Abstract: A method for detecting spherical and ellipsoidal objects is digitized medical images includes providing a 2-dimensional (2D) slice I(x, y) extracted from a medical image volume of a colon, said image volume comprising a plurality of intensities associated with a 3 grid of points, generating a plurality of templates of different sizes whose shape matches a target structure being sought in said slice, calculating a normalized gradient from said slice, calculating a diverging field gradient response (DFGR) for each of the plurality of masks with the normalized gradient, and selecting a strongest response as being indicative of the position and size of the target structure.

Abstract: A method for obtaining a tissue volume, includes inputting a dataset including a plurality of voxels; initializing a tissue probability volume for the plurality of voxels to a pre-determined value; updating, by one of increasing or decreasing the tissue probability volume of each of the plurality of voxels, based on corresponding intensity values of each of the plurality of voxels; and generating the tissue volume by combining the updated tissue probability volume and the inputted dataset.

Abstract: A method for characterizing a shape of an object surface includes acquiring image data including the object. The image data is analyzed at a locus of points that are at a predetermined distance from a point of interest proximate to the object surface to determine which of the locus of points represents a foreground and which of the locus of points represents a background. The shape of the object surface is characterized based on the characterization of the locus of points.

Abstract: A method for displaying computer aided detection markers from a digitized image includes marking a first set of locations of interest during a first read of the image, producing a first set of markers, receiving a second set of markers of locations of interest on the image from the CAD algorithm, combining the first set of markers with the second set of markers, sorting the combined set of markers according to a predetermined criteria; and presenting the sorted set of markers to a user for a second read of said image.

Abstract: A method for removing a virtual object from a digitized image comprises the steps of computing a point spread function of the intensities of an image, wherein a point spread function is a measure of the blurriness of said image, marking a plurality of points that represent an object of interest in the image, and subtracting the point spread function value from the intensity for each marked point, wherein the object of interest is removed from said image.

Abstract: A computer-implemented method for aligning sub-volumes includes receiving a data volume, providing a plurality of parameters of the sub-volumes, and determining a start point of each of the two or more sub-volume according to the plurality of parameters. The method further includes determining an amount of mis-alignment of the two or more sub-volumes, and shifting a start point of each sub-volume to align the start points with a grid of a virtual volume. The method may include determining a feature in the two or more sub-volumes, which have been aligned. The method may include visualizing the aligned sub-volumes.

Abstract: A system and method for segmenting a structure of interest are provided. The method comprises: segmenting a surface of the structure of interest; and interpolating a border of the structure of interest, wherein the border separates the structure of interest from other structures having similar properties.

Abstract: A system and method for segmentation of structures in an image based on curvature slope are provided, the system including a processor, a curvature slope unit in signal communication with the processor for computing curvature slopes in the image and discarding voxels having a slope not satisfying specifications, and a segmentation unit in signal communication with the processor for locating connected clusters of retained voxels; and the method including computing curvature slopes in the image, discarding voxels having a slope not satisfying specifications, and locating connected clusters of retained voxels.