一种稳健的非刚性医学影像配准方法

doi:10.3969/j.issn.1001-2400.2014.05.010

J4 ›› 2014, Vol. 41 ›› Issue (5): 54-60.doi: 10.3969/j.issn.1001-2400.2014.05.010

一种稳健的非刚性医学影像配准方法

李琦;姬红兵;臧博;刘靳

(西安电子科技大学电子工程学院，陕西西安 710071)

收稿日期:2013-05-13 出版日期:2014-10-20 发布日期:2014-11-27
通讯作者: 李琦
作者简介:李琦(1979-)，女，讲师，西安电子科技大学博士研究生，E-mail: qili@xidian.edu.cn．
基金资助:
国家自然科学基金资助项目(61101246)；中央高校基本科研业务费专项资金资助项目(JB140209，72125748)

Non-rigid registration of medical images based on local linear embedding and improved L-BFGS optimization

LI Qi;JI Hongbing;ZANG Bo;LIU Jin

(School of Electronic Engineering, Xidian Univ., Xi'an 710071, China)

Received:2013-05-13 Online:2014-10-20 Published:2014-11-27
Contact: LI Qi

摘要/Abstract

摘要：

提出了一种基于局部线性嵌入(LLE)和改进的L-BFGS优化算法的非刚性配准新方法．该方法首先计算影像不同方向上的定序特征，用于补充传统互信息测度中缺失的空间结构信息；然后，运用LLE方法及其逆映射对高维定序特征进行降维和融合；进而结合影像灰度信息构造了一种基于混合熵的配准测度，有效保证了配准测度函数的光滑性和收敛性；最后，采用改进的L-BFGS优化方法搜索最优配准参数．多组仿真数据的测试结果表明，在噪声情况下，所提方法具有精度高、鲁棒性强的特点，优于现有几种方法．

关键词: 非刚性配准, 局部线性嵌入, 定序特征

Abstract:

Non-rigid registration of medical images has become a challenging task in medical image processing and applications. In this paper, we propose a local linear embedding (LLE) and improved L-BFGS (limited-memory Broyden Fletcher Goldfarb Shanno) optimization based registration method. With abundant spatial information and good stability in noisy environment, the ordinal features are computed on different orientations to represent spatial information in medical images. For high dimensional ordinal features, the LLE algorithm is used for dimensionality reduction and the inverse mapping of LLE is used to fuse complementary information together. Then a hybrid entropy based similarity measure which integrates image intensity with ordinal feature is chosen as the registration function. Finally an improved L-BFGS algorithm is used to search for the optimal registration parameters. We evaluate the effectiveness of the proposed approach by applying it to the simulated brain image data. Experimental results show that the proposed registration algorithm is less sensitive to noise in images. Compared with some traditional methods, the proposed algorithm is of higher precision and better robustness.

Key words: non-rigid registration, local linear embedding, ordinal feature

中图分类号:

TP391.4

李琦;姬红兵;臧博;刘靳. 一种稳健的非刚性医学影像配准方法[J]. J4, 2014, 41(5): 54-60.

LI Qi;JI Hongbing;ZANG Bo;LIU Jin. Non-rigid registration of medical images based on local linear embedding and improved L-BFGS optimization[J]. J4, 2014, 41(5): 54-60.

参考文献

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[1]	刘道华,王莎莎,杨志鹏,崔玉爽. 一种改进的局部嵌入网络人脸图像分类方法[J]. 西安电子科技大学学报, 2020, 47(4): 18-23.
[2]	李琦;姬红兵;同鸣. 一种多模态医学影像鲁棒配准方法[J]. J4, 2011, 38(6): 30-36+145.

一种稳健的非刚性医学影像配准方法

Non-rigid registration of medical images based on local linear embedding and improved L-BFGS optimization

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