摘要
针对传统医学超声图像去斑方法的不足,该文提出一种自适应多曝光融合框架和前馈卷积神经网络模型图像去斑方法。首先,制作超声图像训练数据集;然后,提出一种自适应增强因子的多曝光融合框架,增强图像进行有效特征提取;最后,通过网络训练去斑模型并获得去斑后的图像。实验结果表明,该文较已有的方法,能更有效地滤除医学超声图像中的斑点噪声并更多的保留图像细节。
Considering the shortage of traditional medical ultrasound image despeckle methods, an adaptive multi-exposure fusion framework and feedforward convolutional neural network model image despeckle method is proposed. Firstly, an ultrasound image training data set is produced. Then, a multi-exposure fusion framework with adaptive enhancement factors is proposed to enhance the image for effective feature extraction.Finally, a speckle model is trained through the network and a speckle image is obtained.Experimental results show that, compared with the existing methods, this paper can more effectively remove speckle noise in medical ultrasound images and retain more image details.
作者
付晓薇
杨雪飞
陈芳
李曦
FU Xiaowei;YANG Xuefei;CHEN Fang;LI Xi(College of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China;Key Laboratory of Intelligent Information Processing and Real-time Industrial System,Wuhan 430065,China;State Key Laboratory of Materials Processing and Die&Mould Technology,Huazhong University of Science and Technology,Wuhan 430074,China;Department of Ultrasound and Imaging,Wuhan University of Science and Technology Hospital,Wuhan 430065,China;School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2020年第7期1782-1789,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61873323)
材料成形与模具技术国家重点实验室开放课题研究基金(P2018-016)
湖北省自然科学基金(2017CFB506)
智能信息处理与实时工业系统湖北省重点实验室开放课题项目(2016znss02A,znxx2018ZD01)
大学生科技创新基金项目(18ZRA076)。
关键词
超声图像
深度学习
多曝光融合框架
乘性斑点噪声
Ultrasound image
Deep learning
Multi-exposure fusion framework
Multiplicative speckle noise
