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基于线性化核标签融合的脑MR图像分割方法

刘悦 魏颖 贾晓甜 王楚媛

刘悦, 魏颖, 贾晓甜, 王楚媛.基于线性化核标签融合的脑MR图像分割方法.自动化学报, 2020, 46(12): 2593−2606 doi: 10.16383/j.aas.c180407
引用本文: 刘悦, 魏颖, 贾晓甜, 王楚媛.基于线性化核标签融合的脑MR图像分割方法.自动化学报, 2020, 46(12): 2593−2606 doi: 10.16383/j.aas.c180407
Liu Yue, Wei Ying, Jia Xiao-Tian, Wang Chu-Yuan. Linearized kernel-based label fusion method for brain MR image segmentation. Acta Automatica Sinica, 2020, 46(12): 2593−2606 doi: 10.16383/j.aas.c180407
Citation: Liu Yue, Wei Ying, Jia Xiao-Tian, Wang Chu-Yuan. Linearized kernel-based label fusion method for brain MR image segmentation. Acta Automatica Sinica, 2020, 46(12): 2593−2606 doi: 10.16383/j.aas.c180407

基于线性化核标签融合的脑MR图像分割方法

doi: 10.16383/j.aas.c180407
基金项目: 

国家自然科学基金 61871106

详细信息
    作者简介:

    刘悦  东北大学信息科学与工程学院博士研究生.主要研究方向为图像处理, 计算机视觉与模式识别. E-mail: 18512478164@163.com

    贾晓甜  东北大学信息科学与工程学院模式识别与智能系统专业硕士研究生.主要研究方向为模式识别与图像处理. E-mail: jxt_neu@163.com

    王楚媛  东北大学信息科学与工程学院模式识别与智能系统专业硕士研究生.主要研究方向为模式识别与图像处理. E-mail: wangchuyuan0718@163.com

    通讯作者:

    魏颖  东北大学信息科学与工程学院教授.主要研究方向为图像处理与模式识别, 医学影像计算与分析, 计算机辅助诊断, 计算机视觉.本文通信作者. E-mail: weiying@ise.neu.edu.cn

  • 本文责任编委 张道强

Linearized Kernel-Based Label Fusion Method for Brain MR Image Segmentation

Funds: 

National Natural Science Foundation of China 61871106

More Information
    Author Bio:

    LIU Yue  Ph. D. candidate at the College of Information Science and Engineering, Northeastern University. Her research interest covers image processing, computer vision, and pattern recognition

    JIA Xiao-Tian  Master student of science in pattern recognition and intelligent systems at the College of Information Science and Engineering, Northeastern University. Her research interest covers pattern recognition and image processing

    WANG Chu-Yuan  Master student of Science in Pattern Recognition and Intelligent Systems at the College of Information Science and Engineering, Northeastern University. Her research interest covers pattern recognition and image processing

    Corresponding author: WEI Ying  Professor at the College of Information Science and Engineering, Northeastern University. Her research interest covers image processing and pattern recognition, medical image computation and analysis, computeraided diagnosis, and computer vision. Corresponding author of this paper
  • Recommended by Associate Editor ZHANG Dao-Qiang
  • 摘要: 深层脑结构的形态变化和神经退行性疾病相关, 对脑MR图像中的深层脑结构分割有助于分析各结构的形态变化.多图谱融合方法利用图谱图像中的先验信息, 为脑结构分割提供了一种有效的方法.大部分现有多图谱融合方法仅以灰度值作为特征, 然而深层脑结构灰度分布之间重叠的部分较多, 且边缘不明显.为克服上述问题, 本文提出一种基于线性化核多图谱融合的脑MR图像分割方法.首先, 结合纹理与灰度双重特征, 形成增强特征用于更好地表达脑结构信息.其次, 引入核方法, 通过高维映射捕获原始空间中特征的非线性结构, 增强数据间的判别性和线性相似性.最后, 利用Nyström方法, 对高维核矩阵进行估计, 通过特征值分解计算虚样本, 并在核标签融合过程中利用虚样本替代高维样本, 大大降低了核标签融合的计算复杂度.在三个公开数据集上的实验结果表明, 本文方法在较少的时间消耗内, 提高了分割精度.
    Recommended by Associate Editor ZHANG Dao-Qiang
    1)  本文责任编委 张道强
  • 图  1  搜索邻域示意图.图示为某一图谱MRI的搜索邻域, 对于每个图谱, 都以同样的方法选择搜索邻域内的图像块, 所有图像块集合成预定义字典

    Fig.  1  Diagram of search volume. For each atlas, the same strategy is used to extract image patches in search volume. All patches form predefined dictionary

    图  2  纹理特征计算过程.用LBP算子遍历整幅MRI, 计算每个像素的LBP值, 得到MRI对应的LBP图像

    Fig.  2  Calculation of texture feature. Use LBP operator to traverse the entire MRI and calculate the LBP value for each pixel. Then, the corresponding LBP image is obtained

    图  3  增强特征计算过程示意图.取MR图像和LBP图像同一位置的图像块, 拼接成增强特征向量

    Fig.  3  Calculation of augmented feature. Image patches with the same coordinate in MRI and LBP image concatenate together to form AF vector

    图  4  特征的相似性(a)原始特征的线性相似度; (b)高维特征的线性相似度.映射后的特征具有较好的相似性

    Fig.  4  Similarity of features. (a) Similarity between original features; (b) Similarity between high dimensional features. Mapped data have better similarities

    图  5  线性化核标签融合算法的整体流程

    Fig.  5  Process of linearized kernel-based label fusion method

    图  6  IBSR中的六种脑结构(以第2组为例)

    Fig.  6  Six structures in IBSR dataset (The second subject)

    图  7  各脑结构分割准确率随图谱数目变化趋势

    Fig.  7  Dice value of each structure with different atlas numbers

    图  8  搜索邻域大小对LK + SRC的影响

    Fig.  8  Impact of search volume size on LK + SRC

    图  9  图像块大小对LK + SRC的影响

    Fig.  9  Impact of image patch size on LK + SRC

    图  10  采样对LK + SRC的影响

    Fig.  10  Impact of sample on LK + SRC

    图  11  核函数参数对分割结果的影响

    Fig.  11  Impact of kernel on LK + SRC

    图  12  不同图像特征对分割结果的影响(AF为本文所提的增强特征方法)

    Fig.  12  Impact of different features on segmentation result. AF is the proposed method

    图  13  深层脑结构分割二维结果(黑色加粗的方法为本文所提方法)

    Fig.  13  2D results of subcortical brain structures (Black bolds show the proposed method)

    图  14  深层脑结构分割三维结果(其中, (e)为本文所提方法)

    Fig.  14  3D results of subcortical brain structures ((e) show the proposed method)

    图  15  线性化核标签融合方法在SATA数据集上的分割准确率

    Fig.  15  Dice value of linearized kernel-based label fusion method on SATA dataset

    图  16  各方法消耗时间对比

    Fig.  16  Computational complexity

    表  1  数据集基本信息

    Table  1  Information of each dataset

    数据集 个体数 年龄 类别数 尺寸 分辨率(mm)
    IBSR 18 7$\sim$71 32 $256 \times 256 \times 128$ $0.94 \times 0.94 \times 1.5$和$0.84 \times 0.84 \times 1.5$
    Hammers67n 20 20$\sim$54 67 $192 \times 256 \times 124$ $0.937 \times 0.937 \times 1.5$
    SATA 35 - 14 $256 \times 256 \times 287$ $1.0 \times 1.0 \times 1.0$
    下载: 导出CSV

    表  2  LK方法在IBSR上的Dice值, 加粗的Dice值为同种LF + AF方法中的最高值

    Table  2  Dice value of LK method on IBSR. Blue bolds show the maximum in the same LF + AF method

    方法 丘脑 壳核 尾状核 苍白球 海马 杏仁核 平均
    PB + AF 无LK 0.885 0.802 0.807 0.746 0.811 0.688 0.790
    有LK ${\bf{0.899}}$ ${\bf{0.881}}$ ${\bf{0.886}}$ ${\bf{0.837}}$ ${\bf{0.816}}$ ${\bf{0.743}}$ ${\bf{0.844}}$
    SRC + AF 无LK 0.864 0.823 0.809 0.758 0.714 0.639 0.768
    有LK ${\bf{0.898}}$ ${\bf{0.883}}$ ${\bf{0.866}}$ ${\bf{0.831}}$ ${\bf{0.818}}$ ${\bf{0.678}}$ ${\bf{0.829}}$
    DDL + AF 无LK 0.902 0.892 0.882 0.851 0.830 ${\bf{0.695}}$ 0.842
    有LK ${\bf{0.905}}$ ${\bf{0.898}}$ ${\bf{0.883}}$ ${\bf{0.854}}$ ${\bf{0.839}}$ 0.678 ${\bf{0.843}}$
    下载: 导出CSV

    表  3  LK方法在Hammers67n20上的Dice值, 加粗的Dice值为每种LF + AF方法中的最高值

    Table  3  Dice value of LK method on Hammers67n20. Blue bolds show the maximum in the same LF + AF method

    方法 丘脑 壳核 尾状核 苍白球 海马 杏仁核 平均
    PB + AF 无LK 0.844 0.849 0.838 0.762 0.799 0.825 0.820
    有LK ${\bf{0.879}}$ ${\bf{0.890}}$ ${\bf{0.889}}$ ${\bf{0.811}}$ ${\bf{0.831}}$ ${\bf{0.868}}$ ${\bf{0.861}}$
    SRC + AF 无LK 0.862 0.859 0.852 0.748 0.793 0.802 0.819
    有LK ${\bf{0.887}}$ ${\bf{0.885}}$ ${\bf{0.893}}$ ${\bf{0.792}}$ ${\bf{0.842}}$ ${\bf{0.839}}$ ${\bf{0.856}}$
    DDL + AF 无LK 0.875 0.882 0.870 0.789 0.812 ${\bf{0.860}}$ 0.848
    有LK ${\bf{0.892}}$ ${\bf{0.891}}$ ${\bf{0.875}}$ ${\bf{0.802}}$ ${\bf{0.830}}$ 0.857 ${\bf{0.858}}$
    下载: 导出CSV

    表  4  本文方法和现有脑结构分割方法在IBSR数据集上的结果, 评价指标Dice

    Table  4  Results compared with existing brain structure segmentation method, measured with Dice

    FIRST FreeSurfer MS-CNN BrainSegNet FCNN M-net Dolz 本文方法
    丘脑 0.889 0.840 0.889 0.89 0.87 0.90 0.92 0.905
    壳核 0.875 0.809 0.875 0.91 0.83 0.90 0.90 0.898
    尾状核 0.827 0.803 0.849 0.87 0.78 0.87 0.91 0.886
    苍白球 0.810 0.703 0.787 0.82 0.75 0.82 0.83 0.854
    海马 0.811 0.764 0.788 0.82 - 0.82 - 0.839
    杏仁核 0.750 0.589 0.654 0.74 - 0.73 - 0.743
    平均 0.827 0.751 0.807 0.842 0.808 0.84 0.89 0.844
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-06-08
  • 录用日期:  2019-01-09
  • 刊出日期:  2020-12-29

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