27 results about "Multimodality image registration" patented technology

The invention discloses a multi-modal image registration method and system based on deformation adaptation and computer equipment, and the method comprises the steps: collecting a plurality of groups of multi-modal images, carrying out the gray standardization, and constructing a diversified registration data set; building a registration network model comprising a pyramid coding module, a deformation adaptive module, a cross-modal interaction module and a registration parameter estimation module; inputting an image pair into the modules in sequence, respectively extracting basic feature mapping, deformation feature mapping and interaction enhancement feature mapping, and finally outputting an estimation conversion parameter matrix; a training process is supervised through a preset loss function, optimal network parameters are selected, and a trained registration model is obtained; in practical application, an image pair to be registered is input into the trained model, a conversion parameter matrix is obtained, and image registration is completed. The multi-modal image registration performance can be effectively improved, and the method still has good robustness and adaptability especially under the condition that serious geometric distortion and significant modal difference exist.

Disclosed in the present invention are a multi-modal image registration method and system based on iterative optimization. The method comprises: acquiring several low-light images and processing same, in order to obtain a training set and a validation set; constructing a multi-modal image registration neural network model; training the multi-modal image registration neural network model by means of the training set, and using a loss function to calculate loss, in order to obtain a trained multi-modal image registration neural network model; using the validation set to perform iterative optimization on the trained multi-modal image registration neural network model, and determining whether an iteration termination condition is met, in order to obtain an iteratively optimized multi-modal image registration neural network model; and acquiring multi-modal images in a real scene, forming image pairs to be registered, and inputting said image pairs into the iteratively optimized multi-modal image registration neural network model for processing, in order to obtain registered and fused images. The method can improve the robustness of image pairs, which each consist of a reference image and an image to be matched, during a registration process.

The application discloses a kind of guided path planning method and system.Method includes following steps:S1.MRI image and ultrasound image are obtained, multi-modal image registration and segmentation are carried out, and MRI image and ultrasound image are fused.S2.real-time modeling of electric field distribution, based on dynamic conductivity model and finite element method, the electric field intensity distribution of cartilage and surrounding tissue after electrode applied pulse is calculated in real time, to provide input for subsequent pulse parameter optimization and path planning.S3.pulse parameter adaptive optimization, through model predictive control algorithm, real-time adjustment pulse voltage and pulse width, so that the electric field intensity of ablation area accurately covers target cartilage, while protecting blood vessel / nerve sensitive area from damage.S4.restrained path search is carried out, and guided path planning is realized.

A method for aligning multiple depth cameras in an environment based on image data can include accessing, by one or more processors, a first plurality of point cloud data points corresponding to a first pose for a subject and a second plurality of point cloud data points corresponding to a second pose for the subject. The method can include determining, by the one or more processors, a frame of reference for the image data based on at least one of the first pose or the second pose. The method can include transforming and aligning, by the one or more processors, at least one of the first plurality of point cloud data points or the second plurality of point cloud data points to the frame of reference.

The disclosure generally relates to methods and systems for image co-registration of multi-modal temporal sensing. Conventional techniques for image co-registration of multi-modality images focus on either the spatial or temporal domain and thus are not of high accuracy and do not preserve both global and local characteristics for the matching. The present disclosure solves the technical problems in the art for image co-registration of multi-modal temporal sensing using a deep-learning based multi-input-output encoder-decoder network with a Gabor Jet Model. The deep-learning based multi-input-output encoder-decoder network is utilized for the feature extraction. A distinctive Gabor-jet layer of the Gabor Jet Model is utilized for the similarity matching. The Gabor-jet layer generates a Gabor jet graph which provides sparse feature points for matching between matching images and reference images.

The invention discloses an equipment fault source positioning method and device based on multi-modal information fusion, and the method comprises the steps: carrying out the data collection of a power distribution room inspection robot which carries an infrared thermal imager, a visible light camera and a self-adaptive lifting mechanism, carrying out the preprocessing of a collected infrared image, constructing a power distribution room equipment data set, and carrying out the positioning of a power distribution room equipment fault source. The power distribution room equipment data set comprises a plurality of image pairs composed of infrared images and visible light images which are in one-to-one correspondence; performing multi-modal image registration on each image pair based on an improved whale optimization algorithm to obtain a registered image pair; fusing the registered image pair by using a deep learning fusion network containing a coordinate attention module to obtain a fused image; and carrying out fault source positioning based on the relative temperature difference and a Monte Carlo method according to the fused image. According to the invention, accurate identification and grade determination of a fault source are realized through four stages of processes of multi-modal data acquisition, accurate image registration, high-quality image fusion and automatic fault positioning.

The invention discloses an automatic multi-modal image registration method and system. The method comprises the following steps: initializing and preprocessing a registration image; extracting feature points of the registered image, and calculating descriptors; matching descriptors of the registered image pairs; calculating an affine matrix; and performing affine matrix processing on the to-be-registered image to obtain a registered image. According to the method, the registration problem of an endogenous imaging technology, a light field imaging technology, a tissue slicing technology and the like which cannot be solved in the prior art can be solved at the same time, fine adjustment is not needed for different modes, zero sample learning can be achieved, and the method can be suitable for imaging conditions of different scales from mesoscopic imaging to microscopic imaging and the like.

The application discloses a kind of based on common feature and imaging parameter joint guiding image registration method and system, the method of the present application includes extracting deep feature map to target modality and reference modality, after projection to unified structure representation space, using bidirectional interactive attention mechanism carries out geometric alignment to obtain coarse registration transformation matrix, the coarse registration image of target modality and the input image of reference modality are respectively using depth estimation network to predict dense depth map, dense depth map is used to construct three-dimensional point cloud graph using imaging parameter, and fine registration transformation matrix is calculated to the three-dimensional point cloud graph of target modality and reference modality;Coarse registration transformation matrix and fine registration transformation matrix are fused to obtain the final registration transformation matrix, and the input image of target modality is used to generate the final coarse registration image of target modality using the final registration transformation matrix.The present application aims to solve the problem that the existing multi-modal image registration method is not good in complex scene registration effect.

The invention discloses a multi-modal image unsupervised registration method based on alternative optimization training. The method comprises the following steps: firstly, simulating to generate a same-mode image pair with a random deformation truth value; during training, the mode conversion module converts a source image mode into a target image mode, and the homography estimation registration module carries out deformation prediction on a to-be-registered image pair. The homography estimation registration module uses the source image pair and the target image pair which are subjected to mode conversion, and self-supervised training is carried out by utilizing a simulation deformation truth value. The mode conversion module enables the source image to be approximately aligned with the target image through the homography estimation registration module, and supervises training through perception loss. And carrying out verification enhancement training on the two modules in combination with additional simulation deformation, and optimizing the training effect. In the training process of the homography estimation registration module and the modal conversion module, gradient truncation and weight updating are independent and alternately optimized. According to the invention, unsupervised registration of multi-modal images can be realized, dependence on manual annotation data is reduced, and the precision of multi-modal image registration is improved.

The present application relates to a kind of multi-modal medical image flexible registration algorithm based on multi-frame intelligent agent, based on reinforcement learning design a new end-to-end multi-modal image registration method, by soft actor-critic algorithm SAC driven training, can imitate the step-by-step registration process of human expert, improve the accuracy of high-dimensional registration action.In view of the extremely complex multi-modal environment, there are severe challenges in pixel-level control in three-dimensional space, the present application combines reinforcement learning with planner network, encourages artificial agent to learn more accurate registration action explicitly from the state frame that has been generated, overcomes the challenge from multi-modal and high-dimensional continuous action space with the advantage of space-time dimension, avoids introducing deep neural network with huge amount of parameters, and has strong robustness and generalization ability, can drive model to twist and move image in the correct direction.

The invention relates to a medical image processing technology, and discloses a multi-modal image registration method and system based on representation decoupling. Respectively coding a moving image and a fixed image through a dual-branch multi-scale coding network with independent parameters, and extracting modal-independent features through a multilayer modal-independent contrast loss constraint in training; the features of different scales are input into cascaded multi-scale feature registration modules, registration is carried out step by step according to the scales from small to large, and each module receives the deformation field and the deformation feature of the previous scale, outputs the deformation field and the deformation feature of the current scale and transmits the deformation field and the deformation feature backwards; finally, the deformation field acts on the moving image to obtain a registration result. According to the multi-modal medical image registration method and device, modal irrelevant feature extraction is achieved through double-branch independent coding and multi-layer comparison loss constraint cooperation, error accumulation is relieved through deformation field and deformation feature joint transmission, and the precision and stability of multi-modal medical image registration are remarkably improved.

The invention relates to an infrared-visible light image fusion method based on a deformation field, a storage medium and electronic equipment. The infrared-visible light image fusion method comprises the following steps: acquiring an infrared image and a visible light image of a high-voltage switch cabinet; processing the visible light image by adopting a cross-modal perception style migration network in the multi-modal image registration model to generate a pseudo-infrared image; inputting the pseudo infrared image and the infrared image into a multistage refined registration network of the model to generate a distortion displacement vector deformation field, and performing registration reconstruction on the infrared image to obtain a registered infrared image; and respectively inputting the registered infrared image and visible light image into a feature extraction network, extracting features, fusing the features by adopting a multi-modal image fusion network, and obtaining and outputting a fused image. According to the invention, the deformation field is generated through the pseudo-infrared image to realize the registration and fusion of the infrared and visible light images, the method is constructed and applied to the infrared and visible light image fusion of the switch cabinet, the image registration and fusion are automatically realized, and the manual workload is reduced.

The invention discloses an EIT region segmentation and quantitative analysis method and system based on ultrasonic anatomy calibration, and belongs to the field of medical image and respiratory physiology monitoring. Processing the ultrasound image to identify anatomical markers such as a rib line, a pleural line and a diaphragm line; performing multi-modal image registration based on the physical coordinates and the common anatomical anchor points; mapping the anatomical landmark to an EIT grid; performing hierarchical constraint region segmentation on the EIT grid; and calculating an impedance average value and a tidal impedance variation based on a segmentation result. According to the method, individualized accurate region segmentation of the EIT image is achieved through ultrasonic anatomical structure calibration, data pollution is avoided, the EIT can provide a quantitative report with a clear anatomical identifier, and the application value and reliability of the EIT in clinical scenes such as pulmonary ventilation efficiency evaluation, pulmonary edema monitoring and pleural effusion quantification are greatly improved.

The invention discloses an image registration method and system based on common feature and imaging parameter joint guidance, and the method comprises the steps: extracting depth feature maps from a target mode and a reference mode, projecting the depth feature maps to a unified structure representation space, and carrying out the geometric alignment through a bidirectional interactive attention mechanism, so as to obtain a coarse registration transformation matrix; respectively using a depth estimation network to predict dense depth maps of the coarse registration image of the target modal and the input image of the reference modal, using imaging parameters to construct a three-dimensional point cloud map for the dense depth maps, and calculating a fine registration transformation matrix for the three-dimensional point cloud maps of the target modal and the reference modal; and fusing the coarse registration transformation matrix and the fine registration transformation matrix to obtain a final registration transformation matrix, and generating a final coarse registration image of the target modal from the input image of the target modal by using the final registration transformation matrix. The invention aims to solve the problem that an existing multi-modal image registration method is poor in registration effect in a complex scene.

The application discloses a kind of based on deformation adaptation multi-modal image registration method, system and computer equipment, comprising: acquisition multiple multi-modal images and carries out gray standardization, constructs diversified registration dataset;Build registration network model including pyramid coding module, deformation self-adapting module, cross-modal interaction module and registration parameter estimation module;Image pair is sequentially input into the above module, respectively extract basic feature mapping, deformation feature mapping and interactive enhanced feature mapping, finally output estimated conversion parameter matrix;Through preset loss function supervision training process, select optimal network parameter, obtain trained registration model;When practical application, the image pair to be registered is input into trained model, obtains conversion parameter matrix and completes image registration.Can effectively improve multi-modal image registration performance, especially in the case of severe geometric distortion and significant modal difference, still have good robustness and adaptability.

The invention provides a prostate cancer radical operation navigation method, device and system based on PSMA-PET / MRI and ultrasonic multi-mode fusion, a medium and a terminal, and the method comprises the steps: obtaining a PSMA-PET / MRI image collected before an operation, and drawing a prostate contour and a focus contour on the obtained PSMA-PET / MRI image; the method comprises the following steps: acquiring an ultrasonic image acquired in real time during an operation, and drawing a prostate contour on the acquired ultrasonic image; performing multi-modal image registration on the PSMA-PET / MRI image and the ultrasonic image by taking the prostate contour drawn on the PSMA-PET / MRI image and the ultrasonic image as a reference, and fusing the focus contour drawn on the PSMA-PET / MRI image after registration to the ultrasonic image to obtain a multi-modal fusion image; and according to the obtained multi-mode fusion image, carrying out real-time guiding prostate cancer radical treatment operation. According to the application, accurate identification and accurate positioning of the focus can be realized in the prostatic cancer radical operation, so that the long-term contradiction between function retention of the prostatic cancer radical operation and tumor radical operation is solved.

The invention discloses a senile dementia related multi-contrast brain structure magnetic resonance imaging analysis method which comprises the following steps: acquiring multi-contrast brain structure magnetic resonance imaging data including T1 weighted imaging, T2 weighted imaging, proton density weighted imaging PD, liquid attenuation inversion recovery FLAIR and magnetic sensitivity weighted imaging SWI; constructing a model for multi-contrast brain structure magnetic resonance imaging analysis, wherein the model comprises an image denoising module, an image registration module and an image segmentation module; inputting the acquired magnetic resonance imaging data into the trained model; in the first step, noise suppression images corresponding to all contrast ratios are output, in the second step, multi-modal image registration is carried out, contrast ratio alignment images of unified voxel grids are generated, and in the third step, a final segmentation probability graph and a corresponding tissue label are output. By using the system and the method, high-quality, automatic and clinically deployable nuclear magnetic resonance imaging (MRI) brain image analysis is realized. The method can be widely applied to the field of medical image processing.

An AI-powered multimodal medical imaging system for the early detection of diseases, consisting of: a module for acquiring medical image data from various imaging modalities; a module for integrating clinical patient data into the acquired image datasets; a module for image preprocessing and enhancement to optimize image quality and normalize heterogeneous image datasets; a module for multimodal image registration and fusion for spatial alignment and fusion of image data from different imaging modalities; a module for lesion detection and anatomical segmentation to detect pathological anomalies and segment anatomical structures; and a module for radiomics and biomarker extraction to extract quantitative imaging biomarkers.a disease classification and prediction module for classifying diseases and generating predictive diagnostic outcomes using artificial intelligence algorithms; an explainable artificial intelligence module for generating interpretable diagnostic explanations; a clinical decision support and emergency response module for generating diagnostic recommendations and emergency alerts; and an adaptive learning, security, and cloud analytics module for continuously improving diagnostic performance while ensuring secure health data analytics and regulatory compliance.

This invention relates to the field of medical image processing, and provides a multimodal medical image registration method, apparatus, device, and medium. The method includes: S3, calculating the target point deviation between a first boundary point set and a second boundary point set; moving the target points of the second boundary point set to the target points of the first boundary point set according to the target point deviation, to obtain a third boundary point set; S4, defining a cost function, iteratively calculating the transformation dataset from the third boundary point set to the first boundary point set to minimize the cost function; S5, performing the transformation in S3 on all pixels in the second target image to obtain a transformation point set; selecting boundary points in the transformation point set and then performing the transformation dataset in S4; performing interpolation transformation based on boundary point transformation on the non-boundary points in the non-boundary point set to obtain the processing result; mapping the processing result onto a multimodal fused image to obtain a registered third target image. This method is used to reduce the degree of image mismatch during multimodal image registration.

The application relates to the technical field of image registration, in particular to a multi-modal image registration method based on deep learning, which comprises the following steps: acquiring two groups of initial images of different modalities, and acquiring a pre-trained convolutional neural network model; pre-processing the initial images, and taking the two groups of pre-processed initial images as fixed images and floating images respectively, wherein the pre-processing comprises denoising processing; inputting the fixed images and the floating images into the pre-trained convolutional neural network model to obtain a deformation field and transformation parameters between the fixed images and the floating images; and performing spatial transformation on the floating images according to the deformation field and the transformation parameters to obtain a registration result. The technical scheme provided by the application can improve the accuracy of the registration result.

The invention relates to the technical field of artificial intelligence, and discloses a medical image AI diagnostic analysis method and system and a storage medium. The method aims at solving the problems that in the prior art, due to the fact that equipment parameters are not uniform, image quality is unstable, multi-modal image registration is difficult, information fusion fails due to semantic gaps, model interpretability is poor, and a continuous optimization mechanism is lacked. The method comprises the steps that personalized imaging parameters are generated based on physiological parameters of a patient and an anatomical prior model, and scanning is controlled; and performing format normalization and metadata verification on the DR, CT and MRI images, and then performing rigid and non-rigid registration. According to the scheme, image acquisition standardization, multi-modal information complementary fusion, diagnosis process interpretability and model sustained evolution are achieved, the focus detection accuracy is remarkably improved, the risk of missed diagnosis and misdiagnosis is reduced, the report generation time is shortened to be within 38 seconds, focus area display is enhanced through eye movement tracking, and the film reading efficiency and clinical decision confidence are improved.

The invention discloses a few-sample multi-modal image unsupervised registration method based on modal adaptive optimization. The method is implemented based on a multi-modal registration network, and the construction method of the network comprises the steps that firstly, a multi-modal pre-training data set with rich modals is constructed, pre-training is conducted on the multi-modal registration network through the data set, and the multi-modal registration network comprises a modal invariant feature extraction module and a homography registration module; when few-sample multi-modal image data is processed, the initial pre-training weight of the network is frozen, a modal self-adaptive optimizer is introduced into a modal invariant feature extraction and homography registration module, and optimization training is carried out for specific few-sample data; unsupervised learning is realized through Gram matrix feature loss and self-supervised homography loss, and enhanced training is performed on a modal adaptive optimizer in combination with simulation deformation so as to improve the network adaptability and robustness. The method can be used for solving the problem of multi-modal image registration under the scene of sample missing or data acquisition difficulty.

The application relates to the field of image processing, and provides a multi-modal image registration verification method, which comprises the following steps: acquiring an optical coherence tomography (OCT) image and a near-infrared autofluorescence (NIRAF) image corresponding to the OCT image; detecting the OCT image and the NIRAF image according to a target detection network to obtain first position information and second position information; determining an offset angle according to the first position information and the second position information; performing registration processing on the NIRAF image according to the offset angle to obtain a first NIRAF image; and performing verification processing on the first NIRAF image according to an optical attenuation coefficient image of the OCT image to obtain a verification result, which is used for indicating whether the first NIRAF image is registered with the OCT image. The verification method can verify the registration result of the image.

The invention provides a registration method and device for a multi-modal image. The method comprises the steps that feature points are extracted from an infrared image and a visible light image to be registered through a binary neighborhood feature point extraction algorithm, and the feature points of the infrared image and the visible light image serve as centers; cutting the image to obtain an infrared image block and a visible light image block, and inputting the infrared image block and the visible light image block into a trained deep neural network to output a registration result; and eliminating wrong matching points in a registration result by using local constraint and global constraint to obtain a final registration result of the infrared image and the visible light image. Compared with the most advanced method, the multi-modal image registration method has the advantages that higher accuracy is realized, and the multi-modal image registration method has better performance in the aspect of multi-modal image registration.

The invention relates to an unsupervised multi-modal image registration and model construction method, and the method comprises the steps: constructing an unsupervised multi-modal image registration model, carrying out the geometric transformation of images of different modalities through a spatial transformation network, so as to achieve the registration, and converting the registered image into an image of a target modal through an image-to-image translation network. According to the invention, registration and conversion of multi-modal images can be automatically realized without pairing training data; according to the method, manual marking or complex feature engineering is not needed, automatic registration of the infrared camera and the visible light camera is achieved, the workload of operators is reduced through the automatic image registration process, efficiency is improved, the generated registration image has high quality and consistency, and follow-up analysis and decision making are facilitated.