Abstract: Systems and methods for determining parameters for image analysis are provided. One method includes obtaining ultrasound data of an object, generating an image of the object, and identifying a region of interest in the image. The method also includes determining a plurality of spatially varying parameters for image analysis of the region of interest using prior information for one or more objects of interest, including prior location information for the one or more objects of interest, and wherein the plurality of spatially varying parameters are determined for a plurality of sections of the region of interest and different for at least some of the plurality of sections. The method further includes using the plurality of spatially varying parameters for performing image analysis of the region of interest in the image to determine the location of the one or more objects of interest.

Abstract: A system and method for CT projection extrapolation are provided. The method comprises receiving a CT projection for extrapolation. The method also comprises selecting a target patch comprising at least one pixel of a row to be extrapolated. The method further comprises generating a correlation profile between the target patch and one or more source patches, wherein the source patches comprise measured pixels in the CT projection in one or more rows adjacent to the target patch. The projection data is generated for at least one pixel of the target patch based on the correlation profile and the measured pixels of at least one of the source patches.

Abstract: Systems and methods for determining parameters for image analysis are provided. One method includes obtaining ultrasound data of an object, generating an image of the object, and identifying a region of interest in the image. The method also includes determining a plurality of spatially varying parameters for image analysis of the region of interest using prior information for one or more objects of interest, including prior location information for the one or more objects of interest, and wherein the plurality of spatially varying parameters are determined for a plurality of sections of the region of interest and different for at least some of the plurality of sections. The method further includes using the plurality of spatially varying parameters for performing image analysis of the region of interest in the image to determine the location of the one or more objects of interest.

Abstract: A method and system for visualizing regions in an image is provided. The method comprises computing a regional response around a region in the image, deriving a region score based on from the regional response for the region and labeling the region in the image by comparing the region score to a plurality of probabilistic models.

Abstract: System for analyzing a vascular structure. The system includes an initialization module that is configured to analyze a slice of a VOI that includes a main vessel of the vascular structure to position first and second luminal models in the lumen. Each of the first and second luminal models represents at least a portion of a cross-sectional shape of the lumen and has a location and a dimension in the slice. The system also includes a tracking module that is configured to determine the locations and the dimensions of the first and second luminal models in subsequent slices. For a designated slice, the locations and the dimensions of the first and second luminal models of the designated slice are based on the locations and the dimensions of the first and second luminal models, respectively, in a prior slice and also the image data of the designated slice.

Abstract: A system and method for CT sinogram extrapolation are provided. The method comprises receiving a CT projection for extrapolation. The method also comprises selecting a target patch comprising at least one pixel of a row to be extrapolated. The method further comprises generating a correlation profile between the target patch and one or more source patches, wherein the source patches comprise measured pixels in the CT projection in one or more rows adjacent to the target patch. The projection data is generated for at least one pixel of the target patch based on the correlation profile and the measured pixels of at least one of the source patches.

Abstract: A method of automatic defect recognition includes receiving a initial set of inspection image data of a scanned object from a scanning machine; applying a first image analysis algorithm to this set of inspection image data; then removing from the set of inspection image data any defect-free image regions, so as to retain a set of analyzed inspection image data; applying an additional image analysis algorithm(s) to the set of analyzed inspection image data, wherein the additional algorithm(s) has a higher computational cost than the first image analysis algorithm; and based on the applying of the additional image analysis algorithm(s), removing from the first set of inspection image data a second set of defect-free image regions, thereby retaining a set of twice-analyzed inspection image data.

Abstract: A method for identifying defects in radiographic image data corresponding to a scanned object is provided. The method includes acquiring radiographic image data corresponding to a scanned object. In one embodiment, the radiographic image data includes an inspection test image and a reference image corresponding to the scanned object. The method includes identifying one or more regions of interest in the reference image and aligning the inspection test image with the regions of interest identified in the reference image, to obtain a residual image. The method further includes identifying one or more defects in the inspection test image based upon the residual image and one or more defect probability values computed for one or more pixels in the residual image.

Abstract: The present disclosure relates to the use of probabilistic models to classify elements of a medical image into different tissue types. The models may be based upon geometric abstractions of the different tissue types being classified. In addition, the present disclosure teaches the derivation and modification of models for tissue classification based upon the neighboring region of the voxels undergoing classification.

Abstract: An anomaly detection method includes acquiring image data corresponding to nondestructive testing (NDT) of a scanned object. The NDT image data comprises at least one inspection test image of the scanned object and multiple reference images for the scanned object. The anomaly detection method further includes generating an anomaly detection model based on a statistical analysis of one or more image features in the reference images for the scanned object and identifying one or more defects in the inspection test image, based on the anomaly detection model.

Abstract: A method that relates to mobile platform positioning through landmark recognition. The method includes receiving a captured image of a landmark, wherein the captured image was captured from an onboard camera system located on the mobile platform. A comparison of the captured image to a plurality of stored images, that are images of landmarks at known locations is made. The method recognizes the captured image based on the comparison and then determines the location of the mobile platform, based on the recognition. The method may be employed with a locomotive. A system and computer program product for mobile device positioning is also disclosed.

Abstract: Methods and systems for segmenting images, wherein the image pixels are categorized into a plurality of subsets using one or more indexes, then a log-likelihood function of one or more of the indexes is determined, and one or more maps are generated based on the determination of the log-likelihood function of one or more of the indexes.

Abstract: A method of automatic defect recognition includes receiving a initial set of inspection image data of a scanned object from a scanning machine; applying a first image analysis algorithm to this set of inspection image data; then removing from the set of inspection image data any defect-free image regions, so as to retain a set of analyzed inspection image data; applying an additional image analysis algorithm(s) to the set of analyzed inspection image data, wherein the additional algorithm(s) has a higher computational cost than the first image analysis algorithm; and based on the applying of the additional image analysis algorithm(s), removing from the first set of inspection image data a second set of defect-free image regions, thereby retaining a set of twice-analyzed inspection image data.

Abstract: The present disclosure relates to the use of probabilistic models to classify elements of a medical image into different tissue types. The models may be based upon geometric abstractions of the different tissue types being classified. In addition, the present disclosure teaches the derivation and modification of models for tissue classification based upon the neighboring region of the voxels undergoing classification.

Abstract: An anomaly detection method includes acquiring image data corresponding to nondestructive testing (NDT) of a scanned object. The NDT image data comprises at least one inspection test image of the scanned object and multiple reference images for the scanned object. The anomaly detection method further includes generating an anomaly detection model based on a statistical analysis of one or more image features in the reference images for the scanned object and identifying one or more defects in the inspection test image, based on the anomaly detection model.

Abstract: A method for identifying defects in radiographic image data corresponding to a scanned object is provided. The method includes acquiring radiographic image data corresponding to a scanned object. In one embodiment, the radiographic image data includes an inspection test image and a reference image corresponding to the scanned object. The method includes identifying one or more regions of interest in the reference image and aligning the inspection test image with the regions of interest identified in the reference image, to obtain a residual image. The method further includes identifying one or more defects in the inspection test image based upon the residual image and one or more defect probability values computed for one or more pixels in the residual image.

Abstract: A method of image reconstruction comprising: obtaining a Helmholtz reciprocal pair of images of an object, the images comprising a first image and a corresponding reciprocal image; determining an imaging geometry associated with the obtaining; selecting a plurality of points in the first image and identifying corresponding candidate points in the corresponding reciprocal image; matching a selected point of the plurality of points and a candidate point of the corresponding candidate points. A method of image registration with an object comprising: obtaining a Helmholtz reciprocal pair of images of an object, the Helmholtz reciprocal pair of images comprising a first image and a corresponding reciprocal image; estimating a pose for the object; predicting an estimated image corresponding to the pose and one image of the reciprocal pair of images; comparing the estimated image with a corresponding actual image from the pair of images; and refining the estimating a pose based on the comparing.

Abstract: A system for determining a track location operates to acquire a current video frame via at least one video camera mounted on board a locomotive, determine a track location based on information extracted from the at least one video frame, and transmit the track location information to a navigation system to determine control parameters for the locomotive.

Abstract: A method for calibrating a projective camera is provided. The method includes acquiring information by detecting at least one object on a substantially flat ground plane within a field of view. A projective camera calibration is performed. A measurement uncertainty is considered to yield a plurality of camera parameters from the projective camera calibration.

Abstract: A method and system for visualizing regions in an image is provided. The method comprises computing a regional response around a region in the image, deriving a region score based on from the regional response for the region and labeling the region in the image by comparing the region score to a plurality of probabilistic models.