Abstract: A method of registering 3-dimensional digitized images to 2-dimensional digitized images during a medical procedure includes providing a pair of correctly-registered training images L={lr, lf} and their joint intensity distribution pl(ir, if), wherein ir and if are reference and floating images, respectively, providing a pair of observed images O={or, of} and their joint intensity distribution po(ir, if), mapping a marginal intensity distribution of the observed pair O={or, of} to a marginal intensity distribution of the training pair L={lr, lf}, and estimating a set of parameters T that registers image of to image or by maximizing a weighted sum of a Jensen-Shannon divergence (JSD) of a joint intensity distribution of the observed pair and a joint intensity distribution of the training pair and a similarity measure between the observed images.

Abstract: A method for imaging for cardiac catheter guidance comprises displaying a two-dimensional (2D) image of a heart, including a catheter; registering and blending the 2D image and a three-dimensional (3D) image of the heart to derive a blended image; displaying the blended image and the 3D image; and extracting an image of the catheter and inserting it into the 3D image.

Abstract: An intuitive user interface is provided for endoscopic view visualization. The user interface may permit individual or simultaneous navigation through images displayed in an endoscopic view window, a three dimensional overview window, and a multi-planar reformatted window. An operation performed directly on one of the windows may commence the navigation. The endoscopic view window may be divided into a plurality of sub-regions, each sub-region having a corresponding navigation function. The endoscopic view window and the three dimensional overview window may have corresponding icons related to a single location in corresponding images being displayed. The corresponding icons may be color coded to facilitate intuitive alignment of the corresponding images displayed in the different windows. The three dimensional overview window may have a geometric icon to facilitate intuitive alignment of the corresponding images.

Abstract: A method for aligning a pair of images includes providing a pair of images, identifying salient feature regions in both a first image and a second image, wherein each region is associated with a spatial scale, representing feature regions by a center point of each region, registering the feature points of one image with the feature points of the other image based on local intensities, ordering said feature pairs by a similarity measure, and optimizing a joint correspondence set of feature pairs by refining the center points to sub-pixel accuracy.

Abstract: A system and method for automatically registering a three dimensional (3D) pre-operative image of an anatomical structure with intra-operative electrophysiological (EP) points of a 3D electro-anatomical (EA) image map of the anatomical structure is disclosed. The pre-operative image is displayed in a first supporting view. The intra-operative EA image map is displayed in a second supporting view. An alignment of the pre-operative image with the intra-operative map is performed by identifying at least one corresponding point on each image. The view of the pre-operative image is integrated with the EA map based on the alignment.

Abstract: A multilevel image segmentation technique using graph cuts is disclosed. A reduced resolution image is generated from a full resolution image which is to be segmented. The reduced resolution image is then segmented in order to identify a boundary between an object and a background within the image. The identified boundary then identifies a portion of an increased resolution image which is segmented in order to refine the earlier identified boundary. The steps may be iterated for successively increasing image resolutions in order to refine the boundary as required by a particular application. An initial identification of object and background portions of the image may be provided as input by a user. Alternatively, a user may identify only the object portion, and the background portion may be automatically determined.

Abstract: A method and system for non-rigidly registering a fixed to a moving image utilizing a B-Spline based free form deformation (FFD) model is disclosed. The methodology utilizes sparse feature correspondences to estimate an elastic deformation field in a closed form. In a multi-resolution manner, the method is able to recover small to large non-rigid deformations. The resulting deformation field is globally smooth and guarantees one-to-one mapping between the images being registered.

Abstract: A method of aligning a pair of images with (101) a first image and a second image, wherein said images comprise a plurality of intensities corresponding to a domain of points in a D-dimensional space includes identifying (102) feature points on both images using the same criteria, computing (103) a feature vector for each feature point, measuring a feature dissimilarity (104) for each pair of feature vectors, wherein a first feature vector of each pair is associated with a first feature point on the first image, and a second feature vector of each pair is associated with a second feature point on the second image. A correspondence mapping (105) for each pair of feature points is determined using the feature dissimilarity associated with each feature point pair, and an image transformation (106) is defined to align (108) the second image with the first image using one or more pairs of feature points that are least dissimilar.

Abstract: A method of aligning a pair of images includes providing a pair of images with a first image and a second image, wherein the images comprise a plurality of intensities corresponding to a domain of points in a D-dimensional space. Salient feature regions are identified in both the first image and the second image, a correspondence between each pair of salient feature regions is hypothesized, wherein a first region of each pair is on the first image and a second region of each pair is on the second image, the likelihood of the hypothesized correspondence of each pair of feature regions is measured, and a joint correspondence is determined from a set of pairs of feature regions with the greatest likelihood of correspondence.