Abstract: A method and system for polyp segmentation in computed tomography colonogrphy (CTC) volumes is disclosed. The polyp segmentation method utilizes a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from surrounding tissue in CTC volumes. Based on an input initial polyp position, a polyp tip is detected in a CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary.

Abstract: A method for performing computer assisted diagnosis, includes receiving medical image data of a structure under analysis including background and foreground pixels, matching a set of one or more masks to foreground pixels of the acquired medical image data, converting a background pixel of the acquired medical image data to foreground pixel based on a match between one of the masks and the acquired medical image data, performing morphological dilation on the medical image data with the converted pixel, performing morphological erosion on the dilated medical image data, and identifying one or more regions of interest based on a difference between the original acquired medical image data and the eroded medical image data.

Abstract: A method for segmenting tubular structures in medical images includes providing at least a start point and an end point in a digital image volume, minimizing an action surface U0(p) which, at each image point p, corresponds to a minimal energy integrated along a path that starts at start point p0 and ends at p, sliding back on the minimal action surface from an end point to the start point to find a minimal path connecting the terminal points, initializing a level set function with points on the minimal path, and evolving the level set function to find a surface of a structure about the minimal path, wherein the level set function is constrained to be close to a signed distance function and wherein the level set function is prevented from growing wider than a predetermined diameter R, wherein the surface about the minimal path defines a tubular structure.

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 performing computer-assisted diagnosis includes receiving a plurality of two-dimensional views of an internal structure, defining a search space around one or more areas of analysis within each view of the internal structure, calculating a convex hull for each area of analysis within each search space of each view of the internal structure, determining a set of foreground pixels that are located within the convex hull for each area of analysis within each search space within each view of the internal structure, and for each area of analysis, merging the set of foreground pixels that are located within the convex hull from each view.

Abstract: A method for segmenting a tubular structure includes providing a three-dimensional image containing the tubular structure, providing at least one seed point within the tubular structure, fitting an initial cylinder into the tubular structure at the seed point, adding cylinder segments to the initial cylinder in forward and backward directions within the three-dimensional image of the tubular structure by tracking the cylinder model over the tubular structure, modeling each cylinder segment using parameters to account for a three-dimensional orientation, a radius, a length and a curvature, describing an intensity image of the tubular structure using a second model to represent an edge and intensity distribution of the tubular structure, fitting the parameters as the cylinder segment is added to the intensity image, and outputting cylinder segments as a segmentation of the tube.

Abstract: An image processing system for recognizing image features in three dimensional images, which can be medical images, uses a mask generator for generating masks that are used by a candidate searcher to search for candidate images in the three dimensional image. The candidate searcher applies the mask to a section of a foreground region of the image to determine the presence of a structure/object by counting the number of intersections between the mask and the section of the foreground region.

Abstract: A method for joint segmentation and registration includes providing a plurality of datasets comprising images of an object of interest, and performing, iteratively, a segmentation and a registration of at least a portion of the plurality of datasets comprising, performing the segmentation of the plurality of datasets, outputting a segmentation result, performing the registration on the segmentation result, outputting a registration result, and merging information of the plurality of datasets, including the registration result, by propagating the segmentation result from each dataset to all other datasets.

Abstract: A computer-implemented method for identifying an object of interest includes providing input data including an image and a candidate for the object of interest in the image, extracting a boundary of the candidate, and extracting a segment of a region of interest containing the candidate. The method further includes determining a plurality of features of an extracted segment of the region of interest containing the candidate, and outputting the object of interest, wherein the object of interest is characterized by the plurality of features, wherein the object of interest and the plurality of features are stored as computer-readable code.

Abstract: A method for identifying non-body structures in digitized medical images including the steps of providing a digitized image comprising a plurality of intensities corresponding to a domain of points on an N-dimensional grid, wherein said image includes a representation of a body and of non-body structures separate from said body, initializing a surface in said image on a side of said non-body structures opposite from said body, defining a plurality of forces acting on said surface, and displacing said surface through said non-body structures using said forces until said body is encountered.

Abstract: An exemplary method of detecting one or more objects in image data is provided. The image data includes a plurality of pixels/voxels. The method includes sliding pixels/voxels that meet sliding criteria; and collecting the slid pixels/voxels that satisfy collecting criteria. An exemplary method of segmenting an object in image data is also provided. The method includes receiving an initial pixel/voxel in the image data; and forming a segmentation of the object based on the initial pixel/voxel.

Abstract: In one aspect of the present invention, a method for calculating a response value at a first voxel indicative of a global shape in an image is provided. The method includes the steps of (a) determining at least one local shape descriptor associated with each of the at least one local shape descriptor; (b) determining a spread function associated with the each of the at least one local shape descriptor; (c) determining second voxels around the first voxel; (d) calculating values for each the at least one local shape descriptor at each of the second voxels; (e) determining a contribution of each of the second voxels at the first voxel based on the spread functions; and (f) using a combination function to combine the contributions to determine the response value indicative of the global shape.

Abstract: An image processing system for recognizing image features in three dimensional images, which can be medical images, uses a mask generator for generating masks that are used by a candidate searcher to search for candidate images in the three dimensional image. The candidate searcher applies the mask to a section of a foreground region of the image to determine the presence of a structure/object by counting the number of intersections between the mask and the section of the foreground region.