Abstract: A system and corresponding method for image acquisition are provided, the system including a processor, an imaging adapter in signal communication with the processor for receiving image data from each of a static imaging device and a dynamic imaging device, and a homography unit in signal communication with the processor for computing a planar homography between the static and dynamic image data; and the method including receiving an image from a static imaging device, receiving an image from a dynamic imaging device, and registering the dynamic image to the static image using planar homography.

Abstract: A computer readable medium is provided embodying instructions executable by a processor to perform a method for sparse volume segmentation for 3D scan of a target. The method including learning prior knowledge, providing volume data comprising the target, selecting a plurality of key contours of the image of the target, building a 3D spare model of the image of the target given the plurality of key contours, segmenting the image of the target given the 3D sparse model, and outputting a segmentation of the image of the target.

Abstract: A computer readable medium is provided embodying instructions executable by a processor to perform a method for sparse volume segmentation for 3D scan of a target. The method including learning prior knowledge, providing volume data comprising the target, selecting a plurality of key contours of the image of the target, building a 3D spare model of the image of the target given the plurality of key contours, segmenting the image of the target given the 3D sparse model, and outputting a segmentation of the image of the target.

Abstract: A method of segmenting tubular structures in digital images includes selecting a point in an image of a tubular object to be segmented, defining an initial state of the selected point, initializing measurement weights, a conditional probability distribution and a prior probability distribution of a feature space of the initial state, sampling the feature space from the prior probability distribution, estimating a posterior probability distribution by summing sample measurements weighted by the measurement weights, and segmenting a cross section of the tubular object from the posterior probability distribution.

Abstract: A method for segmenting an object of interest from an image of a patient having such object. Each one of a plurality of training shapes is distorted to overlay a reference shape with a parameter ?i being a measure of the amount of distortion required to effect the overlay. A vector of the parameters ?i is obtained for every one of the training shapes through the minimization of a cost function along with an estimate of uncertainty for every one of the obtained vectors of parameters ?i, such uncertainty being quantified as a covariance matrix ?i. A statistical model represented as {circumflex over (f)}H (?,?) is generated with the sum of kernels having a mean ?i and covariance ?i. The desired object of interest in the image of the patient is identified by positioning of the reference shape on the image and distorting the reference shape to overlay the obtained image with a parameter ? being a measure of the amount of distortion required to effect the overlay.

Abstract: A method of segmenting tubular structures in digital images comprises providing a digitized image, selecting a point within an object for segmenting in the image, defining an initial state of the selected point in the object, performing an initial segmentation of a 2D cross section of the object based on the initial state, predicting a new state of said object about a new point that is a translation of said selected point along the object tangent, correcting said new state prediction based on a measurement of said new point in said image, and segmenting a 2D cross section of said object based on said new state.

Abstract: A method for dynamic scene modeling and change detection applicable to motion analysis utilizes optical flow for capturing and modeling the dynamics of the scene. Uncertainties in the measurements are evaluated and utilized in order to develop a robust representation of the scene in a higher dimensional space. In another embodiment, a dynamical model of the scene is developed that utilizes multiple past frames to predict the next frame. Incremental methods for updating the model are developed and, towards detection of events, a new measure is introduced that is based on a state-driven comparison between the prediction and the actual observation.

Abstract: A method for detecting events in a video sequence includes providing a video sequence, sampling the video sequence at regular intervals to form a series of snapshots of the sequence, measuring a similarity of each snapshot, measuring a similarity change between successive pairs of snapshots, wherein if a similarity change magnitude is greater than a predetermined threshold, a change event has been detected, verifying the change event to exclude a false positive, and completing the processing of the snapshot incorporating the verified change event.

Abstract: A method for segmenting a sequence of images includes developing an autoregressive model using training data including segmented images of a same type as the sequence of images. The sequence of images showing a progression of a subject through a cycle is acquired. At least two images from the sequence of images are identified. A region of interest is manually segmented from the identified images. The manually segmented images are parameterized. The autoregressive model is adapted to the parameterized segmented images. The autoregressive model is used to perform segmentation on the region of interest for a plurality of images of the sequence of images.

Abstract: This invention relates to shape priors for level set representations. An embodiment of the invention comprises a first stage and a second stage. In the first stage, a shape model can be built directly on level set space using a collection of samples. The shape model can be constructed using a variational framework to create a non-stationary pixel-wise model that accounts for shape variabilities. Then, in the second stage, the shape model can be used as basis to introduce the shape prior in an energetic form. In terms of level set representations, the shape prior aims at minimizing non-stationary distance between the evolving interface and the shape model. An embodiment according to the present invention can be integrated with an existing, data-driven variational method to perform image segmentation for physically corrupted and incomplete data.

Abstract: A method for dynamic scene modeling and change detection applicable to motion analysis utilizes optical flow for capturing and modeling the dynamics of the scene. Uncertainties in the measurements are evaluated and utilized in order to develop a robust representation of the scene in a higher dimensional space. In another embodiment, a dynamical model of the scene is developed that utilizes multiple past frames to predict the next frame. Incremental methods for updating the model are developed and, towards detection of events, a new measure is introduced that is based on a state-driven comparison between the prediction and the actual observation.

Abstract: A method for dynamic scene modeling and change detection applicable to motion analysis utilizes optical flow for capturing and modeling the dynamics of the scene. Uncertainties in the measurements are evaluated and utilized in order to develop a robust representation of the scene in a higher dimensional space. In another embodiment, a dynamical model of the scene is developed that utilizes multiple past frames to predict the next frame. Incremental methods for updating the model are developed and, towards detection of events, a new measure is introduced that is based on a state-driven comparison between the prediction and the actual observation.

Abstract: A method of tracking an object comprises providing a plurality of cameras, determining an image from each camera, and determining a common plane in the images. The method further comprises determining a parallax for scene points across the images, incorporating the parallax as a feature in a background model, and estimating a change in the scene using the background model. A camera can be a pan-tilt-zoom camera. A camera can be uncalibrated.

Abstract: A method for boundary based image segmentation comprises segmenting an image, providing a level set representation of the segmentation for interaction, and providing an interactive edit of the level set representation. The method further comprises converting the interactive edit into a propagation constraint, and determining a segment according to the interactive edit and the level set representation.

Abstract: A method is provided for segmenting a moving object immersed in a background, comprising: obtaining a time-varying autoregressive model of prior motion of the object to predict future motion of the object; predicting a subsequent contour of the object from the background using the obtaining time-varying autoregressive model comprising using the obtained time-varying autoregressive model to initialize and/or constrain segmentation of the object from the background, and segmenting the object using the predicted subsequent contour and updating the autoregressive model while tracking of the segmented object.

Abstract: A system and method for non-rigid image registration using distance functions includes portions for receiving a source shape into an image space, integrating a global linear registration model with local deformations to assess the source shape, optimizing a functional defined on a parameter or feature space where the functional quantifies the similarity between the source shape and a target shape in terms of distance functions, creating an augmented registration space including a plurality of target shape clones coherently positioned in the image space, tracking moving interfaces between the source shape and the target shapes and clones through a level set method, and registering the source shape by seeking mutual correspondences between the source shape, the target shape and the target shape clones.

Abstract: A method for segmenting an object of interest from an image of a patient having such object. Each one of a plurality of training shapes is distorted to overlay a reference shape with a parameter ?i being a measure of the amount of distortion required to effect the overlay. A vector of the parameters ?i is obtained for every one of the training shapes through the minimization of a cost function along with an estimate of uncertainty for every one of the obtained vectors of parameters ?i, such uncertainty being quantified as a covariance matrix ?i. A statistical model represented as {circumflex over (ƒ)}H (?,?) is generated with the sum of kernels having a mean ?i and covariance ?i . The desired object of interest in the image of the patient is identified by positioning of the reference shape on the image and distorting the reference shape to overlay the obtained image with a parameter ? being a measure of the amount of distortion required to effect the overlay.

Abstract: This invention relates to shape priors for level set representations. An embodiment of the invention comprises a first stage and a second stage. In the first stage, a shape model can be built directly on level set space using a collection of samples. The shape model can be constructed using a variational framework to create a non-stationary pixel-wise model that accounts for shape variabilities. Then, in the second stage, the shape model can be used as basis to introduce the shape prior in an energetic form. In terms of level set representations, the shape prior aims at minimizing non-stationary distance between the evolving interface and the shape model. An embodiment according to the present invention can be integrated with an existing, data-driven variational method to perform image segmentation for physically corrupted and incomplete data.

Abstract: A method of segmenting tubular structures in digital images comprises providing a digitized image, selecting a point within an object for segmenting in the image, defining an initial state of the selected point in the object, performing an initial segmentation of a 2D cross section of the object based on the initial state, predicting a new state of said object about a new point that is a translation of said selected point along the object tangent, correcting said new state prediction based on a measurement of said new point in said image, and segmenting a 2D cross section of said object based on said new state.

Abstract: A system and method for particle filter based vessel segmentation are provided, the system including a processor, a Particle Filter unit in signal communication with the processor, and a Vessel Segmentation unit in signal communication with the processor; and the method including receiving image data for a vessel, initializing the vessel, modeling successive planes of the vessel as unknown states of a sequential process, and using a Particle Filter with a Monte Carlo sampling rule to propagate a plurality of segmentation hypotheses in parallel.