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结合MRF能量和模糊速度的乳腺癌图像分割方法

冯宝 陈业航 刘壮盛 李智 宋嵘 龙晚生

冯宝, 陈业航, 刘壮盛, 李智, 宋嵘, 龙晚生. 结合MRF能量和模糊速度的乳腺癌图像分割方法. 自动化学报, 2020, 46(6): 1188−1199 doi: 10.16383/j.aas.c180759
引用本文: 冯宝, 陈业航, 刘壮盛, 李智, 宋嵘, 龙晚生. 结合MRF能量和模糊速度的乳腺癌图像分割方法. 自动化学报, 2020, 46(6): 1188−1199 doi: 10.16383/j.aas.c180759
Feng Bao, Chen Ye-Hang, Liu Zhuang-Sheng, Li Zhi, Song Rong, Long Wan-Sheng. Segmentation of breast cancer on DCE-MRI images with MRF energy and fuzzy speed function. Acta Automatica Sinica, 2020, 46(6): 1188−1199 doi: 10.16383/j.aas.c180759
Citation: Feng Bao, Chen Ye-Hang, Liu Zhuang-Sheng, Li Zhi, Song Rong, Long Wan-Sheng. Segmentation of breast cancer on DCE-MRI images with MRF energy and fuzzy speed function. Acta Automatica Sinica, 2020, 46(6): 1188−1199 doi: 10.16383/j.aas.c180759

结合MRF能量和模糊速度的乳腺癌图像分割方法

doi: 10.16383/j.aas.c180759
基金项目: 广西高等学校千名中青年骨干教师培育计划项目基金(2018GXQGFB160)资助
详细信息
    作者简介:

    冯宝:2014年获华南理工大学博士学位. 现为中山大学博士后. 主要研究方向为机器学习, 模式识别及其在生物医学信号处理中的应用. E-mail: fengbao1986.love@163.com

    陈业航:桂林电子科技大学硕士研究生. 主要研究方向为生物医学信号处理与模式识别. E-mail: cyh93yl@163.com

    刘壮盛:广东省江门市中心医院副主任医师. 2010年获中山大学影像医学与核医学硕士学位. 主要研究方向为乳腺及骨骼肌肉系统影像诊断. E-mail: zhuangshengliu@126.com

    李智:2003年获电子科技大学博士学位. 桂林航天工业学院教授. 主要研究方向为智能仪器系统, 现代测试理论与技术. E-mail: cclizhi@guet.edu.cn

    宋嵘:2006年获中国香港理工大学生物医学工程博士学位. 现为中山大学生物医学工程学院教授. 主要研究方向为肌肉骨骼建模, 生物医学信号处理, 人体运动分析和机器人辅助中风康复. E-mail: songrong@mail.sysu.edu.cn

    龙晚生:广东省江门市中心医院主任医师. 1990年获四川大学放射诊断硕士学位. 主要研究方向为腹部影像鉴别诊断. 本文通信作者.E-mail: jmlws2@163.com

Segmentation of Breast Cancer on DCE-MRI Images With MRF Energy and Fuzzy Speed Function

Funds: Supported by Natural Science Foundation of Guangxi Zhuang Autonomous Region (2016GXNSFBA380160)
  • 摘要: 乳腺癌灶的精确分割是乳腺癌计算机辅助诊断的重要前提. 在动态对比增强核磁共振成像(Dynamic contrast-enhanced magnetic resonance imaging, DCE-MRI)的图像中, 乳腺癌灶具有对比度低、边界模糊及亮度不均匀等特点, 传统的活动轮廓模型方法很难取得准确的分割结果. 本文提出一种结合马尔科夫随机场(Markov random field, MRF)能量和模糊速度函数的活动轮廓模型的半自动分割方法来完成乳腺癌灶的分割, 相对于专业医生的手动分割, 本文方法具有速度快、可重复性高和分割结果相对客观等优点. 首先, 计算乳腺DCE-MRI图像的MRF能量, 以增强目标区域与周围背景的差异. 其次, 在能量图中计算每个像素点的后验概率, 建立基于后验概率驱动的活动轮廓模型区域项. 最后, 结合Gabor纹理特征、DCE-MRI时域特征和灰度特征构建模糊速度函数, 将其引入到活动轮廓模型中作为边缘检测项. 在乳腺癌灶边界处, 该速度函数趋向于零, 活动轮廓曲线停止演变, 完成对乳腺癌灶的分割. 实验结果表明, 所提出的方法有助于乳腺癌灶在DCE-MRI图像中的准确分割.
  • 图  1  本文分割算法流程图

    Fig.  1  The flowchart of segmentation algorithm in this paper

    图  2  乳腺DCE-MRI图像的MRF能量和后验概率

    Fig.  2  MRF energy and posterior probability of breast DCE-MRI image

    图  3  一个乳腺图像的纹理分析

    Fig.  3  Texture analysis of a breast image

    图  4  DCE-MRI时域特征分析

    Fig.  4  Time domain characteristic analysis of DCE-MRI

    图  5  模糊速度函数

    Fig.  5  Fuzzy speed function

    图  6  肿块形乳腺癌灶分割结果((a)医生手动分割; (b)结合二值水平集和形态学的分割方法; (c)基于边界的ACM; (d)基于局部高斯能量的ACM; (e)基于区域的ACM; (f)基于MRF的区域ACM; (g) U-net; (h) V-net; (i)本文方法)

    Fig.  6  Tumor-shaped breast lesion segmentation results ((a) Expert manual segmentation; (b) Segmentation method based on binary level set and morphological operation; (c) Edge-based ACM; (d) ACM based on local Gaussian energy; (e) Region-based ACM; (f) Regional ACM based on MRF; (g) U-net; (h) V-net; (i) The method in this paper)

    图  7  非肿块形乳腺癌灶分割结果((a)医生手动分割; (b)结合二值水平集和形态学的分割方法; (c)基于边界的ACM; (d)基于局部高斯能量的ACM; (e)基于区域的ACM; (f)基于MRF的区域ACM; (g) U-net; (h) V-net; (i)本文方法)

    Fig.  7  Non tumor-shaped breast lesion segmentation results ((a) Expert manual segmentation; (b) Segmentation method based on binary level set and morphological operation; (c) Edge-based ACM; (d) ACM based on local gaussian energy; (e) Region-based ACM; (f) Regional ACM based on MRF; (g) U-net; (h) V-net; (i) The method in this paper)

    表  1  V-net和U-net训练集结果

    Table  1  Results of V-net and U-net in training sets

    分割
    算法
    $R_{\rm{TP}}$
    (均值±标准差)
    $R_{\rm{FP}}$
    (均值±标准差)
    $D_{\rm{S}}$
    (均值±标准差)
    U-net0.9706±0.11890.6673±0.66620.6252±0.1456
    V-net0.9408±0.06370.0732±0.12870.8836±0.0917
    下载: 导出CSV

    表  2  50个临床样本分割结果

    Table  2  Segmentation results of 50 clinical samples

    分割算法$R_{\rm{TP}}$(均值±标准差)$R_{\rm{FP}}$(均值±标准差)$D_{\rm{S}}$(均值±标准差)
    结合二值水平集和形态学的分割方法0.9759±0.02760.9100±1.61390.6910±0.2537
    基于边界的ACM0.9676±0.05510.6270±0.42380.5146±0.2272
    基于局部高斯能量的ACM0.9548±0.08180.8368±1.02170.6147±0.2040
    基于区域的ACM0.9892±0.01351.0604±1.01930.5857±0.2357
    基于MRF的区域ACM0.8709±0.11780.2018±0.32710.7524±0.1438
    U-net0.9389±0.11560.5988±0.37720.6143±0.1456
    V-net0.8762±0.14080.1131±0.09610.7920±0.1402
    本文方法0.9297±0.04130.0337±0.02220.8996±0.0410
    下载: 导出CSV

    表  3  算法的时间复杂度

    Table  3  Time complexity of the algorithm

    分割算法时间复杂度平均执行时间 (s)
    结合二值水平集和形态学的分割方法${\rm O}(n)$3.3500
    基于边界的ACM${\rm O}(n)$3.2550
    基于局部高斯能量的ACM${\rm O}(n)$3.6790
    基于区域的ACM${\rm O}(n)$1.1188
    基于MRF的区域ACM${\rm O}(n^2)$2.7925
    U-net${\rm O}(n)$1.6102
    V-net${\rm O}(n)$3.3669
    本文方法${\rm O}(n^2)$4.6575
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-11-14
  • 录用日期:  2019-06-21
  • 网络出版日期:  2020-07-10
  • 刊出日期:  2020-07-10

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