Abstract: Embodiments of the present invention provide a method to temporally isolate data accessed by a computing device so that the data accessed by the computing device is limited to a single set of data. The method includes removing any data that is accessed by the computing device when operating in different modes so that the data is inaccessible by the computing device when operating in the mode. The method also includes switching to the mode after the data associated with the modes different from the mode have been removed. The method also includes operating in the mode based on a plurality of rules associated with the security policy in temporal isolation from any other mode associated with the computing device. The computing device is limited to operating in the mode and is prevented from accessing any data that is distinct from the single set of data of the mode.

Abstract: Embodiments of the present invention provide a method to temporally isolate data accessed by a computing device so that the data accessed by the computing device is limited to a single set of data. The method includes removing any data that is accessed by the computing device when operating in different modes so that the data is inaccessible by the computing device when operating in the mode. The method also includes switching to the mode after the data associated with the modes different from the mode have been removed. The method also includes operating in the mode based on a plurality of rules associated with the security policy in temporal isolation from any other mode associated with the computing device. The computing device is limited to operating in the mode and is prevented from accessing any data that is distinct from the single set of data of the mode.

Abstract: Embodiments of the present invention provide a method to temporally isolate data accessed by a computing device so that the data accessed by the computing device is limited to a single set of data. The method includes removing any data that is accessed by the computing device when operating in different modes so that the data is inaccessible by the computing device when operating in the mode. The method also includes switching to the mode after the data associated with the modes different from the mode have been removed. The method also includes operating in the mode based on a plurality of rules associated with the security policy in temporal isolation from any other mode associated with the computing device. The computing device is limited to operating in the mode and is prevented from accessing any data that is distinct from the single set of data of the mode.

Abstract: Systems, computer-readable media, methods, and a medical imaging apparatus for improving the automated detection of suspicious regions of interest in x-ray images of anatomical organs under study are disclosed. Noise effects in x-ray images are suppressed to predetermined levels by filtering the original x-ray images and then combining the original images with the filtered images in such a way that the predetermined noise value is met. The resulting modified x-ray images then may be analyzed to automatically detect suspected breast microcalcifications or other suspicious regions of interest. In addition, three-dimensional digital images of anatomical organs may be computed from a plurality of such modified x-ray images of an anatomical organ taken from different angles, as in CT imaging, and the three-dimensional digital images may be processed to automatically detect suspicious regions of interest.

Abstract: Systems, computer-readable media, methods, and a medical imaging apparatus for improving the automated detection of suspicious regions of interest in x-ray images of anatomical organs under study are disclosed. Noise effects in x-ray images are suppressed to predetermined levels by filtering the original x-ray images and then combining the original images with the filtered images in such a way that the predetermined noise value is met. The resulting modified x-ray images then may be analyzed to automatically detect suspected breast microcalcifications or other suspicious regions of interest. In addition, three-dimensional digital images of anatomical organs may be computed from a plurality of such modified x-ray images of an anatomical organ taken from different angles, as in CT imaging, and the three-dimensional digital images may be processed to automatically detect suspicious regions of interest.

Abstract: A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded, clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding, but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm.

Abstract: A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded, clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding, but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm.

Abstract: A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded, clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding, but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm.

Abstract: A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded, clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding, but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm.

Abstract: A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm.

Abstract: A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded, clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding, but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm.

Abstract: A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded, clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding, but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm.

Abstract: A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded, clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding, but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm.

Abstract: A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded, clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding, but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm.