吉林大学学报（信息科学版）2024，Vol.42Issue(6)：1018-1024,7.
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
摘要
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分类
信息技术与安全科学引用本文复制引用
朱彦华..基于改进CNN的弱边缘超声图像分割方法[J].吉林大学学报（信息科学版）,2024,42(6):1018-1024,7.基金项目
广东省经济与信息化委员会、广东省财政厅共同编制的广东省工业和信息化专项资金"互联网+"应用基金资助项目(粤经信电软函[2017]74号) （粤经信电软函[2017]74号）
