Elastic medical image registration model with high-frequency preservation based on spectrum decomposition

doi:10.11772/j.issn.1001-9081.2025030322

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (3): 924-932.DOI: 10.11772/j.issn.1001-9081.2025030322

• Multimedia computing and computer simulation • Previous Articles Next Articles

Elastic medical image registration model with high-frequency preservation based on spectrum decomposition

Yongwei JIANG¹^,², Xiaoqing CHEN¹^,²(), Linjie FU¹^,²

^1.Chengdu Institution of Computer Application，Chinese Academy of Sciences，Chengdu Sichuan 610213，China
^2.School of Computer Science and Technology，University of Chinese Academy of Sciences，Beijing 100049，China

Received:2025-03-28 Revised:2025-05-12 Accepted:2025-05-13 Online:2025-05-27 Published:2026-03-10
Contact: Xiaoqing CHEN
About author:JIANG Yongwei， born in 2000， M. S. candidate. His research interests include deep learning， medical image processing.
FU Linjie， born in 1995， Ph. D. candidate. His research interests include image registration.
Supported by:
Chengdu Municipal Science and Technology Bureau Project(20240321174430664)

基于频谱分解的高频保持医学图像弹性配准模型

姜勇维¹^,², 陈晓清¹^,²(), 付麟杰¹^,²

^1.中国科学院成都计算机应用研究所，成都 610213
^2.中国科学院大学计算机科学与技术学院北京 100049

通讯作者: 陈晓清
作者简介:姜勇维（2000—），男，黑龙江哈尔滨人，硕士研究生，主要研究方向：深度学习、医学图像处理
付麟杰（1995—），重庆人，博士研究生，主要研究方向：图像配准。
基金资助:
成都市科技局项目(20240321174430664)

Abstract

Abstract:

Elastic registration is regarded as a key task in medical image processing， whose performance directly affects the accuracy of subsequent tasks such as segmentation， classification， and prediction. However， due to the insensitivity of neural networks to high-frequency components， the existing methods have difficulty in capturing high-frequency information in images， which affects the fitting accuracy of registration field. To address this issue， a high-frequency-preserving medical image registration model based on frequency spectrum decomposition — DFRes （Decomposition in Frequency domain model for Registration） was proposed. In the model， a frequency decomposition strategy was introduced， and a dual-branch structure was adopted to process high- and low-frequency information from the original image. Meanwhile， an Invertible Neural Network （INN） structure with high-frequency preservation characteristics and a bridge-style feature fusion module with ability to fuse high- and low-frequency information were designed， and an alternating spatial-frequency information extraction module was used to further enhance the model’s ability to extract and fuse frequency- and spatial-domain information. Experimental results of comparing DFRes and the existing advanced models on the IXI， OSSAI， and Huaxi rectal cancer datasets show that DFRes achieves significant improvements on multiple metrics. On IXI dataset， compared to the TransMorph model， DFRes has the Dice Similarity Coefficient （DSC） increased by 2.5 percentage points， the Average Surface Distance （ASD） reduced by 0.012， and the Structural SIMilarity （SSIM） increased by 1.6 percentage points. At the same time， the effectiveness of the module design is verified through ablation experiments.

Key words: image registration, medical image processing, spectrum analysis, Inverse Neural Network (INN), deep learning

摘要：

弹性配准是医学图像处理中的关键任务之一，它的效果会直接影响到后续的分割、分类和预测等任务的准确性；然而，由于神经网络的高频不敏感特性，现有的方法难以捕捉图像的高频信息，这影响了配准场的拟合精度。为了解决这个问题，提出一种基于频谱分解的高频保持医学图像配准模型——DFRes （Decomposition in Frequency domain model for Registration）。该模型引入频谱分解策略，并采用双支结构处理原始图像中的高频信息和低频信息；同时，设计具有高频保持特性的可逆神经网络（INN）结构和具有高频低频融合能力的桥式特征融合模块，并通过交替的空间-频谱信息提取模块进一步加强模型对频域和空域信息的提取和融合能力。在IXI、OSSAI和华西直肠癌数据集上，DFRes与现有的先进模型对比的实验结果表明，DFRes在多个指标上取得了显著的提升。在IXI数据集上，相较于TransMorph模型，Dice相似系数（DSC）提高了2.5个百分点，平均表面距离（ASD）降低了0.012，而结构化相似性（SSIM）提高了1.6个百分点。同时，通过消融实验验证了模块设计的有效性。

关键词: 图像配准, 医学图像处理, 频谱分析, 可逆神经网络, 深度学习

CLC Number:

TP183

Yongwei JIANG, Xiaoqing CHEN, Linjie FU. Elastic medical image registration model with high-frequency preservation based on spectrum decomposition[J]. Journal of Computer Applications, 2026, 46(3): 924-932.

姜勇维, 陈晓清, 付麟杰. 基于频谱分解的高频保持医学图像弹性配准模型[J]. 《计算机应用》唯一官方网站, 2026, 46(3): 924-932.

Figures/Tables 12

References 36

[1]	HASKINS G， KRUGER U， YAN P. Deep learning in medical image registration： a survey ［J］. Machine Vision and Applications， 2020， 31（1）： No.8.
[2]	黄一超，傅锐芝，王昕辰，等. 医学影像图像与组织学图像配准的研究进展［J］. 中国医学计算机成像杂志， 2024， 30（5）： 646-652.
	HUANG Y C， FU R Z， WANG X C， et al. Research progress on registration of medical image and histological image ［J］. Chinese Computed Medical Imaging， 2024， 30（5）： 646-652.
[3]	NEYSHABUR B， TOMIOKA R， SREBRO N. In search of the real inductive bias： on the role of implicit regularization in deep learning［EB/OL］. ［2024-10-02］..
[4]	RAHAMAN N， BARATIN A， ARPIT D， et al. On the spectral bias of neural networks［C］// Proceedings of the 36th International Conference on Machine Learning. New York： JMLR.org， 2019： 5301-5310.
[5]	JIANG L， DAI B， WU W， et al. Focal frequency loss for image reconstruction and synthesis［C］// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE， 2021： 13899-13909.
[6]	AZAD R， BOZORGPOUR I A， ASADI-AGHBOLAGHI M， et al. Deep frequency re-calibration U-Net for medical image segmentation［C］// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision Workshops. Piscataway： IEEE， 2021： 3267-3276.
[7]	WANG H， WU X， HUANG Z， et al. High-frequency component helps explain the generalization of convolutional neural networks［C］// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2020： 8681-8691.
[8]	MUSTAJAB A H， LYU H， RIZVI Z， et al. Physics-informed neural networks for high-frequency and multi-scale problems using transfer learning ［J］. Applied Sciences， 2024， 14（8）： No.3204.
[9]	MILDENHALL B， SRINIVASAN P P， TANCIK M， et al. NeRF： representing scenes as neural radiance fields for view synthesis ［J］. Communications of the ACM， 2022， 65（1）： 99-106.
[10]	DINH L， KRUEGER D， BENGIO Y. NICE： non-linear independent components estimation［EB/OL］. ［2024-10-02］..
[11]	KINGMA D P， DHARIWAL P. Glow： generative flow with invertible 1×1 convolutions［C］// Proceedings of the 32nd International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2018： 10236-10245.
[12]	XU X， WU C， ROSENMAN S， et al. BridgeTower： building bridges between encoders in vision-language representation learning［C］// Proceedings of the 37th AAAI Conference on Artificial Intelligence. Palo Alto： AAAI Press， 2023： 10637-10647.
[13]	ONOFREY J A， STAIB L H， PAPADEMETRIS X. Semi-supervised learning of nonrigid deformations for image registration［C］// Proceedings of the 2013 International MICCAI Workshop on Medical Computer Vision， LNCS 8331. Cham： Springer， 2014： 13-23.
[14]	BALAKRISHNAN G， ZHAO A， SABUNCU M R， et al. VoxelMorph： a learning framework for deformable medical image registration ［J］. IEEE Transactions on Medical Imaging， 2019， 38（8）： 1788-1800.
[15]	KIM B， KIM D H， PARK S H， et al. CycleMorph： cycle consistent unsupervised deformable image registration［J］. Medical Image Analysis， 2021， 71： No.102036.
[16]	CHEN J， FREY E C， HE Y， et al. TransMorph： Transformer for unsupervised medical image registration［J］. Medical Image Analysis， 2022， 82： No.102615.
[17]	CHEN J， HE Y， FREY E C， et al. ViT-V-Net： Vision Transformer for unsupervised volumetric medical image registration［EB/OL］. ［2024-10-02］..
[18]	WANG W， XIE E， LI X， et al. Pyramid Vision Transformer： a versatile backbone for dense prediction without convolutions［C］// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE， 2021： 548-558.
[19]	XU Z， LUO J， LU D， et al. Double-uncertainty guided spatial and temporal consistency regularization weighting for learning-based abdominal registration ［C］// Proceedings of the 2022 International Conference on Medical Image Computing and Computer-Assisted Intervention， LNCS 13436. Cham： Springer， 2022： 14-24.
[20]	RAO Y， ZHOU Y， WANG Y. Salient deformable network for abdominal multiorgan registration ［J］. Medical Physics， 2022， 49（9）： 5953-5963.
[21]	CHEN J， FREY E C， DU Y. Unsupervised learning of diffeomorphic image registration via TransMorph ［C］// Proceedings of the 2022 International Workshop on Biomedical Image Registration， LNCS 13386. Cham： Springer， 2022： 96-102.
[22]	王一铭，李世源，廖南清，等. 基于证据深度学习的不确定性感知无监督医学图像配准模型［J］. 计算机应用，2025， 45（10）： 3371-3380.
	WANG Y M， LI S Y， LIAO N Q， et al. Uncertainty-aware unsupervised medical image registration model based on evidential deep learning［J］. Journal of Computer Applications， 2025， 45（10）： 3371-3380.
[23]	CHEN Y， HU X， LU T， et al. A multi-scale large kernel attention with U-Net for medical image registration［J］. The Journal of Supercomputing， 2025， 81： No.70.
[24]	MENG M， FENG D， BI L， et al. Correlation-aware coarse-to-fine MLPs for deformable medical image registration［C］// Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2024： 9645-9654.
[25]	HAN Y， WANG L， HUANG Z， et al. A novel 3D magnetic resonance imaging registration framework based on the Swin-Transformer UNet+ model with 3D dynamic snake convolution scheme ［J］. Journal of Imaging， 2025， 11（2）： No.54.
[26]	QI Y， HE Y， QI X， et al. Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation［C］// Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE， 2023： 6047-6056.
[27]	MENG M， FULHAM M， FENG D， et al. AutoFuse： automatic fusion networks for deformable medical image registration［J］. Pattern Recognition， 2025， 161： No.111338.
[28]	SHAN L， LI X， WANG W. Decouple the high-frequency and low-frequency information of images for semantic segmentation［C］// Proceedings of the 2021 IEEE International Conference on Acoustics， Speech and Signal Processing. Piscataway： IEEE， 2021： 1805-1809.
[29]	LI J， ZHANG S， SUN Y， et al. Frequency-driven edge guidance network for semantic segmentation of remote sensing images［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2024， 17： 9677-9693.
[30]	PATRO B N， NAMBOODIRI V P， AGNEESWARAN V S. SpectFormer： frequency and attention is what you need in a vision Transformer ［C］// Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision. Piscataway： IEEE， 2025： 9543-9554.
[31]	WANG J， CHEN H， XU H， et al. SFANet： spatial-frequency attention network for weather forecasting［EB/OL］. ［2024-12-02］..
[32]	QIN Z， ZHANG P， WU F， et al. FcaNet： frequency channel attention networks［C］// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE， 2021： 763-772.
[33]	ZHENG H， GONG M， LIU T， et al. HFA-Net： high frequency attention Siamese network for building change detection in VHR remote sensing images［J］. Pattern Recognition， 2022， 129： No.108717.
[34]	PHAM C， NGUYEN V A， LE T， et al. Frequency attention for knowledge distillation［C］// Proceedings of the 2024 IEEE/CVF Winter Conference on Applications of Computer Vision. Piscataway： IEEE， 2024： 2266-2275.
[35]	ZHAO Z， BAI H， ZHANG J， et al. CDDFuse： correlation-driven dual-branch feature decomposition for multi-modality image fusion［C］// Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2023： 5906-5916.
[36]	XIAO M， ZHENG S， LIU C， et al. Invertible image rescaling［C］// Proceedings of the 2020 European Conference on Computer Vision， LNCS 12346. Cham： Springer， 2020： 126-144.

模型	IXI				OASIS				Huaxi Rect
模型	DSC	ASD	SSIM	Jac	DSC	ASD	SSIM	Jac	DSC	ASD	SSIM	Jac
VoxelMorph	0.679	1.135	0.865	2.152	0.715	0.782	0.813	1.864	0.710	1.279	0.716	2.152
CycleMorph	0.695	1.068	0.875	1.784	0.741	0.735	0.825	1.784	0.717	1.126	0.711	2.006
ViT-V-Net	0.741	0.942	0.912	0.410	0.781	0.636	0.917	0.372	0.792	0.548	0.863	0.410
PVT	0.736	0.976	0.905	0.767	0.776	0.689	0.648	0.865	0.748	0.863	0.789	0.865
TransMorph	0.744	0.917	0.902	0.967	0.782	0.581	0.912	0.926	0.795	0.586	0.902	0.764
Dsc	0.765	0.911	0.926	0.577	0.788	0.512	0.941	0.553	0.807	0.492	0.894	0.142
LKA	0.737	0.971	0.911	0.745	0.622	0.622	0.932	0.849	0.784	0.577	0.883	0.872
DFRes	0.769	0.905	0.918	0.397	0.791	0.497	0.943	0.302	0.802	0.484	0.897	0.012

模型	IXI				OASIS				Huaxi Rect
模型	DSC	ASD	SSIM	Jac	DSC	ASD	SSIM	Jac	DSC	ASD	SSIM	Jac
VoxelMorph	0.679	1.135	0.865	2.152	0.715	0.782	0.813	1.864	0.710	1.279	0.716	2.152
CycleMorph	0.695	1.068	0.875	1.784	0.741	0.735	0.825	1.784	0.717	1.126	0.711	2.006
ViT-V-Net	0.741	0.942	0.912	0.410	0.781	0.636	0.917	0.372	0.792	0.548	0.863	0.410
PVT	0.736	0.976	0.905	0.767	0.776	0.689	0.648	0.865	0.748	0.863	0.789	0.865
TransMorph	0.744	0.917	0.902	0.967	0.782	0.581	0.912	0.926	0.795	0.586	0.902	0.764
Dsc	0.765	0.911	0.926	0.577	0.788	0.512	0.941	0.553	0.807	0.492	0.894	0.142
LKA	0.737	0.971	0.911	0.745	0.622	0.622	0.932	0.849	0.784	0.577	0.883	0.872
DFRes	0.769	0.905	0.918	0.397	0.791	0.497	0.943	0.302	0.802	0.484	0.897	0.012

实验序号	Harr	INN	Sp	Fusion	DSC	ASD	SSIM
1		√	√	√	0.705	3.731	0.785
2	√	√	√		0.677	2.374	0.709
3	√	√		√	0.762	1.158	0.892
4	√		√	√	0.723	1.875	0.815
5	√	√	√	√	0.769	0.905	0.918

实验序号	Harr	INN	Sp	Fusion	DSC	ASD	SSIM
1		√	√	√	0.705	3.731	0.785
2	√	√	√		0.677	2.374	0.709
3	√	√		√	0.762	1.158	0.892
4	√		√	√	0.723	1.875	0.815
5	√	√	√	√	0.769	0.905	0.918

Two-Stage	DSC	Bending	DSC	ASD	SSIM
	√	√	0.766	0.891	0.917
√		√	0.757	0.885	0.908
√	√		0.768	0.901	0.918
√	√	√	0.769	0.905	0.918

Elastic medical image registration model with high-frequency preservation based on spectrum decomposition

基于频谱分解的高频保持医学图像弹性配准模型

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