Abstract: A method of detecting blood vessel shadows in an anterior posterior x-ray radiograph comprising the steps of: generating candidate sub areas of the radiograph showing changes in contrast above a threshold level; supressing rib shadow edges; eliminating lung tissue shadow edges, and categorizing and eliminating nodule shadows.

Abstract: A method of detecting lung nodules in an anterior posterior x-ray radiograph comprising the steps of: generating candidate regions in image showing changes in contrast above a threshold level, and eliminating false positives by eliminating edges assignable to organs by: identifying edges; categorizing and eliminating rib edges; categorizing and eliminating lung tissue edges, and categorizing and eliminating blood vessels.

Abstract: A computer system for automatic selection of a computer-aided detection (CAD) algorithm including a database storing image data, a browser for navigating the data and selecting image data, an application receiving image data selected by the browser, and a selector selecting a CAD algorithm for processing the image data according to at least one of fixed attributes of the image data and an indication of a subject of the image data.

Abstract: A medical imaging system is used to recognize an internal structure from a three-dimensional image. The image includes image sub-volumes. An image sub-volume is selected using a non-linear search pattern. The selected image sub-volume is analyzed for the presence of the internal structure. The steps of selecting an image sub-volume using the non-linear search pattern and analyzing the selected sub-volume for the presence of the internal structure are repeated until the internal structure is found in an image sub-volume. Bounds of the internal structure are identified based on the location of the image sub-volume within which the internal structure is found.

Abstract: The invention concerns an image processing device, including an input for receiving a time series of data sets representing comparable volume digital images, each data having a position component and an intensity component, pre-processing means for modifying the data sets so as to obtain images updated in position and intensity, and comparative processing means for examining sets of time series of image elements and to detect therein signs of variations. The processing means include a modeling function for adjusting a parametric model separately on some of the sets of time series of image elements, to obtain pairs of data (image element, time) and a function for statistical analysis of said pairs of data to isolate the pairs of data representing a significant variation.

Abstract: Described herein is a technology for supporting an efficient workflow. In one implementation, a computer system receives at least one image of a subject and at least one corresponding image finding (302). The image finding identifies one or more regions-of-interest in a subject area of the image. The computer system generates enhanced annotations based on the image finding (306), overlays the enhanced annotations on the image (310) and displays (312) the resulting image to facilitate image assessment by a skilled user.

Abstract: Computer assisted detection (CAD) is made accessible to more medical offices. The CAD is provided as a service. Customers gain access to CAD service through a computer network but without the purchase of expensive software and/or hardware. The customers use software for extracting needed patient data to use the CAD service. The CAD service provider uses a server farm or third party server facilities, allowing growth without as substantial upfront costs. The CAD service provider collects patient data by providing the service. The aggregated patient data allows training of different or improved CAD algorithms. The service also identifies suspect data, such as associated with incorrect imaging settings, and provides help to the customers.

Abstract: Computer assisted detection (CAD) is made accessible to more medical offices. The CAD is provided as a service. Customers gain access to CAD service through a computer network but without the purchase of expensive software and/or hardware. The customers use software for extracting needed patient data to use the CAD service. The CAD service provider uses a server farm or third party server facilities, allowing growth without as substantial upfront costs. The CAD service provider collects patient data by providing the service. The aggregated patient data allows training of different or improved CAD algorithms. The service also identifies suspect data, such as associated with incorrect imaging settings, and provides help to the customers.

Abstract: A method for associating a computer aided detection/diagnosis result with an image including the steps of providing one or more digitized images, each image comprising a plurality of intensities corresponding to a domain of points on an N-dimensional grid, performing a computer-aided detection/diagnosis of intensity data of a first image, calculating a hash signature of intensity of a second input image, storing said computer-aided diagnosis results, and storing said hash signature, wherein said hash signature verifies said second image when said second image is displayed with said computer-aided diagnosis result.

Abstract: A method of visualizing an object in an image includes presenting an image, selecting a point in an object of interest in said image, estimating a gradient of the image in a region about the selected point, calculating a structure tensor from the image gradient, analyzing said structure tensor to determine a main orientation of said object of interest, and presenting a visualization of said object of interest based on the main orientation of the object. Various techniques can be used to increase the robustness of the gradient estimation with respect to noise, and to enhance the visualization of the object-of-interest presented to a user.

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: We propose using different classifiers based on the spatial location of the object. The intuitive idea behind this approach is that several classifiers may learn local concepts better than a “universal” classifier that covers the whole feature space. The use of local classifiers ensures that the objects of a particular class have a higher degree of resemblance within that particular class. The use of local classifiers also results in memory, storage and performance improvements, especially when the classifier is kernel-based. As used herein, the term “kernel-based classifier” refers to a classifier where a mapping function (i.e., the kernel) has been used to map the original training data to a higher dimensional space where the classification task may be easier.

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: CAD (computer-aided decision) support systems, methods and tools for medical imaging are provided, which use machine learning classification for automated detection and marking of regions of interest in medical images. Machine learning methods are used for adapting/optimizing a CAD process by seamlessly incorporating physician knowledge into the CAD process using training data that is obtained during routine use of the CAD system.

Abstract: A method of identifying nodules in radiological images, said method comprising: (a) obtaining a radiological image; (b) selecting a sub-image centered around a candidate location; (c) dividing the sub-image into a rectangular array of cells; (d) calculating absolute values of Intensity Differences id(k) according to a Fractional Brownian Motion (FBM) calculation equation: id ( k ) = [ ? x = 0 N - 1 ? ? y = 0 N - k - 1 ? ? I ? ( x , y ) - I ? ( x , y + k ) ? 4 ? N ? ( N - k ) + ? y = 0 N - 1 ? ? x = 0 N - k - 1 ? ? I ? ( x , y ) - I ? ( x + k , y ) ? 4 ? N ? ( N - k ) + ? x = 0 N - 1 - k ? ? y = 0 N - k - 1 ? ? I ? ( x , y ) - I ? ( x + k , y + k ) ? 4 ? ( N - k ) 2 + ? x = 0 N - 1 - k ? ? y = 0 N - k - 1 ? ? I ? ( x , N - y ) - I ? ( x + k , N - ( y + k ) ) ? 4 ? ( N - k ) 2 ] , for k=1

Abstract: A method of detecting lung nodules in an anterior posterior x-ray radiograph comprising the steps of: generating candidate regions in image showing changes in contrast above a threshold level, and eliminating false positives by eliminating edges assignable to organs by: identifying edges; categorizing and eliminating rib edges; categorizing and eliminating lung tissue edges, and categorizing and eliminating blood vessels.

Abstract: A method of detecting blood vessel shadows in an anterior posterior x-ray radiograph comprising the steps of: generating candidate sub areas of the radiograph showing changes in contrast above a threshold level; supressing rib shadow edges; eliminating lung tissue shadow edges, and categorizing and eliminating nodule shadows.

Abstract: A medical imaging system is used to recognize an internal structure from a three-dimensional image. The image includes image sub-volumes. An image sub-volume is selected using a non-linear search pattern. The selected image sub-volume is analyzed for the presence of the internal structure. The steps of selecting an image sub-volume using the non-linear search pattern and analyzing the selected sub-volume for the presence of the internal structure are repeated until the internal structure is found in an image sub-volume. Bounds of the internal structure are identified based on the location of the image sub-volume within which the internal structure is found.

Abstract: A computer system for automatic selection of a computer-aided detection (CAD) algorithm including a database storing image data, a browser for navigating the data and selecting image data, an application receiving image data selected by the browser, and a selector selecting a CAD algorithm for processing the image data according to at least one of fixed attributes of the image data and an indication of a subject of the image data.

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.