Abstract: In a method for localizing a candidate foci in neuroimaging, two images are acquired, one being a baseline (interictal) image and another being an intervention (ictal) image. The two images are aligned and the intensities of the images are normalized. A difference image is calculated by subtracting the baseline (interictal) image from the intervention (ictal) image. The difference image is normalized and regions of interest are selected as candidate foci.

Abstract: In a method or apparatus for identifying a region of interest in a medical image of a subject, at least one local maximum region of the image is determined for which a value of a given variable is a local maximum value. A user-selection of an initial voxel in the image is registered. As the region of interest, a connected set of voxels for which values of the given variable are greater than a threshold is selected, the selected set of voxels comprising a first local maximum region at a distance from the user-selected voxel, and the threshold being a given fraction of the local maximum value of the first local maximum region in the set.

Abstract: In a method for localizing a candidate foci in neuroimaging, two images are acquired, one being a baseline (interictal) image and another being an intervention (ictal) image. The two images are aligned and the intensities of the images are normalized. A difference image is calculated by subtracting the baseline (interictal) image from the intervention (ictal) image. The difference image is normalized and regions of interest are selected as candidate foci.

Abstract: In a method and apparatus for measuring activity of a tracer in a subject in a medical imaging protocol, comparable features in each of a time series of image data sets of the subject are detected. A first activity value for a first region of interest containing a first of the features in a first image data set is obtained, and a size of the first feature is measured. The first activity value is then modified using the measured size of the first feature. A second activity value for a second region of interest containing a second of the features in a second image data set is obtained. The modified first activity value and the second activity value are then combined to produce a measure of activity of the tracer over the time series.

Abstract: In a method and apparatus for measuring activity of a tracer in a subject in a medical imaging protocol, comparable features in each of a time series of image data sets of the subject are detected. A first activity value for a first region of interest containing a first of the features in a first image data set is obtained, and a size of the first feature is measured. The first activity value is then modified using the measured size of the first feature. A second activity value for a second region of interest containing a second of the features in a second image data set is obtained. The modified first activity value and the second activity value are then combined to produce a measure of activity of the tracer over the time series.

Abstract: In a method or apparatus for identifying a region of interest in a medical image of a subject, at least one local maximum region of the image is determined for which a value of a given variable is a local maximum value. A user-selection of an initial voxel in the image is registered. As the region of interest, a connected set of voxels for which values of the given variable are greater than a threshold is selected, the selected set of voxels comprising a first local maximum region at a distance from the user-selected voxel, and the threshold being a given fraction of the local maximum value of the first local maximum region in the set.

Abstract: A method for automatically segmenting a liver in digital medical images includes providing a 3-dimensional (3D) digital image I and a set of N training shapes {?i}i=1, . . . , N for a liver trained from a set of manually segmented images, selecting a seed point to initialize the segmentation, representing a level set function ??(?x+h) of a liver boundary ? in the image as ? ? ? ( x ) = ? 0 + ? i = 1 n ? ? i ? V i ? ( x ) , ? where ? ? 0 ? ( x ) = 1 N ? ? i = 1 N ? ? i ? ( x ) is a mean shape, {Vi(x)}i=1, . . .

Abstract: A method and system for registering a first image of, for example, a liver and a second image of the liver being contrast-enhanced comprises: deriving a statistical similarity measure between images; deriving a smooth divergence-free vector field derived from a gradient of the statistical similarity measure; and integrating the vector field for providing a fluid-based algorithm including a volume-preserving constraint for a transformation for registering the images.

Abstract: A method for segmenting an anatomical image, including: receiving a patient anatomical image; receiving a baseline anatomical image having pre-segmented labels, wherein the pre-segmented labels identify regions of interest in the baseline anatomical image; aligning the patient anatomical image with the baseline anatomical image to produce a transformation that when applied to the pre-segmented labels roughly identifies regions of interest in the patient anatomical image that correspond to the regions of interest in the baseline anatomical image; and updating the pre-segmented labels, which have been deformed by application of the transformation, with a new transformation that minimizes the likelihood of intensity distributions within the regions of interest of the patient anatomical image to produce a gradient image that better identifies the regions of interest of the patient anatomical image.

Abstract: A method and system for registering a first image of, for example, a liver and a second image of the liver being contrast-enhanced comprises: deriving a statistical similarity measure between images; deriving a smooth divergence-free vector field derived from a gradient of the statistical similarity measure; and integrating the vector field for providing a fluid-based algorithm including a volume-preserving constraint for a transformation for registering the images.

Abstract: A method for automatically segmenting a liver in digital medical images includes providing a 3-dimensional (3D) digital image I and a set of N training shapes {?i}i=1, . . . , N for a liver trained from a set of manually segmented images, selecting a seed point to initialize the segmentation, representing a level set function ??(?x+h) of a liver boundary ? in the image as ? ? ? ( x ) = ? 0 + ? i = 1 n ? ? i ? V i ? ( x ) , ? where ? ? 0 ? ( x ) = 1 N ? ? i = 1 N ? ? i ? ( x ) is a mean shape, {Vi(x)}i=1, . . .

Abstract: A method of registering two images using a graphics processing unit includes providing a pair of images with a first and second image, calculating a gradient of the second image, initializing a displacement field on the grid point domain of the pair of images, generating textures for the first image, the second image, the gradient, and the displacement field, and loading said textures into the graphics processing unit. A pixel buffer is created and initialized with the texture containing the displacement field. The displacement field is updated from the first image, the second image, and the gradient for one or more iterations in one or more rendering passes performed by the graphics processing unit.