Non-rigid multi-modal medical image registration based on multi-channel sparse coding

doi:10.11772/j.issn.1001-9081.2017102392

Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (4): 1127-1133.DOI: 10.11772/j.issn.1001-9081.2017102392

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Non-rigid multi-modal medical image registration based on multi-channel sparse coding

WANG Lifang, CHENG Xi, QIN Pinle, GAO Yuan

School of Data Science and Technology, North University of China, Taiyuan Shanxi 030051, China

Received:2017-10-10 Revised:2017-11-18 Online:2018-04-10 Published:2018-04-09
Supported by:
This work is partially supported by the Natural Science Foundation of Shanxi Province (2015011045).

基于多通道稀疏编码的非刚性多模态医学图像配准

王丽芳, 成茜, 秦品乐, 高媛

中北大学大数据学院, 太原 030051

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:大数据处理、医学图像处理、机器视觉;成茜(1993-),女,山西晋中人,硕士研究生,主要研究方向:医学图像配准、机器学习;秦品乐(1978-),男,山西长治人,副教授,博士,主要研究方向:医学影像大数据存储与分析、机器视觉、机器学习;高媛(1972-),女,山西太原人,副教授,硕士,主要研究方向:大数据处理、医学图像处理、三维重建。
基金资助:
山西省自然科学基金资助项目（2015011045）。

Abstract

Abstract: Sparse coding similarity measure has good robustness to gray-scale offset field in non-rigid medical image registration, but it is only suitable for single-modal medical image registration. A non-rigid multi-modal medical image registration method based on multi-channel sparse coding was proposed to solve this problem. In this method, the multi-modal registration was regarded as a multi-channel registration, with each modal running in a separate channel. At first, the two registered images were synthesized and regularized separately, and then they were divided into channels and image blocks. The K-means-based Singular Value Decomposition (K-SVD) algorithm was used to train the image blocks in each channel to get the analytical dictionary and sparse coefficients, and each channel was weightedy summated. The multilayer P-spline free transform model was used to simulate the non-rigid geometric deformation, and the gradient descent method was used to optimize the objective function. The experimental results show that compared with multi-modal similarity measure such as local mutual information, Multi-Channel Local Variance and Residual Complexity (MCLVRC), Multi-Channel Sparse-Induced Similarity Measure (MCSISM) and Multi-Channel Rank Induced Similarity Measure (MCRISM), the root mean square error of the proposed method is decreased by 30.86%, 22.24%, 26.84% and 16.49% respectively. The proposed method can not only effectively overcome the influence of gray-scale offset field on registration in multi-modal medical image registration, but also improve the accuracy and robustness of registration.

Key words: multi-modal image registration, multi-channel, sparse coding, multilayer P-spline, gradient descent method

摘要： 针对稀疏编码相似性测度在非刚性医学图像配准中对灰度偏移场具有较好的鲁棒性，但只适用于单模态医学图像配准的问题，提出基于多通道稀疏编码的非刚性多模态医学图像配准方法。该方法将多模态配准问题视为一个多通道配准问题来解决，每个模态在一个单独的通道下运行；首先对待配准的两幅图像分别进行合成和正则化，然后划分通道和图像块，使用K奇异值分解（K-SVD）算法训练每个通道中的图像块得到分析字典和稀疏系数，并对每个通道进行加权求和，采用多层P样条自由变换模型来模拟非刚性几何形变，结合梯度下降法优化目标函数。实验结果表明，与局部互信息、多通道局部方差和残差复杂性（MCLVRC）、多通道稀疏诱导的相似性测度（MCSISM）、多通道Rank Induced相似性测度（MCRISM）多模态相似性测度相比，均方根误差分别下降了30.86%、22.24%、26.84%和16.49%。所提方法能够有效克服多模态医学图像配准中灰度偏移场对配准的影响，提高配准的精度和鲁棒性。

关键词: 多模态图像配准, 多通道, 稀疏编码, 多层P样条, 梯度下降法

CLC Number:

TP391.41

WANG Lifang, CHENG Xi, QIN Pinle, GAO Yuan. Non-rigid multi-modal medical image registration based on multi-channel sparse coding[J]. Journal of Computer Applications, 2018, 38(4): 1127-1133.

王丽芳, 成茜, 秦品乐, 高媛. 基于多通道稀疏编码的非刚性多模态医学图像配准[J]. 计算机应用, 2018, 38(4): 1127-1133.

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Non-rigid multi-modal medical image registration based on multi-channel sparse coding

基于多通道稀疏编码的非刚性多模态医学图像配准

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