Siemens Medical Solutions USA, Inc.

Siemens Information Systems, Ltd.

Siemens Computer Aided Diagnosis Ltd.

Siemens Computer Aided Diagnostics, Ltd.

Jonathan Stoeckel

Arun Krishnan

Dinesh Mysore Siddu

Mausumi Acharyya

Matthias Wolf

Pascal Cathier

Glenn Fung

Alexandra Manevitch

Eliahu Ratner

Sumit Chakravarty

Grégoire Guétat

Xiangwei Zhang

Sandesh Gupta

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.

Identifying blood vessels in lung x-ray radiographs

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.

Identifying ribs in lung X-rays

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.

Automatic CAD algorithm selection

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.

System and method for automatic detection of internal structures in medical images

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.

System and method for quality assurance for distributed computer aided diagnosis solutions

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.

Method and system for local visualization for tubular structures

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.

System and method for body extraction in medical image volumes

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.

Incorporating spatial knowledge for classification

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.

CAD (computer-aided decision) support for medical imaging using machine learning to adapt CAD process with knowledge collected during routine use of CAD system

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.

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Exton, PA, United States of America

Jonathan Stoeckel