Dr. Kevin A. Kreeger Profile

Dr. Kevin A. Kreeger

at Hologic Inc

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Author

Publications (6)

Proceedings Article | 24 April 2002 Paper

Image segmentation approach to extract colon lumen through colonic material tagging and hidden Markov random field model for virtual colonoscopy

Lihong Li, Dongqing Chen, Sarang Lakare, Kevin Kreeger, Ingmar Bitter, Arie Kaufman, Mark Wax, Petar Djuric, Zhengrong Liang

Proceedings Volume 4683, (2002) https://doi.org/10.1117/12.463607

KEYWORDS: Colon, Image segmentation, Expectation maximization algorithms, Virtual colonoscopy, Computed tomography, Tissues, Magnetorheological finishing, Colorectal cancer, Medical imaging, Tumor growth modeling

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Proceedings Article | 24 April 2002 Paper

Covering all clinically significant areas of the colon surface in virtual colonoscopy

Kevin Kreeger, Frank Dachille, Mark Wax, Arie Kaufman

Proceedings Volume 4683, (2002) https://doi.org/10.1117/12.463583

KEYWORDS: Colon, Visualization, Virtual colonoscopy, Endoscopy, Volume rendering, Cameras, Computed tomography, Optical simulations, Device simulation, Opacity

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Proceedings Article | 21 May 2001 Paper

Interactive electronic biopsy for 3D virtual colonscopy

Ming Wan, Frank Dachille, Kevin Kreeger, Sarang Lakare, Mie Sato, Arie Kaufman, Mark Wax, Zhengrong Liang

Proceedings Volume 4321, (2001) https://doi.org/10.1117/12.428176

KEYWORDS: Colon, Biopsy, Virtual colonoscopy, Opacity, Volume rendering, Computed tomography, Cameras, Visualization, Human-machine interfaces, Colorectal cancer

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Proceedings Article | 21 May 2001 Paper

Interactive navigation for PC-based virtual colonscopy

Frank Dachille, Kevin Kreeger, Mark Wax, Arie Kaufman, Zhengrong Liang

Proceedings Volume 4321, (2001) https://doi.org/10.1117/12.428179

KEYWORDS: Virtual colonoscopy, Volume rendering, Colon, Visualization, Inspection, Endoscopy, Image segmentation, 3D image processing, Control systems, Diagnostics

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Proceedings Article | 20 April 2000 Paper

3D virtual colonoscopy with real-time volume rendering

Ming Wan, Wei Li, Kevin Kreeger, Ingmar Bitter, Arie Kaufman, Zhengrong Liang, Dongqing Chen, Mark Wax

Proceedings Volume 3978, (2000) https://doi.org/10.1117/12.383395

KEYWORDS: Colon, Virtual colonoscopy, Volume rendering, Visualization, Cameras, 3D modeling, Image quality, 3D image processing, Computer simulations, Light sources and illumination

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Proceedings Article | 1 April 1992 Paper

Real-time identification of fixed targets

Patricia Rowland, Kevin Kreeger, Albert Sanders

Proceedings Volume 1623, (1992) https://doi.org/10.1117/12.58050

KEYWORDS: Target recognition, Automatic target recognition, Detection and tracking algorithms, 3D modeling, Buildings, Forward looking infrared, Image processing, Feature extraction, Algorithm development, Data modeling

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Fixed targets such as bridges, airfields, and buildings are of military significance and their value is constantly being appraised as the battle scenario evolves. For examples, a building thought to be of no significance may be reappraised, through intelligence reports, as a military command center. The ability to quickly strike these targets with a minimal amount of a priori information is necessary. The requirements placed on such a system are: (1) Rapid turnaround time from the moment the decision is made to attack. Depending on the user organization, this time ranges from fifteen minutes to twelve hours. (2) Minimal a priori target information. There is likely to be no imagery data base of the target, and the system may be required to operate with as little information as an overhead photograph. (3) Real time recognition of the target. Terminal guidance of the weapons delivery system to a specified destructive aimpoint of will be impacted by the recognition system. (4) Flexibility to attack a variety of targets. A data base of known high value fixed targets (HVFT) may be stored, but the sudden inclusion of new targets must be accommodated. This paper will discuss a real time implementation of a model based approach to automatically recognize high value fixed targets in forward looking infrared (FLIR) imagery. This approach generates a predictive model of the expected target features to be found in the image, extracts those feature types from the image, and matches the predictive model with the image features. A generic approach to the description of the target features has been taken to allow for rapid preparation of the models from minimal a priori target information. The real time aspect has been achieved by implementing the system on a massively parallel single instruction, multiple data architecture. An overview of an entire system approach to attack high value fixed targets will be discussed. The automatic target recognizer (ATR), which is a part of this system, will be discussed in detail and results of the ATR operating against HVFT in FLIR imagery will be shown.

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