Abstract: A method (10) for respiratory motion compensation by applying principle component analysis (PCA) on cardiac imaging samples obtained using 2D/3D registration of a pre-operative 3D segmentation of the coronary arteries.

Abstract: Methods for analyzing biomedical data include: (a) obtaining macroscopic imaging data; (b) obtaining histopathological imaging data; (c) executing a parallel algorithm stored on a non-transient computer-readable medium to compute one or a plurality of network cycle features of a relative neighborhood graph derived from the histopathological imaging data; (d) registering the macroscopic imaging data and the histopathological imaging data; and (e) correlating the macroscopic imaging data and the network cycle features. Systems for analyzing biomedical data and computer readable storage media are described.

Abstract: An image mosaicking method includes performing pairwise registration of a plurality of tiles (101), determining absolute homographies for each of the plurality of tiles according to the pairwise registration (102B), and performing a blending of the plurality of tiles to obtain a stitched image according to the absolute homographies (103).

Abstract: A method for non-rigid registration of digital 3D coronary artery models with 2D fluoroscopic images during a cardiac intervention includes providing a digitized 3D centerline representation of a coronary artery tree that comprises a set of S segments composed of QS 3D control points, globally aligning the 3D centerline to at least two 2D fluoroscopic images, and non-rigidly registering the 3D centerline to the at least two 2D fluoroscopic images by minimizing an energy functional that includes a summation of square differences between reconstructed centerline points and registered centerline points, a summation of squared 3D registration vectors, a summation of squared derivative 3D registration vectors, and a myocardial branch energy. The non-rigid registration of the 3D centerline is represented as a set of 3D translation vectors rs,q that are applied to corresponding centerline points xs,q in a coordinate system of the 3D centerline.

Abstract: A method for segmenting an image includes registering an annotated template image to an acquired reference image using only rigid transformations to define a transformation function relating the annotated template image to the acquired reference image. The defined transformation function is refined by registering the annotated template image to the acquired reference image using only affine transformations. The refined transformation function is further refined by registering the annotated template image to the acquired reference image using only multi-affine transformations. The twice refined transformation function is further refined by registering the annotated template image to the acquired reference image using deformation transformations.

Abstract: A method for tracking coronary artery motion includes constructing (11) a centerline model of a vascular structure in a base phase image in a sequence of 2D images of coronary arteries acquired over a cardiac phase, computing (12), for each pixel in a region-of-interest in each subsequent image, a velocity vector that represent a change in position between the subsequent image and base phase image, calculating (13) positions of control points in each phase using the velocity vectors, and applying (14) PCA to a P×2N data matrix XT constructed from position vectors (x, y) of N centerline control points for P phases to identify d eigenvectors corresponding to the largest eigenvalues of XXT to obtain a d-dimensional linear motion model {circumflex over (?)}p, in which a centerline model for a new image at phase p+1 is estimated by adding {circumflex over (?)}p to each centerline control point of a previous frame at phase p.

Abstract: A method for compensating respiratory motion in coronary fluoroscopic images includes finding a set of transformation parameters of a parametric motion model that maximize an objective function that is a weighted normalized cross correlation function of a reference image acquired at a first time that is warped by the parametric motion model and a first incoming image acquired at a second time subsequent to the first time. The weights are calculated as a ratio of a covariance of the gradients of the reference image and the gradients of the first incoming image with respect to a root of a product of a variance of the gradients of the reference image and the variance of the gradients of the first incoming image. The parametric motion model transforms the reference image to match the first incoming image.

Abstract: Methods for analyzing biomedical data include: (a) obtaining macroscopic imaging data; (b) obtaining histopathological imaging data; (c) executing a parallel algorithm stored on a non-transient computer-readable medium to compute one or a plurality of network cycle features of a relative neighborhood graph derived from the histopathological imaging data; (d) registering the macroscopic imaging data and the histopathological imaging data; and (e) correlating the macroscopic imaging data and the network cycle features. Systems for analyzing biomedical data and computer readable storage media are described.

Abstract: An image mosaicking method includes performing pairwise registration of a plurality of tiles (101), determining absolute homographies for each of the plurality of tiles according to the pairwise registration (102B), and performing a blending of the plurality of tiles to obtain a stitched image according to the absolute homographies (103)

Abstract: A method for performing deformable non-rigid registration of 2D and 3D images of a vascular structure for assistance in surgical intervention includes acquiring 3D image data. An abdominal aorta is segmented from the 3D image data using graph-cut based segmentation to produce a segmentation mask. Centerlines are generated from the segmentation mask using a sequential topological thinning process. 3D graphs are generated from the centerlines. 2D image data is acquired. The 2D image data is segmented to produce a distance map. An energy function is defined based on the 3D graphs and the distance map. The energy function is minimized to perform non-rigid registration between the 3D image data and the 2D image data. The registration may be optimized.

Abstract: A medical imaging system to segment an original blood vessel of a body part represented by an original medical image is provided. The system includes an image analyzer for receiving and the original medical image to analyze the original medical image to provide a Hessian Eigen analysis comprising a first data and a second data mapped to each pixel of the medical image, and an image identifier for receiving the Hessian Eigen analysis and for identifying seed points from the pixels by processing the first data and the second data along with a vesselness property, wherein the seed points are used for segmenting the original blood vessel to provide a corrected medical image representing a corrected blood vessel.

Abstract: A method for registering 2-dimensional (2D) images with 3-dimensional (3D) images includes receiving a 2D reference image and a 3D moving image, initializing a registration parameter matrix that rigidly transforms the domain of the moving image, randomly sampling a set of registration parameter matrices in a neighborhood of the initial registration parameters, estimating a cost function for each of the randomly sampled parameter matrices, calculating a distance from each randomly sampled parameter matrix to the initial registration parameter matrix, calculating a mean shift vector from the estimated cost functions and distance, and updating the initial registration parameter matrix from the mean shift vector.

Abstract: A method for tracking coronary artery motion includes constructing (11) a centerline model of a vascular structure in a base phase image in a sequence of 2D images of coronary arteries acquired over a cardiac phase, computing (12), for each pixel in a region-of-interest in each subsequent image, a velocity vector that represent a change in position between the subsequent image and base phase image, calculating (13) positions of control points in each phase using the velocity vectors, and applying (14) PCA to a P×2N data matrix XT constructed from position vectors (x, y) of N centerline control points for P phases to identify d eigenvectors corresponding to the largest eigenvalues of XXT to obtain a d-dimensional linear motion model {circumflex over (?)}p, in which a centerline model for a new image at phase p+1 is estimated by adding {circumflex over (?)}p to each centerline control point of a previous frame at phase p.

Abstract: A method for non-rigid registration of digital 3D coronary artery models with 2D fluoroscopic images during a cardiac intervention includes providing a digitized 3D centerline representation of a coronary artery tree that comprises a set of S segments composed of QS 3D control points, globally aligning the 3D centerline to at least two 2D fluoroscopic images, and non-rigidly registering the 3D centerline to the at least two 2D fluoroscopic images by minimizing an energy functional that includes a summation of square differences between reconstructed centerline points and registered centerline points, a summation of squared 3D registration vectors, a summation of squared derivative 3D registration vectors, and a myocardial branch energy. The non-rigid registration of the 3D centerline is represented as a set of 3D translation vectors rs,q that are applied to corresponding centerline points xs,q in a coordinate system of the 3D centerline.

Abstract: A medical imaging system for processing an original medical image is provided. The system includes a seed generator for receiving the original medical image representing an original blood vessel and to generate coarse seeds on a basis of vesselness feature in respect to every pixel of the original blood vessel represented by the medical image, and a seed processor for receiving the coarse seeds and for processing the coarse seeds to select a set of refined seeds on a basis of the regression profile for each of the coarse seed by using random forest classification, such that the set of refined seeds are selected from the coarse seeds such that the set of refined seeds adapted to lie on a corrected blood vessel.

Abstract: A medical imaging system for processing an original medical image is provided. The system includes a seed generator for receiving the original medical image representing an original blood vessel and to generate coarse seeds on a basis of vesselness feature in respect to every pixel of the original blood vessel represented by the medical image, and a seed processor for receiving the coarse seeds and for processing the coarse seeds to select a set of refined seeds on a basis of the regression profile for each of the coarse seed by using random forest classification, such that the set of refined seeds are selected from the coarse seeds such that the set of refined seeds adapted to lie on a corrected blood vessel.

Abstract: A medical imaging system to segment an original blood vessel of a body part represented by an original medical image is provided. The system includes an image analyzer for receiving and the original medical image to analyze the original medical image to provide a Hessian Eigen analysis comprising a first data and a second data mapped to each pixel of the medical image, and an image identifier for receiving the Hessian Eigen analysis and for identifying seed points from the pixels by processing the first data and the second data along with a vesselness property, wherein the seed points are used for segmenting the original blood vessel to provide a corrected medical image representing a corrected blood vessel.

Abstract: A method for compensating respiratory motion in coronary fluoroscopic images includes finding a set of transformation parameters of a parametric motion model that maximize an objective function that is a weighted normalized cross correlation function of a reference image acquired at a first time that is warped by the parametric motion model and a first incoming image acquired at a second time subsequent to the first time. The weights are calculated as a ratio of a covariance of the gradients of the reference image and the gradients of the first incoming image with respect to a root of a product of a variance of the gradients of the reference image and the variance of the gradients of the first incoming image. The parametric motion model transforms the reference image to match the first incoming image.

Abstract: A method for segmenting an image includes registering an annotated template image to an acquired reference image using only rigid transformations to define a transformation function relating the annotated template image to the acquired reference image. The defined transformation function is refined by registering the annotated template image to the acquired reference image using only affine transformations. The refined transformation function is further refined by registering the annotated template image to the acquired reference image using only multi-affine transformations. The twice refined transformation function is further refined by registering the annotated template image to the acquired reference image using deformation transformations.

Abstract: A method for registering 2-dimensional (2D) images with 3-dimensional (3D) images includes receiving a 2D reference image and a 3D moving image, initializing a registration parameter matrix that rigidly transforms the domain of the moving image, randomly sampling a set of registration parameter matrices in a neighborhood of the initial registration parameters, estimating a cost function for each of the randomly sampled parameter matrices, calculating a distance from each randomly sampled parameter matrix to the initial registration parameter matrix, calculating a mean shift vector from the estimated cost functions and distance, and updating the initial registration parameter matrix from the mean shift vector.