DR. STEVEN L. ROGERS, O.D.
Optometry at Miamisburg Centerville Rd, Dayton, OH

6556699, Apr 29, 2003

Aug 24, 2001

09/938908

Steven K. Rogers - Beavercreek OH
Philip Amburn - Dayton OH
Telford S. Berkey - London OH
Randy P. Broussard - Huber Heights OH
Martin P. DeSimio - Fairborn OH
Jeffrey W. Hoffmeister - Beavercreek OH
Edward M. Ochoa - San Antonio TX
Thomas P. Rathbun - Beavercreek OH
John E. Rosenstengel - Huber Heights OH

Qualia Computing, Inc. - Beavercreek OH

G06K 900

382132, 128922

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.

6650766, Nov 18, 2003

Oct 25, 2002

10/280237

Steven K. Rogers - Beavercreek OH
Philip Amburn - Dayton OH
Telford S. Berkey - London OH
Randy P. Broussard - West River MD
Martin P. DeSimio - Fairborn OH
Jeffrey W. Hoffmeister - Manhattan Beach CA
Edward M. Ochoa - Franklin OH
Thomas F. Rathbun - Monument CO
John E. Rosenstengel - Beavercreek OH

Qualia Computing, Inc. - Beavercreek OH

G06K 900

382132, 128922

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.

6115488, Sep 5, 2000

Oct 14, 1999

9/418382

Steven K. Rogers - Beavercreek OH
Philip Amburn - Dayton OH
Telford S. Berkey - London OH
Randy P. Broussard - Huber Heights OH
Martin P. Desimio - Fairborn OH
Jeffrey W. Hoffmeister - Beavercreek OH
Edward M. Ochoa - San Antonio TX
Thomas P. Rathbun - Beavercreek OH
John E. Rosenstengel - Huber Heights OH

Qualia Computing, Inc. - Beavercreek OH

G06K 900

382132

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.

2012026, Oct 11, 2012

Apr 3, 2012

13/438240

Thomas E. Dube - Beavercreek OH, US
Richard A. Raines - Centerville OH, US
Steven K. Rogers - Beavercreek OH, US

Government of the United States, as represented by the Secretary of the Air Force - Wright-Patterson AFB OH

G06F 21/00

726 24

A method, apparatus and program product are provided to recognize malware in a computing environment having at least one computer. A sample is received. An automatic determination is made by the at least one computer to determine if the sample is malware using static analysis methods. If the static analysis methods determine the sample is malware, dynamic analysis methods are used by the at least one computer to automatically determine if the sample is malware. If the dynamic analysis methods determine the sample is malware, the sample is presented to a malware analyst to adjudicate the automatic determinations of the static and dynamic analyses. If the adjudication determines the sample is malware, a response action is initiated to recover from or mitigate a threat of the sample.

6401082, Jun 4, 2002

Nov 8, 1999

09/434549

Steven K. Rogers - Beavercreek OH
Mark E. Oxley - Dayton OH
Matthew Kabrisky - Dayton OH

The United States of America as represented by the Secretary of the Air Force - Washington DC

G06N 300

706 26, 706 25, 216 60

An efficient neural network computing technique capable of synthesizing two sets of output signal data from a single input signal data set. The method and device of the invention involves a unique integration of autoassociative and heteroassociative neural network mappings, the autoassociative neural network mapping enabling a quality metric for assessing the generalization or prediction accuracy of the heteroassociative neural network mapping.

6763128, Jul 13, 2004

Jun 13, 2003

10/461316

Steven K. Rogers - Beavercreek OH
Michael J. Collins - Beavercreek OH
Richard A. Mitchell - Springboro OH

Qualia Computing, Inc. - Beavercreek OH

B06K 900

382130, 378 37

A computer aided detection method and system to assist radiologists in the reading of medical images. The method and system has particular application to the area of mammography including detection of clustered microcalcifications and densities. A microcalcification detector is provided wherein individual detections are rank ordered and classified, and one of the features for classification is derived using a multilayer perceptron. A density detector is provided including an iterative, dynamic region growing module with embedded subsystem for rank ordering and classification of a best subset of candidate masks. A post processing stage is provided where detections are analyzed in the context of a set of images for a patient. The post processing includes a normalcy classification including providing computed values corresponding to each detection from a category of detections on an image set, computing a normalcy value using the computed values, and removing all detections from an image set when the normalcy value does not meet a predetermined condition. The final output of the system is a set of indications overlaid on the input medical images.

6757415, Jun 29, 2004

Jun 13, 2003

10/461301

Steven K. Rogers - Beavercreek OH
Kenneth W. Bauer - Fairborn OH
Michael J. Collins - Beavercreek OH
Martin P. DeSimio - Fairborn OH
Richard A. Mitchell - Springboro OH

Qualia Computing, Inc. - Beavercreek OH

G06K 900

382130, 382132

A computer aided detection method and system to assist radiologists in the reading of medical images. The method and system has particular application to the area of mammography including detection of clustered microcalcifications and densities. A microcalcification detector is provided wherein individual detections are rank ordered and classified, and one of the features for classification is derived using a multilayer perceptron. A density detector is provided including an iterative, dynamic region growing module with embedded subsystem for rank ordering and classification of a best subset of candidate masks. Features are computed from a detection on an input image by providing first and second regions on the input image corresponding to areas inside and outside the detection, measurements are computed based on values derived from the two regions, a standard deviation is computed for the measurements in each region, and a feature for the detection is computed using a Bauer-Fisher ratio. A post processing stage is provided where detections are analyzed in the context of a set of images for a patient. The final output of the system is a set of indications overlaid on the input medical images.

5999639, Dec 7, 1999

Aug 28, 1998

9/141802

Steven K. Rogers - Beavercreek OH
Philip Amburn - Dayton OH
Telford S. Berkey - London OH
Randy P. Broussard - Huber Heights OH
Martin P. DeSimio - Fairborn OH
Jeffrey W. Hoffmeister - Beavercreek OH
Edward M. Ochoa - San Antonio TX
Thomas P. Rathbun - Beavercreek OH
John E. Rosenstengel - Huber Heights OH

Qualia Computing, Inc. - Beavercreek OH

G06K 900

382132

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.

6091841, Jul 18, 2000

Oct 14, 1999

9/418383

Steven K. Rogers - Beavercreek OH
Philip Amburn - Dayton OH
Telford S. Berkey - London OH
Randy P. Broussard - Huber Heights OH
Martin P. DeSimio - Fairborn OH
Jeffrey W. Hoffmeister - Beavercreek OH
Edward M. Ochoa - San Antonio TX
Thomas F. Rathbun - Beavercreek OH
John E. Rosenstengel - Huber Heights OH

Qualia Computing, Inc. - Beavercreek OH

G06K 936

382132

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.

7308126, Dec 11, 2007

Oct 14, 2005

11/250734

Steven K. Rogers - Beavercreek OH, US
Maha Sallam - Tampa FL, US
W. Scott Parr - Brewster MA, US

iCAD, Inc. - Nashua NH

G06K 9/00

382132, 128922

The present invention provides for the use of computer-aided detection (CAD) system output displays for providing accurate representations of areas for subsequent exams. Since the CAD output, unlike the original medical imagery, is not used during the initial reading, the radiologist does not mark it until a final determination is reached regarding subsequent procedures. Additionally, since the CAD output contains versions of the original imagery, the regions indicated by the radiologist are shown in the context of the particular anatomical detail for a given patient. This detail assists the technologist, other physicians and patients in more efficiently and accurately locating the exact area for subsequent exams.