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复小波域混合概率图模型的超声医学图像分割

夏平 施宇 雷帮军 龚国强 胡蓉 师冬霞

夏平, 施宇, 雷帮军, 龚国强, 胡蓉, 师冬霞.复小波域混合概率图模型的超声医学图像分割.自动化学报, 2021, 47(1): 185-196 doi: 10.16383/j.aas.c180132
引用本文: 夏平, 施宇, 雷帮军, 龚国强, 胡蓉, 师冬霞.复小波域混合概率图模型的超声医学图像分割.自动化学报, 2021, 47(1): 185-196 doi: 10.16383/j.aas.c180132
Xia Ping, Shi Yu, Lei Bang-Jun, Gong Guo-Qiang, Hu Rong, Shi Dong-Xia. Ultrasound medical image segmentation based on hybrid probabilistic graphical model in complex-wavelet domain. Acta Automatica Sinica, 2021, 47(1): 185-196 doi: 10.16383/j.aas.c180132
Citation: Xia Ping, Shi Yu, Lei Bang-Jun, Gong Guo-Qiang, Hu Rong, Shi Dong-Xia. Ultrasound medical image segmentation based on hybrid probabilistic graphical model in complex-wavelet domain. Acta Automatica Sinica, 2021, 47(1): 185-196 doi: 10.16383/j.aas.c180132

复小波域混合概率图模型的超声医学图像分割

doi: 10.16383/j.aas.c180132
基金项目: 

国家重点研发计划 2016YFB0800403

国家自然科学基金(联合基金)项目 U1401252

湖北省重点实验室开放基金项目 2018SDSJ07

详细信息
    作者简介:

    夏平  三峡大学计算机与信息学院教授.主要研究方向为计算机视觉, 智能信息处理, 概率图模型及其应用.E-mail: pxia@ctgu.edu.cn

    施宇  三峡大学计算机与信息学院硕士研究生.主要研究方向为信号与信息处理, 医学图像的目标检测与分割.E-mail: rwxrfs@163.com

    龚国强  三峡大学计算机与信息学院副教授.主要研究方向为无线通信, 数字信号处理. E-mail: gonggq_shh@163.com

    胡蓉  三峡大学计算机与信息学院硕士研究生.主要研究方向为深度学习, 医学图像的目标检测和分割.E-mail: zhrrongfyqs@163.com

    师冬霞  三峡大学计算机与信息学院硕士研究生.主要研究方向为信号与信息处理, 医学图像的目标检测与分割.E-mail: longbinyuankang@163.com

    通讯作者:

    雷帮军  三峡大学计算机与信息学院教授, IEEE高级会员.主要研究方向为图像处理, 三维计算机视觉及智能视频处理.本文通信作者.E-mail: Bangjun.Lei@ieee.org

  • 本文责任编委 张道强

Ultrasound Medical Image Segmentation Based on Hybrid Probabilistic Graphical Model in Complex-wavelet Domain

Funds: 

National Key Research and Development Program of China 2016YFB0800403

National Natural Science Foundation of China (Joint Fund) Project U1401252

Hubei Provincial Key Laboratory Open Fund Project 2018SDSJ07

More Information
    Author Bio:

    XIA Ping   Professor at the School of Computer and Information, China Three Gorges University. His research interest covers computer vision, intelligent information processing, probabilistic graph model and its application

    SHI Yu   Master student at the School of Computer and Information, China Three Gorges University. Her research interest covers signal and information processing, target detection and segmentation in medical images

    GONG Guo-Qiang   Associate professor at the School of Computer and Information, Three Gorges University, His research interest covers wireless communication and digital signal processing

    HU Rong   Master student at the School of Computer and Information, China Three Gorges University. Her research interest covers deep learning, target detection and segmentation of medical images

    SHI Dong-Xia   Master student at the College of Computer and Information Technology, China Three Gorges University. Her research interest covers signal and information processing, target detection and segmentation in medical images

    Corresponding author: LEI Bang-Jun   Professor at the School of Computer and Information, China Three Gorges University, IEEE senior member. His research interest covers image processing, 3-D imaging and multi-dimensional intelligent computer vision. Corresponding author of this paper
  • Recommended by Associate Editor ZHANG Dao-Qiang
  • 摘要: 针对存在大量不规则斑点噪声、目标边缘弱化的超声医学图像分割中较难识别目标的问题, 提出了一种复小波域中混合概率图模型的超声医学图像分割算法.采用具有近似平移不变性和良好方向选择性的双树复小波变换(Dual tree-complex wavelet transform, DT-CWT)提取超声医学图像6个方向的高频特征信息; 其次, 为关联目标的弱特征信息并抑制统计独立的高频噪声, 构建了复小波域混合概率图模型; 尺度间"父—子"节点间标记采用贝叶斯网络进行建模, 尺度内邻域间标记采用马尔科夫随机场(Markov random field, MRF)无向图建模, 对复小波域中同尺度的特征系数采用高斯混合模型建模, 尺度内同标记的观测特征采用高斯模型建模; 最后, 用迭代条件模式(Iterated conditional mode, ICM)实现MRF中误分割率最小的能量函数最优解, 获取标记场, 实现超声医学图像分割.实验结果从视觉效果和定量分析两方面验证表明, 本文算法能有效地提取超声图像的弱目标信息, 较好地定位目标区域, 具有较高的分割精度和鲁棒性.
    Recommended by Associate Editor ZHANG Dao-Qiang
    1)  本文责任编委 张道强
  • 图  1  DT-CWT变换及其子带方向

    Fig.  1  DT-CWT and the sub-band direction

    图  2  DT-CWT域系数向量结构

    Fig.  2  Coefficient vector structure of DT-CWT domain

    图  3  DT-CWT域标记场贝叶斯网络模型

    Fig.  3  Marking field Bayesian network model in DT-CWT domain

    图  4  DT-CWT域标记场的2阶邻域系统

    Fig.  4  The second order neighborhood system in DT-CW

    图  5  医学图像的DT-CWT分解及其高频系统统计

    Fig.  5  Medical image of DT-CWT and Statistical characteristics of the coefficient

    图  6  超声医学图像4种算法分割结果比较

    Fig.  6  Comparison of segmentation results of four algorithms for ultrasonic medical images

    图  7  医学超声图像4种算法定量评价指标比较

    Fig.  7  Comparison of quantitative evaluation indexes of four algorithms for medical ultrasound images

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出版历程
  • 收稿日期:  2018-03-08
  • 录用日期:  2018-10-26
  • 刊出日期:  2021-01-29

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