On Multimodal Digital Image Registration Based On Clifford Algebra Feature Descriptors

As digital technology and computing power advance rapidly,digital image processing has become crucial for efficient and accurate information extraction and analysis in fields such as medical diagnosis and treatment,intelligent surveillance,and machine vision.Among these,multimodal medical image registration technology serves as an important diagnostic and therapeutic aid in clinical applications,playing a vital role in lesion analysis,disease monitoring,and surgical planning.Multimodal medical image registration refers to the process of spatially aligning and fusing image data from different medical imaging devices,enabling the comprehensive use of complementary information from various imaging modalities.This provides clinicians with more comprehensive and precise tissue characteristics and lesion localization,improving diagnostic accuracy.However,despite the progress made in multimodal image registration technology in the medical field,challenges remain in clinical applications,including poor timeliness,insufficient robustness,low multimodal registration accuracy,and unsatisfactory clinical outcomes.In this context,this paper identifies key technical challenges in multimodal medical image registration that urgently require research and breakthroughs,including intensity inhomogeneity and noise interference,quality-efficiency imbalance and poor real-time performance,feature volatility and poor generalizability,and initial value dependence and error accumulation.Based on this,the paper focuses on the key technical challenges of medical digital image registration in multimodal scenarios,utilizing geometric feature similarity description and the Clifford algebra computational framework.By comprehensively applying edge segmentation,invariant feature mining,vector fitting,descriptor construction,and spatial optimization techniques,this paper systematically investigates and proposes feature description and feature matching methods for multimodal registration,achieving efficient and stable registration of multimodal medical images.Experimental verification shows that the proposed targeted registration methods have low computational complexity,high accuracy,and robustness,offering potential for clinical trials and widespread application,and providing a reliable solution for multimodal medical image registration.The main research content of this paper is as follows:1)To address the issues of intensity inhomogeneity and noise interference in medical digital images caused by imaging principles or patient physiological changes,a multimodal medical digital image 2D registration algorithm based on Clifford contour descriptors and fast spatial optimization is proposed.The algorithm utilizes the main contour angle measurement calculation method in Clifford algebra space,constructs contour feature descriptors,and performs matching.Subsequently,a fast spatial optimization algorithm based on Clifford rotation operators is constructed for descriptor fitting and spatial transformation,completing the registration through contour feature descriptor matching and optimization fitting.The effectiveness and robustness of the algorithm are evaluated using the publicly available Brain Web dataset.Experimental results show that the proposed registration algorithm effectively mitigates intensity inhomogeneity and noise interference,exhibiting high registration accuracy and robustness.This method not only enriches medical digital image 2D registration strategies but also lays the foundation for the 3D expansion of registration strategies.2)To address the issues of low registration efficiency and real-time performance caused by factors such as complex anatomical structures,individual differences in lesions,and large volumes of 3D multimodal data,a multimodal medical digital image 3D registration algorithm based on conformal geometric algebra local-global feature descriptors is proposed to balance computational efficiency and registration accuracy.The algorithm leverages the feature vector representation and computational advantages of Clifford algebra,proposing a local feature descriptor construction method based on point cloud neighborhood stereoscopic projection invariants,as well as a global feature descriptor construction method based on spatially constrained point pair invariants.The registration is completed using local-global feature descriptor matching.The RIRE public dataset is used for registration visualization analysis and quantitative comparison of registration quality and efficiency to evaluate the algorithm’s effectiveness.Experimental results show that this registration method has low computational complexity,improving registration speed while maintaining registration accuracy,meeting the real-time requirements of multimodal 3D medical image registration in clinical settings.3)To address the issue of registration failure caused by feature loss due to sampling differences in medical digital images and to enhance the generality of registration algorithms,a multimodal medical digital image 3D registration algorithm based on quadratic surface feature descriptors in Clifford algebra space is proposed.The algorithm proposes a multi-scale ellipsoidal feature descriptor construction method with error compensation,as well as a clustered parabolic surface feature descriptor construction method.It uses the Clifford algebra space for multiple vector representations of quadratic surfaces,and performs hierarchical registration through the calibration of the coordinate system with the ellipsoid descriptors and the similarity matching of the parabolic surface descriptors.The RIRE public dataset is used to verify the effectiveness and assess the error level,while the Brain Web public dataset is used to evaluate the algorithm’s generalizability.Experimental results show that this registration method has high registration accuracy and expands the applicability of multimodal registration algorithms.4)To address issues such as strong dependency on registration initialization and preprocessing error accumulation in clinical application scenarios like skull fractures,surgical operations,and brain tumors,a multimodal medical digital image 3D registration algorithm based on an improved 3D SURF framework and Clifford algebra angle invariance descriptors is proposed.The algorithm constructs a 3D SURF framework based on the Hessian fourdimensional scale space and extracts feature points.It proposes a gradient angle invariance calculation method for Clifford algebra geometric feature descriptors and constructs a 3D fast spatial optimization model to achieve robust descriptor registration.The RIRE public dataset and clinical example datasets are used for error assessment and comparison.Experimental results show that the proposed algorithm does not rely on initial registration values and effectively improves registration accuracy and stability,providing theoretical and technical support for the clinical application of multimodal medical digital image 3D registration.