Abstract: A method (100) of processing myocardial MR perfusion images that corrects imaging errors arising from myocardial motion and B-1 field inhomogeneity (115-135); segments the myocardium images (140, 145); and calculates perfusion measures that enable analysis of the segmented myocardium images (150).

Abstract: A method for clinical parameter derivation and adaptive flow acquisition within a sequence of magnetic resonance images includes commencing an acquisition of a sequence of images. One or more landmarks are automatically detected from within one or more images of the sequence of images. The detected one or more landmarks are propagated across subsequent images of the sequence of images. A plane is fitted to the propagation of landmarks. The positions of landmarks or alternatively the position of the fitted plane within the sequence of images is used for derivation of clinical parameters such as tissue velocities and/or performing adaptive flow acquisitions to measure blood flow properties.

Abstract: A method for processing an object in image data includes the steps of drawing a contour on a pre-segmentation of an object in image data, generating at least one seed point on the pre-segmentation from an intersection of the contour and the pre-segmentation, providing a weighting factor between the seed points and the pre-segmentation, and segmenting the pre-segmentation using the seed points and the weighting factor to generate a new pre-segmentation.

Abstract: In a method for image data acquisition of a region of interest in a subject with a magnetic resonance device, wherein, to establish the field of view, a minimal geometric shape encompassing the subject to be acquired and/or the surface of the subject is determined automatically from previously acquired localizer exposures as aliasing information for each exposure, at least one slice plane is determined for the acquisition of the region, and the phase coding direction and/or the extent of the field of view in the phase coding direction is determined for every slice plane using the aliasing information.

Abstract: A method for automatically selecting a number of Gaussian modes for segmentation of a cardiac magnetic resonance (MR) image, including: identifying a left ventricle (LV) in a cardiac MR image slice; quantifying the LV blood pool; obtaining a mask for the LV blood pool; generating a ring mask for a myocardium of the LV from the LV blood pool mask; fitting three Gaussian modes to a histogram of the image slice to obtain a corresponding homogeneity image for the myocardium; computing a quality of fitting (QOF) measure for the three Gaussian modes based on the corresponding homogeneity image; repeating the fitting and computing steps for four and five Gaussian modes; and selecting the homogeneity image of the number of Gaussian modes with the largest QOF measure as the homogeneity image for processing.

Abstract: A general purpose method to segment any kind of lesions in 3D images is provided. Based on a click or a stroke inside the lesion from the user, a distribution of intensity level properties is learned. The random walker segmentation method combines multiple 2D segmentation results to produce the final 3D segmentation of the lesion.

Abstract: A method for cardiac segmentation in magnetic resonance (MR) cine data, includes providing a time series of 3D cardiac MR images acquired at a plurality of phases over at least one cardiac cycle, in which each 3D image includes a plurality of 2D slices, and a heart and blood pool has been detected in each image. Gray scales of each image are analyzed to compute histograms of the blood pool and myocardium. Non-rigid registration deformation fields are calculated to register a selected image slice with corresponding slices in each phase. Endocardium and epicardium gradients are calculated for one phase of the selected image slice. Contours for the endocardium and epicardium are computed from the gradients in the one phase, and the endocardium and epicardium contours are recovered in all phases of the selected image slice. The recovered endocardium and epicardium contours segment the heart in the selected image slice.

Abstract: A system and method for extracting an object of interest from an image using a robust active shape model are provided. A method for extracting an object of interest from an image comprises: generating an active shape model of the object; extracting feature points from the image; and determining an affine transformation and shape parameters of the active shape model to minimize an energy function of a distance between a transformed and deformed model of the object and the feature points.

Abstract: A system and method are provided for contrast-invariant registration of images, the system including a processor, an imaging adapter or a communications adapter for receiving an image data sequence, a user interface adapter for selecting a reference frame from the image sequence or cropping a region of interest (ROI) from the reference frame, a tracking unit for tracking the ROI across the image sequence, and an estimation unit for segmenting the ROI in the reference frame or performing an affine registration for the ROI; and the method including receiving an image sequence, selecting a reference frame from the image sequence, cropping a region of interest (ROI) from the reference frame, tracking the ROI across the image sequence, segmenting the ROI in the reference frame, and performing an affine registration for the ROI.

Abstract: A method for automatic femur segmentation and condyle line detection. The method includes: scanning a knee of a patient with medical imaging equipment to obtain 3D imaging data with such equipment; processing the obtained 3D imaging data in a digital processor to determine two lines tangent to the bottom of the knee condyles in an axial and a coronal plane; and automatically scanning the patient in the defined plane. The processing includes: determining an approximate location of the knee; using the determined the location to define a volume of interest; segmenting the femur in the defined volume of interest; and determining a bottom point on the femur portion on a right side and a left side of the segmented femur in an axial and a coronal slice to determine the two lines.

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 for automatically selecting a number of Gaussian modes for segmentation of a cardiac magnetic resonance (MR) image, including: identifying a left ventricle (LV) in a cardiac MR image slice; quantifying the LV blood pool; obtaining a mask for the LV blood pool; generating a ring mask for a myocardium of the LV from the LV blood pool mask; fitting three Gaussian modes to a histogram of the image slice to obtain a corresponding homogeneity image for the myocardium; computing a quality of fitting (QOF) measure for the three Gaussian modes based on the corresponding homogeneity image; repeating the fitting and computing steps for four and five Gaussian modes; and selecting the homogeneity image of the number of Gaussian modes with the largest QOF measure as the homogeneity image for processing.

Abstract: A method for automatically localizing left ventricle in medical image data includes acquiring a sequence of three-dimensional medical images spanning a cardiac cycle. Each of the images includes a plurality of two-dimensional image slices, one of which is defined as a template slice. The template slice of each medical image of the sequence is automatically cropped to include the heart and a margin around the heart based on temporal variations between pixels of the template slice throughout the sequence of medical images. The template slice of each medical image of the sequence is automatically contoured to determine the endo-cardial and epi-cardial boundaries for at least the end-diastolic and end-systolic phases. Localization information is generated for the left ventricle based on the determined endo-cardial and epi-cardial boundaries for at least the end-diastolic and end-systolic phases.

Abstract: A method for automatically determining a field of view for performing a subsequent medical imaging study includes acquiring one or more preliminary images. A body mask is generated by thresholding the preliminary images and identifying a largest connected component. A boundary mask is obtained from the boundary of the generated body mask. A rectangular bounding box is fit to the obtained boundary mask. The rectangular bounding box is used as a field of view for performing a subsequent medical imaging study.

Abstract: A system and method are provided for contrast-invariant registration of images, the system including a processor, an imaging adapter or a communications adapter for receiving an image data sequence, a user interface adapter for selecting a reference frame from the image sequence or cropping a region of interest (ROI) from the reference frame, a tracking unit for tracking the ROI across the image sequence, and an estimation unit for segmenting the ROI in the reference frame or performing an affine registration for the ROI; and the method including receiving an image sequence, selecting a reference frame from the image sequence, cropping a region of interest (ROI) from the reference frame, tracking the ROI across the image sequence, segmenting the ROI in the reference frame, and performing an affine registration for the ROI.

Abstract: An exemplary embodiment of the present invention includes a method of detecting a left ventricle blood pool. The method includes: localizing a region of interest (ROI) in a three-dimensional temporal (3D+T) image based on motion information of each slice of the 3D image over time, thresholding the ROI to determine pixels of the ROI that correspond to blood, extracting connected components from the determined pixels, clustering the extracted connected components into groups based on criteria that are indicative of a blood pool of a left ventricle, and selecting one of the groups as the left ventricle blood pool.

Abstract: An image editing system comprises an input device for inputting an image, a graphical user interface for selecting background and object seeds for the image, and an image processor for editing the image. The image processor has various editing routines, including a segmentation routine that builds a graph associated with the image and uses a graph cut algorithm to cut the graph into segments. The user marks certain pixels as “object” or “background” to provide hard constraints for segmentation. Additional soft constraints incorporate both boundary and regional information. Graph cuts are used to find the globally optimal segementation of the image. The obtained solution gives the best balance of boundary and region properties satisfying the constraints.

Abstract: A general purpose method to segment any kind of lesions in 3D images is provided. Based on a click or a stroke inside the lesion from the user, a distribution of intensity level properties is learned. The random walker segmentation method combines multiple 2D segmentation results to produce the final 3D segmentation of the lesion.

Abstract: A method for processing an object in image data includes the steps of drawing a contour on a pre-segmentation of an object in image data, generating at least one seed point on the pre-segmentation from an intersection of the contour and the pre-segmentation, providing a weighting factor between the seed points and the pre-segmentation, and segmenting the pre-segmentation using the seed points and the weighting factor to generate a new pre-segmentation.

Abstract: A method is provided for segmenting an image of interest of a left ventricle. The method includes determining a myocardium contour according to a graph cut of candidate endocardium contours, and a spline fitting to candidate epicardium contours in the absence of shape propagation. The method further includes applying a plurality of shape constraints to candidate endocardium contours and candidate epicardium contours to determine the myocardium contour, wherein a template is determined by shape propagation of a plurality of images in a sequence including the image of interest in the presence of shape propagation.