基于改进的Zernike矩的局部描述符与图割离散优化的非刚性多模态脑部图像配准

doi:10.11772/j.issn.1001-9081.2018061423

计算机应用 ›› 2019, Vol. 39 ›› Issue (2): 582-588.DOI: 10.11772/j.issn.1001-9081.2018061423

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于改进的Zernike矩的局部描述符与图割离散优化的非刚性多模态脑部图像配准

王丽芳^1,2, 王雁丽^1,2, 蔺素珍^1,2, 秦品乐^1,2, 高媛^1,2

1. 中北大学大数据学院, 太原 030051;
2. 山西省生物医学成像与影像大数据重点实验室 (中北大学), 太原 030051

收稿日期:2018-07-10 修回日期:2018-08-10 发布日期:2019-02-15 出版日期:2019-02-10
通讯作者: 王丽芳
作者简介:王丽芳(1977-),女,山西长治人,副教授,博士,CCF会员,主要研究方向:机器视觉、大数据处理、医学图像处理;王雁丽(1992-),女,山西朔州人,硕士研究生,主要研究方向:医学图像配准、机器学习;蔺素珍(1966-),女,山西太原人,教授,博士,主要研究方向:图像处理、机器视觉、信息融合;秦品乐(1978-),男,山西长治人,副教授,博士,主要研究方向:机器视觉、大数据处理、三维重建;高媛(1972-),女,山西太原人,副教授,硕士,主要研究方向:机器视觉、大数据处理、三维重建。
基金资助:
山西省青年基金资助项目（201601D021080）；中北大学研究生科技立项自然科学项目（20171441）。

Non-rigid multi-modal brain image registration by using improved Zernike moment based local descriptor and graph cuts discrete optimization

WANG Lifang^1,2, WANG Yanli^1,2, LIN Suzhen^1,2, QIN Pinle^1,2, GAO Yuan^1,2

1. School of Data Science, North University of China, Taiyuan Shanxi 030051, China;
2. Biomedical Imaging Data Key Laboratory of Shanxi Province(North University of China), Taiyuan Shanxi 030051, China

Received:2018-07-10 Revised:2018-08-10 Online:2019-02-15 Published:2019-02-10
Supported by:
This work is partially supported by the Youth Fund Project of Shanxi Province (201601D021080), the Natural Science Project of Postgraduate in North University of China (20171441).

摘要/Abstract

摘要： 针对脑部图像中存在噪声和强度失真时，基于结构信息的方法不能同时准确提取图像强度信息和边缘、纹理特征，并且连续优化计算复杂度相对较高的问题，根据图像的结构信息，提出了基于改进Zernike距的局部描述符（IZMLD）和图割（GC）离散优化的非刚性多模态脑部图像配准方法。首先，将图像配准问题看成是马尔可夫随机场（MRF）的离散标签问题，并且构造能量函数，两个能量项分别由位移矢量场的像素相似性和平滑性组成。其次，采用变形矢量场的一阶导数作为平滑项，用来惩罚相邻像素间有较大变化的位移标签；用基于IZMLD计算的相似性测度作为数据项，用来表示像素相似性。然后，在局部邻域中用图像块的Zernike矩来分别计算参考图像和浮动图像的自相似性并构造有效的局部描述符，把描述符之间的绝对误差和（SAD）作为相似性测度。最后，将整个能量函数离散化，并且使用GC的扩展优化算法求最小值。实验结果表明，与基于结构表示的熵图像的误差平方和（ESSD）、模态独立邻域描述符（MIND）和随机二阶熵图像（SSOEI）的配准方法相比，所提算法目标配准误差的均值分别下降了18.78%、10.26%和8.89%，并且比连续优化算法缩短了约20 s的配准时间。所提算法实现了在图像存在噪声和强度失真时的高效精确配准。

关键词: 多模态, 图像配准, 自相似性, Zernike矩, 图割

Abstract: When noise and intensity distortion exist in brain images, the method based on structural information cannot accurately extract image intensity information, edge and texture features at the same time. In addition, the computational complexity of continuous optimization is relatively high. To solve these problems, according to the structural information of the image, a non-rigid multi-modal brain image registration method based on Improved Zernike Moment based Local Descriptor (IZMLD) and Graph Cuts (GC) discrete optimization was proposed. Firstly, the image registration problem was regarded as the discrete label problem of Markov Random Field (MRF), and the energy function was constructed. The two energy terms were composed of the pixel similarity and smoothness of the displacement vector field. Secondly, a smoothness constraint based on the first derivative of the deformation vector field was used to penalize displacement labels with sharp changes between adjacent pixels. The similarity metric based on IZMLD was used as a data item to represent pixel similarity. Thirdly, the Zernike moments of the image patches were used to calculate the self-similarity of the reference image and the floating image in the local neighborhood and construct an effective local descriptor. The Sum of Absolute Difference (SAD) between the descriptors was taken as the similarity metric. Finally, the whole energy function was discretized and its minimum value was obtained by using an extended optimization algorithm of GC. The experimental results show that compared with the registration method based on the Sum of Squared Differences on Entropy images (ESSD), the Modality Independent Neighborhood Descriptor (MIND) and the Stochastic Second-Order Entropy Image (SSOEI), the mean of the target registration error of the proposed method was decreased by 18.78%, 10.26% and 8.89% respectively; and the registration time of the proposed method was shortened by about 20 s compared to the continuous optimization algorithm. The proposed method achieves efficient and accurate registration for images with noise and intensity distortion.

Key words: multi-modal, image registration, self-similarity, Zernike moments, Graph Cuts (GC)

中图分类号:

TP391.41

王丽芳, 王雁丽, 蔺素珍, 秦品乐, 高媛. 基于改进的Zernike矩的局部描述符与图割离散优化的非刚性多模态脑部图像配准[J]. 计算机应用, 2019, 39(2): 582-588.

WANG Lifang, WANG Yanli, LIN Suzhen, QIN Pinle, GAO Yuan. Non-rigid multi-modal brain image registration by using improved Zernike moment based local descriptor and graph cuts discrete optimization[J]. Journal of Computer Applications, 2019, 39(2): 582-588.

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基于改进的Zernike矩的局部描述符与图割离散优化的非刚性多模态脑部图像配准

Non-rigid multi-modal brain image registration by using improved Zernike moment based local descriptor and graph cuts discrete optimization

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