Abstract: An image analysis embodiment comprises subsampling a digital image by a subsample factor related to a first anomaly size scale, thereby generating a subsampled image, smoothing the subsampled image to generate a smoothed image, determining a minimum negative second derivative for each pixel in the smoothed image, determining each pixel having a convex down curvature based on a negative minimum negative second derivative value for the respective pixel, joining each eight-neighbor connected pixels having convex down curvature to identify each initial anomaly area, selecting the initial anomaly areas having strongest convex down curvatures based on a respective maximum negative second derivative for each of the initial anomaly areas, extracting one or more classification features for each selected anomaly area, and classifying the selected anomaly areas based on the extracted one or more classification features.

Abstract: An analysis of a digitized image is provided. The digitized image is repeatedly convolved to form first convolved images, which first convolved images are convolved a second time to form second convolved images. Each first convolved image and the respective second convolved image representing a stage, and each stage represents a different scale or size of anomaly. As an example, the first convolution may utilize a Gaussian convolver, and the second convolution may utilize a Laplacian convolver, but other convolvers may be used. The second convolved image from a current stage and the first convolved image from a previous stage are used with a neighborhood median determined from the second convolved image from the current stage by a peak detector to detect peaks, or possible anomalies for that particular scale.

Abstract: An embodiment method for marking an anomaly in an image comprises generating an initial boundary description representing a size, a shape and a location of the anomaly in the image, dilating the initial boundary description to generate a dilated boundary description representing the shape, the location and an enlarged size of the initial boundary description, and saving, on a non-transitory computer-readable medium, the dilated boundary description as an overlay plane object in an output format compliant with a industry standard digital image format.

Abstract: A PDF estimator for determining a probability that a detected object is a specific type of object is provided. Training data from a known set is used to functionally describe the relevant neighborhood for specific representation points. The neighborhood is selected based on the measured features of the object to be classified and weights are calculated to be applied to the representation points. A probability is determined based upon the stored training data, the measured features of the object to be classified, and the weights.

Abstract: An image analysis embodiment comprises generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies, detecting ridges in the digital image to generate a detected ridges map, projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps, determining wedge features for the potential convergence hubs from the set of ridge direction projection maps, selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features, extracting classification features for each of the selected ridge convergence hubs, and classifying the selected ridge convergence hubs based on the extracted classification features.

Abstract: An image analysis embodiment comprises generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies, detecting ridges in the digital image to generate a detected ridges map, projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction) projection maps, determining wedge features for the potential convergence hubs from the set of ridge direction projection maps, selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features, extracting classification features for each of the selected ridge convergence hubs, and classifying the selected ridge convergence hubs based on the extracted classification features.

Abstract: An analysis of a digitized image is provided. The digitized image is repeatedly convolved to form first convolved images, which first convolved images are convolved a second time to form second convolved images. Each first convolved image and the respective second convolved image representing a stage, and each stage represents a different scale or size of anomaly. As an example, the first convolution may utilize a Gaussian convolver, and the second convolution may utilize a Laplacian convolver, but other convolvers may be used. The second convolved image from a current stage and the first convolved image from a previous stage are used with a neighborhood median determined from the second convolved image from the current stage by a peak detector to detect peaks, or possible anomalies for that particular scale.

Abstract: An image segmentation embodiment comprises applying a second derivative operator to the pixels of an image, growing a set of contours using seeding grid points as potential contour starting points, determining a contour strength vector for each of the contour pixels, generating a partial ellipse representing an estimated location of an object in the image, dividing the partial ellipse into a plurality of support sectors with control points, determining a contour strength and position for each contour, adjusting a position of each sector control point based on the contour positions weighted by the contour strengths of the contours centered in the respective sector, fitting the partial ellipse to the adjusted positions of the control points, and generating a segmentation mask of the object based on the partial fitted ellipse.

Abstract: An image segmentation embodiment comprises generating a start model comprising a set of model points approximating an outline of an object in an initial image, smoothing the image at a first smoothing level, generating a curvature image by applying a second derivative operator, locating second derivative local maxima in the curvature image that are orthogonal to a respective model point and within a search region having a first boundary on one side of the start model and a second boundary on an opposite side of the start model, generating a set of contours, shifting the start model to an outer boundary of the contours, and generating a segmentation mask of the object based on the shifted start model.

Abstract: A PDF estimator for determining a probability that a detected object is a specific type of object is provided. Training data from a known set is used to functionally describe the relevant neighborhood for specific representation points. The neighborhood is selected based on the measured features of the object to be classified and weights are calculated to be applied to the representation points. A probability is determined based upon the stored training data, the measured features of the object to be classified, and the weights.

Abstract: An image analysis embodiment comprises generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies, detecting ridges in the digital image to generate a detected ridges map, projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction) projection maps, determining wedge features for the potential convergence hubs from the set of ridge direction projection maps, selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features, extracting classification features for each of the selected ridge convergence hubs, and classifying the selected ridge convergence hubs based on the extracted classification features.

Abstract: An image analysis embodiment comprises subsampling a digital image by a subsample factor related to a first anomaly size scale, thereby generating a subsampled image, smoothing the subsampled image to generate a smoothed image, determining a minimum negative second derivative for each pixel in the smoothed image, determining each pixel having a convex down curvature based on a negative minimum negative second derivative value for the respective pixel, joining each eight-neighbor connected pixels having convex down curvature to identify each initial anomaly area, selecting the initial anomaly areas having strongest convex down curvatures based on a respective maximum negative second derivative for each of the initial anomaly areas, extracting one or more classification features for each selected anomaly area, and classifying the selected anomaly areas based on the extracted one or more classification features.

Abstract: An embodiment method for marking an anomaly in an image comprises generating an initial boundary description representing a size, a shape and a location of the anomaly in the image, dilating the initial boundary description to generate a dilated boundary description representing the shape, the location and an enlarged size of the initial boundary description, and saving, on a non-transitory computer-readable medium, the dilated boundary description as an overlay plane object in an output format compliant with a industry standard digital image format.

Abstract: An analysis of a digitized image is provided. The digitized image is repeatedly convolved to form first convolved images, which first convolved images are convolved a second time to form second convolved images. Each first convolved image and the respective second convolved image representing a stage, and each stage represents a different scale or size of anomaly. As an example, the first convolution may utilize a Gaussian convolver, and the second convolution may utilize a Laplacian convolver, but other convolvers may be used. The second convolved image from a current stage and the first convolved image from a previous stage are used with a neighborhood median determined from the second convolved image from the current stage by a peak detector to detect peaks, or possible anomalies for that particular scale.

Abstract: An image segmentation embodiment comprises generating a start model comprising a set of model points approximating an outline of an object in an initial image, smoothing the image at a first smoothing level, generating a curvature image by applying a second derivative operator, locating second derivative local maxima in the curvature image that are orthogonal to a respective model point and within a search region having a first boundary on one side of the start model and a second boundary on an opposite side of the start model, generating a set of contours, shifting the start model to an outer boundary of the contours, and generating a segmentation mask of the object based on the shifted start model.

Abstract: An image segmentation embodiment comprises applying a second derivative operator to the pixels of an image, growing a set of contours using seeding grid points as potential contour starting points, determining a contour strength vector for each of the contour pixels, generating a partial ellipse representing an estimated location of an object in the image, dividing the partial ellipse into a plurality of support sectors with control points, determining a contour strength and position for each contour, adjusting a position of each sector control point based on the contour positions weighted by the contour strengths of the contours centered in the respective sector, fitting the partial ellipse to the adjusted positions of the control points, and generating a segmentation mask of the object based on the partial fitted ellipse.

Abstract: A system and method for electronically displaying CAD results are disclosed. In one exemplary embodiment, a flat panel display is attached to a film multi-viewer via an articulated arm that allows a radiologist to position the flat panel display with respect to the multi-viewer. A barcode scanner is mounted to the back of the display, allowing case identifiers on CAD hardcopy printouts to be read in order to indicate which results are to be viewed. A small embedded computer is mounted with or near the flat panel display and the barcode scanner, and retrieves CAD results from a remote CAD server when a case identifier is read by the barcode scanner. Other embodiments are described and claimed.