Steven L Rogers

US Patent:

6389157, May 14, 2002

Filed:

Jan 11, 2001

Appl. No.:

09/758889

Inventors:

Steven K. Rogers - Beavercreek OH
Randy P. Broussard - Huber Heights OH
Edward M. Ochoa - San Antonio TX
Thomas F. Rathbun - Beavercreek OH
John E. Rosenstengel - Huber Heights OH

Assignee:

Qualia Computing, Inc. - Beavercreek OH

International Classification:

G06K 900

US Classification:

382132, 382260, 382270

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.

US Patent:

6401082, Jun 4, 2002

Filed:

Nov 8, 1999

Appl. No.:

09/434549

Inventors:

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

Assignee:

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

International Classification:

G06N 300

US Classification:

706 26, 706 25, 216 60

Abstract:

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.

US Patent:

6556699, Apr 29, 2003

Filed:

Aug 24, 2001

Appl. No.:

09/938908

Inventors:

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

Assignee:

Qualia Computing, Inc. - Beavercreek OH

International Classification:

G06K 900

US Classification:

382132, 128922

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.

US Patent:

6560516, May 6, 2003

Filed:

Jan 25, 2002

Appl. No.:

10/054793

Inventors:

Michael L. Baird - Los Altos CA
Jean de Belleuille - Brunswick ME
George M. Gill - Vilonia AR
Steven W. Rogers - Conway AR

Assignee:

Snap-On Technologies, Inc. - Lincolnshire IL

International Classification:

G06F 1700

US Classification:

701 23, 701 35, 709217, 709303

Abstract:

A method for conducting a vehicle diagnostic analysis comprises the machine-implemented steps of executing a software application for conducting the vehicle diagnostic analysis, receiving signals representative of parameters of a vehicle, accessing a software object over a data transmission network, the software object containing information necessary for conducting the vehicle diagnostic analysis, and determining a vehicle diagnostic state based on the signals representative of the parameters of the vehicle and the information contained in the software object. Since the information necessary for conducting the vehicle diagnostic analysis is distributed in different systems that are connected with a data communication network, an automotive service system can minimize the size of software stored locally and can access most of the information and/or algorithm required for conducting the vehicle diagnostic analysis from a remote system.

US Patent:

6564128, May 13, 2003

Filed:

May 22, 2002

Appl. No.:

10/151942

Inventors:

Michael L. Baird - Los Altos CA
Jean de Belleuille - Brunswick ME
George M. Gill - Vilonia AR
Steven W. Rogers - Conway AR

Assignee:

Snap-On Technologies, Inc. - Lincolnshire IL

International Classification:

G06F 1900

US Classification:

701 33, 701 29, 701 35, 709217, 709219

Abstract:

A computerized automotive service equipment system is adapted to access remotely located computer systems to retrieve or exchange data and/or software applications, or to undergo live or real-time and two-way interaction. The system and its components are dynamic with respect to both function and data, and can be easily updated or otherwise altered. The system of the present invention utilizes World Wide Web technology, which enables the use of universal and widely compatible programming tools and techniques for efficient and fast system development.

US Patent:

6650766, Nov 18, 2003

Filed:

Oct 25, 2002

Appl. No.:

10/280237

Inventors:

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

Assignee:

Qualia Computing, Inc. - Beavercreek OH

International Classification:

G06K 900

US Classification:

382132, 128922

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.

US Patent:

6757415, Jun 29, 2004

Filed:

Jun 13, 2003

Appl. No.:

10/461301

Inventors:

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

Assignee:

Qualia Computing, Inc. - Beavercreek OH

International Classification:

G06K 900

US Classification:

382130, 382132

Abstract:

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.

US Patent:

6763128, Jul 13, 2004

Filed:

Jun 13, 2003

Appl. No.:

10/461316

Inventors:

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

Assignee:

Qualia Computing, Inc. - Beavercreek OH

International Classification:

B06K 900

US Classification:

382130, 378 37

Abstract:

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.