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基于平衡系数的Active Demons非刚性配准算法

薛鹏 杨佩 曹祝楼 贾大宇 董恩清

薛鹏, 杨佩, 曹祝楼, 贾大宇, 董恩清. 基于平衡系数的Active Demons非刚性配准算法. 自动化学报, 2016, 42(9): 1389-1400. doi: 10.16383/j.aas.2016.c150186
引用本文: 薛鹏, 杨佩, 曹祝楼, 贾大宇, 董恩清. 基于平衡系数的Active Demons非刚性配准算法. 自动化学报, 2016, 42(9): 1389-1400. doi: 10.16383/j.aas.2016.c150186
XUE Peng, YANG Pei, CAO Zhu-Lou, JIA Da-Yu, DONG En-Qing. Active Demons Non-rigid Registration Algorithm Based on Balance Coefficient. ACTA AUTOMATICA SINICA, 2016, 42(9): 1389-1400. doi: 10.16383/j.aas.2016.c150186
Citation: XUE Peng, YANG Pei, CAO Zhu-Lou, JIA Da-Yu, DONG En-Qing. Active Demons Non-rigid Registration Algorithm Based on Balance Coefficient. ACTA AUTOMATICA SINICA, 2016, 42(9): 1389-1400. doi: 10.16383/j.aas.2016.c150186

基于平衡系数的Active Demons非刚性配准算法

doi: 10.16383/j.aas.2016.c150186
基金项目: 

高等学校博士学科点专项科研基金 20120131110062

山东省科技发展计划项目 2013GGX10104

国家自然科学基金 81371635

详细信息
    作者简介:

    薛鹏山东大学(威海)硕士研究生.2015年获得哈尔滨工程大学工学学士学位.主要研究方向为医学图像处理.E-mail:xuepeng2016@126.com

    杨佩山东大学(威海)硕士研究生.2008年获得山东大学(威海)学士学位.主要研究方向为医学图像处理.E-mail:yangpei301@163.com

    曹祝楼山东大学(威海)数学与统计学院讲师.2015年获得山东大学(威海)博士学位.主要研究方向为图像处理.E-mail:zlouc@sdu.edu.cn

    贾大宇山东大学(威海)硕士研究生, 2013年获得哈尔滨工业大学学士学位.主要研究方向医学图像处理.E-mail:dayu_jia1990@126.com

    通讯作者:

    董恩清山东大学(威海)教授.2002年于西安交通大学获得信息与通信工程专业博士学位.主要研究方向包括无线通信技术, 无线传感器网络, 医学图像处理.本文通信作者.E-mail:enqdong@sdu.edu.cn

Active Demons Non-rigid Registration Algorithm Based on Balance Coefficient

Funds: 

Specialized Research Fund for the Doctoral Program of Higher Education of China 20120131110062

Science and Technology Development Project of Shandong Province 2013GGX10104

National Natural Science Foundation of China 81371635

More Information
    Author Bio:

    Master student at Shandong University (Weihai). He received his bachelor degress from Harbin Engineer University in 2015. His main research interest is medical image processing

    Master student at Shandong University (Weihai). She received her bachelor degree from Shandong University (Weihai) in 2008. Her main research interest is medical image processing

    Lecturer at the Institute of Mathematics and Statistics, Shandong University (Weihai). He received his Ph. D. degree from Shandong University (Weihai) in 2015. His main research interest is image processing

    Master student at Shandong University (Weihai). He received his bachelor degree from Harbin Institute of Technology in 2013. His main research interest is medical image processing

    Corresponding author: DONG En-Qing Professor at the Shandong University (Weihai). He received his Ph. D. degree from Xi0an Jiaotong University in 2002. His research interest covers wireless communication network technology, wireless sensor networks, and medical image processing. Corresponding author of this paper
  • 摘要: 经典的Active demons算法利用参考图像和浮动图像的梯度信息作为驱动力,并使用均化系数调节两种驱动力之间的强度.该算法克服了Demons算法单一使用参考图像的梯度信息作为驱动力的缺点,但是Active demons算法中的均化系数无法同时兼顾大形变和小形变区域的准确配准,还会导致配准的收敛速度和精确度相互制约的问题.为此,本文提出一种新的Active demons非刚性配准算法.提出的算法在Active demons扩散方程中引入一个称为平衡系数的新参数,与均化系数联合调整驱动力,不仅可以兼顾图像中同时具有的大形变和小形变区域的准确配准,而且在一定程度上缓和了收敛速度和精确度相互制约的问题.为了进一步提高配准的收敛速度和精确度,避免陷入局部极值,在新的配准算法的实现中引入由粗到细的多分辨率策略.在Checkboard测试图像、自然图像和医学图像上的实验结果表明,提出的算法较经典的Active demons算法收敛速度更快,配准精度平均提高了54.28%,接近最新的TV-L1光流场图像配准算法的配准精度,解决了Active demons算法存在的问题.
  • 图  1  二值图像

    Fig.  1  The binary images

    图  2  均化系数α对Active demons算法的影响

    Fig.  2  The impact of α on the active demons algorithm

    图  3  均方误差与迭代次数的关系曲线

    Fig.  3  The relations between the mean square error and iterations

    图  4  两种算法对比曲线

    Fig.  4  The comparison of two registration algorithms

    图  5  不同平衡系数k的配准结果

    Fig.  5  The registration results with different k variants

    图  6  不同参数k的配准结果差值

    Fig.  6  The registration results' error with different k variants

    图  7  平衡系数对改进Active demons算法的影响

    Fig.  7  The impact of balance coefficient k on the improved active demons registration algorithm

    图  8  均方差与均化系数和平衡系数的关系曲线

    Fig.  8  The relations of the mean square error with the α and the k

    图  9  图像的配准结果图

    Fig.  9  The image registration results

    图  10  图像的配准结果与参考图像的差值

    Fig.  10  The differences between the registration results and the static image

    图  11  图像的配准结果图

    Fig.  11  The image registration results

    图  12  图像的配准结果与参考图像的差值

    Fig.  12  The differences between the registration results and the static image

    图  13  图像的配准结果图

    Fig.  13  The image registration results

    图  14  图像的配准结果图

    Fig.  14  The image registration results

    图  15  图像的配准结果与参考图像的差值图

    Fig.  15  The differences between the registration results and the static image

    图  16  图像的配准结果图

    Fig.  16  The image registration results

    图  17  图像的配准结果与参考图像的差值图

    Fig.  17  The differences between the registration results and the static image

    表  1  两种算法的配准结果均方差对比

    Table  1  The comparison of two registration algorithms on the MSE

    α 0.05 0.1 0.4 0.5 0.6
    AD(×10−4) 3.55 4.02 6.49 8.18 10
    IAD(×10−4) 2.25 2.35 3.71 4.29 5.27
    α 1.0 1.5 2 2.5 3
    AD(×10−4) 34 107 187 255 311
    IAD(×10−4) 12 47 113 183 246
    下载: 导出CSV

    表  2  配准结果的客观分析

    Table  2  The objective analysis of registration results

    评价方法 Demons AD SIAD MIAD TV-L1
    均方差(×10−4) 43 40 29 25 11
    相互系数(%) 99.16 99.24 99.44 99.52 99.98
    峰值信噪比 54.43 55.31 58.36 59.97 87.74
    归一化互信息 1.42 1.42 1.43 1.43 0.71
    结构相似度(%) 95.42 95.74 96.82 97.41 99.97
    下载: 导出CSV

    表  3  配准结果的客观分析

    Table  3  The objective analysis of registration results

    评价方法 Demons AD SIAD MIAD TV-L1
    均方差(×10−4) 13 8.11 6.19 2.81 4.61
    相互系数(%) 96.35 97.69 98.25 99.22 99.87
    峰值信噪比 66.59 71.15 73.87 81.75 91.49
    归一化互信息 1.37 1.29 1.39 1.41 3.96
    结构相似度(%) 88.55 90.99 92.56 96.28 99.11
    下载: 导出CSV

    表  4  配准结果的客观分析

    Table  4  The objective analysis of registration results

    评价方法 Demons AD SIAD MIAD TV-L1
    R 5.09 5.03 5.12 18.69 2.50
    均方差(×10−5) G 5.07 5.10 5.33 17.68 2.47
    B 4.69 4.79 5.01 16.71 3.20
    R 99.84 99.92 99.92 99.90 99.99
    相互系数(%) G 99.86 99.86 99.85 99.83 99.98
    B 99.92 99.84 99.83 99.81 99.71
    R 91.07 91.12 91.04 90.19 94.16
    峰值信噪比 G 91.08 91.05 90.86 90.43 94.20
    B 91.42 91.33 91.14 90.67 93.08
    R 3.25 3.26 3.25 3.27 3.98
    归一化互信息 G 3.17 3.17 3.16 3.17 4.03
    B 3.02 3.02 3.00 3.01 3.63
    R 98.87 97.49 97.56 97.71 99.21
    结构相似度(%) G 98.99 97.46 97.41 97.44 99.27
    B 98.70 97.30 97.23 97.23 98.89
    下载: 导出CSV

    表  5  配准结果的客观分析

    Table  5  The objective analysis of registration results

    评价方法 Demons AD SIAD MIAD TV-L1
    均方差(×10−5) 4.78 7.53 3.06 0.81 8.56
    相互系数(%) 99.81 99.70 99.88 99.97 99.97
    峰值信噪比 99.51 94.94 103.94 117.26 98.81
    归一化互信息 1.43 1.31 1.44 1.54 3.49
    结构相似度(%) 98.71 98.44 99.03 99.76 99.34
    下载: 导出CSV

    表  6  配准结果的客观分析

    Table  6  The objective analysis of registration results

    评价方法 Demons AD SIAD MIAD TV-L1
    均方差(×10−4) 30 19 16 14 11.4
    相互系数(%) 97.21 98.26 98.50 98.75 99.06
    峰值信噪比 58.08 62.81 64.29 66.05 77.56
    归一化互信息 1.38 1.39 1.41 1.43 2.90
    结构相似度(%) 83.55 89.27 89.61 94.40 98.77
    下载: 导出CSV

    表  7  几种算法的配准结果均方差值比较

    Table  7  The objective analysis of difference algorithms

    Cases AD SIAD MIAD Pe(s) Pe(M)
    (×10−4) (×10−4) (×10−4) (%) (%)
    1 40 29 25 27.5 37.5
    2 8.11 6.19 2.81 23.00 65.00
    3 0.497 0.515 1.769 16.67 53.33
    4 0.753 0.306 0.081 59.32 89.28
    5 19 16 14 15.79 26.32
    Average 28.45 54.28
    下载: 导出CSV
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