3557 results about "Image registration" patented technology

Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Methods and systems for tracking a position and torsional orientation of a patient's eye. In one embodiment, the present invention provides methods and software for registering a first image of an eye with a second image of an eye. In another embodiment, the present invention provides methods and software for tracking a torsional movement of the eye. In a particular usage, the present invention tracks the torsional cyclorotation and translational movement of a patient's eye so as to improve the delivery of a laser energy to the patient's cornea.

A computer based system and method of visualizing a region using multiple image data sets is provided. The method includes acquiring first volumetric image data of a region and acquiring at least second volumetric image data of the region. The first image data is generally selected such that the structural features of the region are readily visualized. At least one control point is determined in the region using an identifiable structural characteristic discernable in the first volumetric image data. The at least one control point is also located in the at least second image data of the region such that the first image data and the at least second image data can be registered to one another using the at least one control point. Once the image data sets are registered, the registered first image data and at least second image data can be fused into a common display data set. The multiple image data sets have different and complimentary information to differentiate the structures and the functions in the region such that image segmentation algorithms and user interactive editing tools can be applied to obtain 3d spatial relations of the components in the region. Methods to correct spatial inhomogeneity in MR image data is also provided.

A “Keyframe Stitcher” provides an efficient technique for building mosaic panoramic images by registering or aligning video frames to construct a mosaic panoramic representation. Matching of image pairs is performed by extracting feature points from every image frame and matching those points between image pairs. Further, the Keyframe Stitcher preserves accuracy of image stitching when matching image pairs by utilizing ordering information inherent in the video. The cost of searching for matches between image frames is reduced by identifying “keyframes” based on computed image-to-image overlap. Keyframes are then matched to all other keyframes, but intermediate image frames are only matched to temporally neighboring keyframes and neighboring intermediate frames to construct a “match structure.” Image orientations are then estimated from this match structure and used to construct the mosaic. Matches between image pairs may be compressed to reduce computational overhead by replacing groups of feature points with representative measurements.

A virtual image is registered among a perceived real world background. Tracking light is scanned into the real world environment, which includes at least one detector pair. A first time and a second time at which the tracking light impinges on the first detector is detected, in which the first time and second time occurs within adjacent scan lines. A time at which a horizontal scan line edge (e.g., beginning of scan line or end of scan line) is encountered is derived as occurring one half way between the first time and the second time. The horizontal location of the first detector then is determined within a specific scan line inferring the scan line edge time. The vertical location of the detector is determined within a scan frame by measuring time duration using the beginning of the frame. By determining a location independently from the temporal resolution of the augmented imaging system, the temporal location of the detector is identified to a sub-pixel / sub-line precision. The augmented image is registered either to a 3D real world spatial coordinate system or to a time domain coordinate system based upon tracked position and orientation of the user.

A system and method for a medical intervention procedure within a cardiac chamber having an imaging system to obtain image data of the cardiac chamber and to create a 3D model from that image data, an interventional system to register the 3D model with a real-time image of the cardiac chamber and to display the 3D model, and an interventional tool positioned in the cardiac chamber to be displayed upon the interventional system and to be navigated in real-time over the registered 3D model. Preferably, the method and system also includes a storage medium to store the 3D model and wherein the interventional system receives the stored 3D model to register with the real-time image of the cardiac chamber.

The evolution of a skin condition over time can be useful in its assessment. In an illustrative arrangement, a user captures skin images at different times, using a smartphone. The images are co-registered, color-corrected, and presented to the user (or a clinician) for review, e.g., in a temporal sequence, or as one image presented as a ghosted overlay atop another. Image registration can employ nevi, hair follicles, wrinkles, pores, and pigmented regions as keypoints. With some imaging spectra, keypoints from below the outermost layer of skin can be used. Hair may be removed for image registration, and restored for image review. Transformations in addition to rotation and affine transforms can be employed. Diagnostic correlations with reference image sequences can be made, employing machine learning in some instances. A great variety of other features and arrangements are also detailed.

A method and system is presented for automatically selecting a region of interest (ROI) within an image of an object, for example when performing non-rigid image registration between the image and another image of the object. In this way, the need for user interaction can be minimized or eliminated during image registration. The ROI is determined by defining an entropy measure H of the image, and selecting the region within the image in which the entropy measure is maximized, In this way, the ROI is optimized to contain as much information as possible. In one embodiment, the entropy measure H is a modified Shannon entropy, defined by H=−ΣI P(I) log P(I), where I is the value of the image intensity level, and P(I) is the probability of an image intensity value I occurring within the ROI.

A vehicle-based navigation system is described including a position locator that establishes the geographic position of the vehicle and a map database located external to and remote from the vehicle. The system determines at least one of a vehicle parameter and a personal parameter, and transfers data from the remote map database to the vehicle and displays the data in the vehicle at a resolution that is a function of at least one of a vehicle parameter and a personal parameter.

An image registration process for detecting a change in position of a surgical target, such as a tumor, within a patient is disclosed. A pre-operative model of the target and surrounding area is generated, then registered to live patient images to determine or confirm a location of the target during the course of surgery. Image registration is based on a non-iterative image processing logic that compares a similarity measure for a template of the target (generated from the pre-operative model) at one location within the live image to other locations within the live image.

A method for determining correspondences between a first and a second image, comprising the steps providing a first image and a second image of the real environment, defining a warping function between the first and second image, determining the parameters of the warping function between the first image and the second image by means of an image registration method, determining a third image by applying the warping function with the determined parameters to the first image, determining a matching result by matching the third image and the second image, and determining correspondences between the first and the second image using the matching result and the warping function with the determined parameters. The method may be used in a keyframe based method for determining the pose of a camera based on the determined correspondences.

Methods and apparatus provide continuous guidance of endoscopy during a live procedure. A data-set based on 3D image data is pre-computed including reference information representative of a predefined route through a body organ to a final destination. A plurality of live real endoscopic (RE) images are displayed as an operator maneuvers an endoscope within the body organ. A registration and tracking algorithm registers the data-set to one or more of the RE images and continuously maintains the registration as the endoscope is locally maneuvered. Additional information related to the final destination is then presented enabling the endoscope operator to decide on a final maneuver for the procedure. The reference information may include 3D organ surfaces, 3D routes through an organ system, or 3D regions of interest (ROIs), as well as a virtual endoscopic (VE) image generated from the precomputed data-set. The preferred method includes the step of superimposing one or both of the 3D routes and ROIs on one or both of the RE and VE images. The 3D organ surfaces and routes may correspond to the surfaces and paths of a tracheobronchial airway tree extracted, for example, from 3D MDCT images of the chest.

An image registration process for detecting a change in position of a surgical target, such as a tumor, within a patient is disclosed. A pre-operative model of the target and surrounding area is generated, then registered to live patient images to determine or confirm a location of the target during the course of surgery. Image registration is based on a non-iterative image processing logic that compares a similarity measure for a template of the target (generated from the pre-operative model) at one location within the live image to other locations within the live image.

User interface systems and methods for roof estimation are described. Example embodiments include a roof estimation system that provides a user interface configured to facilitate roof model generation based on one or more aerial images of a building roof. In one embodiment, roof model generation includes image registration, image lean correction, roof section pitch determination, wire frame model construction, and / or roof model review. The described user interface provides user interface controls that may be manipulated by an operator to perform at least some of the functions of roof model generation. In one embodiment, the user interface provides user interface controls that facilitate the determination of pitch of one or more sections of a building roof. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.

An apparatus and method for communicating with a memory registration enabled adapter, such as an InfiniBand™ host channel adapter, are provided. With the apparatus and method, device driver services may be invoked by a device driver for initializing address translation entries in an address translation data structure of a root complex. An address of a device driver data buffer data structure and registration modifiers may be passed by the device driver to the device driver services. The device driver services may create address translation data structure entries in the address translation data structure associated with the root complex and memory registration (MR) address translation entries in a MR address translation data structure of the adapter. The MR address translation data structure may then be used with I / O operations to bypass the address translation data structure associated with the root complex.

A system for image registration and quantitative feature extraction for multiple image sets. The system includes an imaging workstation having a data processor and memory in communication with a database server in communication. The a data processor capable of inputting and outputting data and instructions to peripheral devices and operating pursuant to a software product and accepts instructions from a graphical user interface capable of interfacing with and navigating the imaging software product. The imaging software product is capable of instructing the data processor, to register images, segment images and to extract features from images and provide instructions to store and retrieve one or more registered images, segmented images, quantitative image features and quantitative image data and from the database server.

User interface systems and methods for roof estimation are described. Example embodiments include a roof estimation system that provides a user interface configured to facilitate roof model generation based on one or more aerial images of a building roof. In one embodiment, roof model generation includes image registration, image lean correction, roof section pitch determination, wire frame model construction, and / or roof model review. The described user interface provides user interface controls that may be manipulated by an operator to perform at least some of the functions of roof model generation. The user interface is further configured to concurrently display roof features onto multiple images of a roof. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.

Automated image on media registration and self-calibration using a built-in printing system duplex media path is enabled. The media sheet can be registered in a registration system and a calibration image can be placed on the media sheet after registration of the media. The media sheet can be inverted in the duplex media path and re-fed for detection of calibration image placement accuracy on the media sheet. The registration system can transmit the image registration placement data to a printer controller. Thereafter, the printer controller can adjust system settings to compensate for an image placement registration offset using a calibration algorithm. The invention enables system adjustments without requiring manual measurements or media transfer to an independent scanning device or adding complexity to printing systems.

A system and method for detecting changes by comparing images which cover the same physical area but are collected at different times, the system comprising: at least one input for inputting an image of a target area; the image of the target area having signatures representing outstanding features; at least one processor operating to divide the image of a target area into a plurality of target subimages; at least one memory comprising reference data comprising reference subimages taken at or near the target area at various times, the at least one processor operating to determine a sparse image representation from the reference data; the sparse image representation of the target data being a linear combination of reference data from corresponding reference subimages stored in the at least one memory; the at least one processor operating to compare the target image to the sparse image representation and to match signatures from the target image to the sparse image representation to register the images and perform change detection.

In part, the invention relates to processing, tracking and registering angiography images and elements in such images relative to images from an intravascular imaging modality such as, for example, optical coherence tomography (OCT). Registration between such imaging modalities is facilitated by tracking of a marker of the intravascular imaging probe performed on the angiography images obtained during a pullback. Further, detecting and tracking vessel centerlines is used to perform a continuous registration between OCT and angiography images in one embodiment.

The invention discloses a map merging method of unmanned aerial vehicle visual SLAM under city complex environment. The method comprises the steps of 1, collecting an image through an RGB-D camera installed on each unmanned aerial vehicle, utilizing the unmanned aerial vehicle to conduct pretreatment on the image, and then conducting image registration; 2, constructing a visual odometer, and achieving loop detection; 3, optimizing the posture of the unmanned aerial vehicle; 4, constructing an octomap map, and achieving real-time on-line SLAM; 5, transmitting the octomap into a ground computer, merging a local octomap into a global octomap, and then transmitting the merged all-region octomap to the unmanned aerial vehicle. According to the map merging method of the unmanned aerial vehicle visual SLAM under the city complex environment, calculated quantity is reduced, real-time on-line SLAM can be achieved, and hidden danger of information losses brought by unstable wireless transmission is reduced; meanwhile, the task execution time is shortened, the task execution efficiency is improved, hidden danger brought by insufficient unmanned aerial vehicle cruising ability, finally more precise positioning can be obtained, and a more precise map can be established.

The invention provides a remote sensing image registration method of a multi-source sensor, relating to an image processing technology. The remote sensing image registration method comprises the following steps of: respectively carrying out scale-invariant feature transform (SIFT) on a reference image and a registration image, extracting feature points, calculating the nearest Euclidean distances and the nearer Euclidean distances of the feature points in the image to be registered and the reference image, and screening an optimal matching point pair according to a ratio; rejecting error registration points through a random consistency sampling algorithm, and screening an original registration point pair; calculating distribution quality parameters of feature point pairs and selecting effective control point parts with uniform distribution according to a feature point weight coefficient; searching an optimal registration point in control points of the image to be registered according to a mutual information assimilation judging criteria, thus obtaining an optimal registration point pair of the control points; and acquiring a geometric deformation parameter of the image to be registered by polynomial parameter transformation, thus realizing the accurate registration of the image to be registered and the reference image. The remote sensing image registration method provided by the invention has the advantages of high calculation speed and high registration precision, and can meet the registration requirements of a multi-sensor, multi-temporal and multi-view remote sensing image.

The present invention provides methods of using current but incomplete data to prepare an approximated complete image of a patient potentially undergoing radiation therapy. A complete image of the patient is fused or aligned with a limited patient image using image registration techniques. The aligned image is converted to sinogram data. This sinogram data is compared to sinogram data corresponding to the limited patient image to determine what data exists beyond the scope of the limited sinogram. Any additional data is added to the limited data sinogram to obtain a complete sinogram. This complete sinogram is then reconstructed into an image that approximates the complete image that would have been taken at the time the limited image was obtained.