Abstract: A method for dynamic contrast enhanced (DCE) image processing and kinetic modeling of an organ's region-of-interest is provided. The method includes deriving at least a contour of an exterior of the organ's region-of-interest from one or more of a plurality of images; generating a spline function in response to the derived contour of the exterior of the organ's region-of-interest from the one or more of the plurality of images; registering the plurality of images wherein the organ's region-of-interest has been segmented; deriving a tracer curve for the organ's region-of-interest in the registered images, the tracer curve indicating a change in concentration of a contrast agent flowing through the organ's region-of-interest over a time period; and kinetic modeling by fitting a kinetic model to the tracer curve to generate one or more maps of tissue physiological parameters associated with the kinetic model.

Abstract: A method for dynamic contrast enhanced (DCE) image processing and kinetic modeling of an organ's region-of-interest is provided. The method includes deriving at least a contour of an exterior of the organ's region-of-interest from one or more of a plurality of images; generating a spline function in response to the derived contour of the exterior of the organ's region-of-interest from the one or more of the plurality of images; registering the plurality of images wherein the organ's region-of-interest has been segmented; deriving a tracer curve for the organ's region-of-interest in the registered images, the tracer curve indicating a change in concentration of a contrast agent flowing through the organ's region-of-interest over a time period; and kinetic modeling by fitting a kinetic model to the tracer curve to generate one or more maps of tissue physiological parameters associated with the kinetic model.

Abstract: A method of correcting magnetic resonance images is provided. Seed points having a same expected true intensity are selected from the image points in an image. An intensity correction function If(X) is determined by fitting measured intensities Im(Xs) of the seed points to Im(Xs)=If(Xs), where x denotes the coordinate of the image points. If(X) is a polynomial function and the values of its coefficients are determined by the fitting. Corrected intensities Ic(Xc) for corrected image points are calculated according to Ic(Xc)=Im(Xc)/If(Xc). A corrected image includes the corrected image points.

Abstract: A method of correcting magnetic resonance images is provided. Seed points having a same expected true intensity are selected from the image points in an image. An intensity correction function If(X) is determined by fitting measured intensities Im(Xs) of the seed points to Im(Xs)=If(Xs), where x denotes the coordinate of the image points. If(X) is a polynomial function and the values of its coefficients are determined by the fitting. Corrected intensities Ic(Xc) for corrected image points are calculated according to Ic(Xc)=Im(Xc)/If(Xc). A corrected image includes the corrected image points.

Abstract: A method is proposed for binarising an image by deriving an intensity threshold and classifying pixels according to whether their intensity is below or above the threshold. In the derivation of the threshold, prior konwledge is used to define a region of interest (ROI) in the image. Furthermore, prior knowledge is used to select a range in the frequency distribution of the intensities of the pixels in the ROI, and that only data within this frequency range is used to derive the threshold. These techniques provide a highly effective mechanism for incorporating prior knowledge into the threshold selection which is critical whether the image is a medical image or not. In particular, a threshold can be found to binarise images which exhibits high robustness to imaging artefacts such a gray level inhomogeneity and noise.

Abstract: Methods, computer systems, and computer program products for segmenting tissue based upon image data representing brain tissue are disclosed. In the method, image data representing brain tissue is reformatted to a predetermined spatial orientation. Spatial statistics relating to spatial features of the represented brain tissue are determined. The represented brain tissue is classified within categories of white matter, gray matter and cerebrospinal fluid. A region of interest in the represented brain tissue classified as cerebrospinal fluid is identified. The represented brain tissue proximate the identified region is segmented using a region-growing technique based on the identified region of interest.