Qualia Computing, Inc.

Icad, Inc.

Redwall Technologies, LLC

John E Rosenstengel

Steven Keith Rogers

Randy P Broussard

Edward M Ochoa

Telford S Berkey

Martin P DeSimio

Philip Amburn

Jeffrey W Hoffmeister

Thomas F Rathbun

Thomas P Rathbun

Michael J Collins

Kevin S Woods

Kent H Hunter

Eric Ridvan Üner

James E Sabin

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.

Temporally isolating data accessed by a computing device

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.

Systems, computer-readable media, methods, and medical imaging apparatus for the automated detection of suspicious regions of interest in noise normalized X-ray medical imagery

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. The results of the system are optimally combined with a radiologist's observation of the original mammogram by combining the observations with the results, after the radiologist has first accepted or rejected individual detections reported by the system.

Method for combining automated detections from medical images with observed detections of a human interpreter

Joint optimization of parameters for the detection of clustered microcalcifications in digital mammograms

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. The results of the system are optimally combined with a radiologist's observation of the original mammogram by combining the observations with the results, after the radiologist has first accepted or rejected individual detections reported by the system.

Method and system for automated detection of clustered microcalcifications from digital mammograms

Method and system for segmentation and detection of microcalcifications

Method and system for combining automated detections from digital

Method and system for segmenting desired regions in digital mammograms

Method and system for automated detection of clustered

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Beavercreek, OH, United States of America

John E Rosenstengel