于改进CNN(Convolutional Neural Networks)的弱边缘超声图像分割方法.该方法首先利用平稳小波变换去除图像中的噪声,并通过加权最小二乘滤波器强化图像边缘细节,然后将改进卷积注意力模块添加到残差网络模型中提取图像特征,最后通过优化损失函数提高图像的分割精度.实验结果表明,所提方法对超声图像的弱边缘细节处理效果好,可提高对医学超声图像的分割精度.
Abstract
To solve the problem of difficulty in segmentation of weak edge ultrasound images,an improved CNN(Convolutional Neural Networks)based weak edge ultrasound image segmentation method is proposed.The method first uses stationary wavelet transform to remove the noise in the image,and then uses weighted least square filter to enhance the image edge details.Then,an improved convolutional attention module is added to the residual network model to extract image features.Finally,the image segmentation accuracy is improved by optimizing the loss function.The experimental results show that the proposed method has good performance in processing weak edge details of ultrasound images and can improve the segmentation accuracy of medical ultrasound images.
关键词
超声图像分割/图像预处理/卷积神经网络/平稳小波变换/加权最小二乘滤波器Key words
ultrasound image segmentation/image preprocessing/convolutional neural network(CNN)/stationary wavelet transform/weighted least squares filter引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据