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基于相位对称性的血管超声图像分割算法

关少亚 张诚 孟偲 曹建树 孙凯 王田苗

关少亚,张诚,孟偲,等. 基于相位对称性的血管超声图像分割算法[J]. 北京航空航天大学学报,2023,49(10):2645-2650 doi: 10.13700/j.bh.1001-5965.2021.0696
引用本文: 关少亚,张诚,孟偲,等. 基于相位对称性的血管超声图像分割算法[J]. 北京航空航天大学学报,2023,49(10):2645-2650 doi: 10.13700/j.bh.1001-5965.2021.0696
GUAN S Y,ZHANG C,MENG C,et al. Vascular ultrasound image segmentation algorithm based on phase symmetry[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(10):2645-2650 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0696
Citation: GUAN S Y,ZHANG C,MENG C,et al. Vascular ultrasound image segmentation algorithm based on phase symmetry[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(10):2645-2650 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0696

基于相位对称性的血管超声图像分割算法

doi: 10.13700/j.bh.1001-5965.2021.0696
基金项目: 国家自然科学基金(61873010,61533016);中国科协中非“一带一路”国际工程科技与教育培训合作(2021ZZGJB071529);北京石油化工学院交叉科研探索项目(BIPTCSF-016)
详细信息
    通讯作者:

    E-mail:tsai@buaa.edu.cn

  • 中图分类号: TP242.3;

Vascular ultrasound image segmentation algorithm based on phase symmetry

Funds: National Natural Science Foundation of China (61873010,61533016);China - Africa “Belt and Road” International Engineering, Technology, Education and Training cooperation (2021ZZGJB071529);Cross-Disciplinary Science Foundation from Beijing Institute of Petrochemical Technology (BIPTCSF-016)
More Information
  • 摘要:

    超声成像凭借其经济便携、无辐射、可实时成像等优点,已经成为临床诊断中常用的检查方法之一。血管超声成像不仅可以减少术中射线,也可以实现手术室外血管病变情况的初步判断,满足无法进行造影手术病人的检测需求。超声图像中目标组织边缘特征一般较为明显,综合考虑超声成像的特点及特征分布,选择基于相位对称性的分割算法对降噪滤波后超声图像中的目标组织进行分割,并根据目标组织边缘特性对分割结果进行形态学处理。通过与传统的活动轮廓型及深度学习算法对目标组织边缘的分割效果进行对比及量化分析,验证基于相位对称性的分割算法在目标组织分割及边缘提取中的优势。

     

  • 图 1  超声图像分割流程图

    Figure 1.  Flow chart of ultrasonic image segmentation.

    图 2  原始超声图像

    Figure 2.  Original ultrasonic images

    图 3  血管及骨组织超声图像处理过程及结果

    Figure 3.  Ultrasonic image processing process and results of blood vessel and bone tissue.

    图 4  各算法分割结果量化分析及对比

    Figure 4.  Quantitative analysis and comparison of segmentation results of each algorithm

    表  1  本文算法和专家分割结果对比

    Table  1.   Comparison of segmentation results between proposed algorithm and experts

    算法AccSenSpeDsc
    胸骨桡骨颈动脉 胸骨桡骨颈动脉 胸骨桡骨颈动脉 胸骨桡骨颈动脉
    本文0.930.970.890.860.950.950.950.980.880.860.950.75
    专家0.990.980.980.970.950.970.990.990.990.970.960.97
    下载: 导出CSV

    表  2  各算法平均运行时间

    Table  2.   Comparison of average running time of each algorithm s

    本文算法活动轮廓模型霍夫变换+蛇模型CNN算法
    2.5712.6315.570.50
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-18
  • 录用日期:  2022-01-08
  • 网络出版日期:  2022-02-23
  • 整期出版日期:  2023-10-31

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