Dr. Donald Peck Profile

Dr. Donald Peck

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Publications (9)

Proceedings Article | 5 June 2003 Paper

Optimal radiographic techniques for digital mammograms obtained with an amorphous selenium detector

Michael Flynn, Charles Dodge, Donald Peck, Ann Swinford

Proceedings Volume 5030, (2003) https://doi.org/10.1117/12.480486

KEYWORDS: Breast, Sensors, X-rays, Molybdenum, Signal detection, Mammography, Tungsten, Digital mammography, Image quality, Monte Carlo methods

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Proceedings Article | 3 July 2001 Paper

Polynomial transformation for MRI feature extraction

Hamid Soltanian-Zadeh, Mahmood Kharrat, Donald Peck

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.430991

KEYWORDS: Tissues, Image segmentation, Magnetic resonance imaging, Feature extraction, Brain, 3D image processing, Visualization, Scattering, Prototyping, Image analysis

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Proceedings Article | 6 June 2000 Paper

Feature space analysis: effects of MRI protocols

Hamid Soltanian-Zadeh, Lisa Scarpace, Donald Peck

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387662

KEYWORDS: Image segmentation, Tissues, Magnetic resonance imaging, Tumors, Gadolinium, Image analysis, Brain, Visualization, 3D image processing, Neuroimaging

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Proceedings Article | 6 June 2000 Paper

Optimal linear filter for fMRI analysis

Hamid Soltanian-Zadeh, Donald Peck, David Hearshen, Renee Lajiness-O'Neill

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387741

KEYWORDS: Functional magnetic resonance imaging, Linear filtering, Signal to noise ratio, Composites, Image segmentation, Image filtering, Brain, Image analysis, Model-based design, Magnetic resonance imaging

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Proceedings Article | 24 June 1998 Paper

Combining cluster analysis with supervised segementation methods for MRI

Hamid Soltanian-Zadeh, Joe Windham, Donald Peck, Linda Emery

Proceedings Volume 3338, (1998) https://doi.org/10.1117/12.310872

KEYWORDS: Tissues, Magnetic resonance imaging, Image segmentation, Brain, Neuroimaging, Image filtering, Image processing, Data analysis, Statistical analysis, Edge detection

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Proceedings Article | 25 April 1997 Paper

Feature space analysis of MRI

Hamid Soltanian-Zadeh, Joe Windham, Donald Peck

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274127

KEYWORDS: Tissues, Image segmentation, Magnetic resonance imaging, Image analysis, Tumors, Biopsy, Visualization, Brain, Nonlinear filtering, Image visualization

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This paper presents development and performance evaluation of an MRI feature space method. The method is useful for: identification of tissue types; segmentation of tissues; and quantitative measurements on tissues, to obtain information that can be used in decision making (diagnosis, treatment planning, and evaluation of treatment). The steps of the work accomplished are as follows: (1) Four T2-weighted and two T1-weighted images (before and after injection of Gadolinium) were acquired for ten tumor patients. (2) Images were analyed by two image analysts according to the following algorithm. The intracranial brain tissues were segmented from the scalp and background. The additive noise was suppressed using a multi-dimensional non-linear edge- preserving filter which preserves partial volume information on average. Image nonuniformities were corrected using a modified lowpass filtering approach. The resulting images were used to generate and visualize an optimal feature space. Cluster centers were identified on the feature space. Then images were segmented into normal tissues and different zones of the tumor. (3) Biopsy samples were extracted from each patient and were subsequently analyzed by the pathology laboratory. (4) Image analysis results were compared to each other and to the biopsy results. Pre- and post-surgery feature spaces were also compared. The proposed algorithm made it possible to visualize the MRI feature space and to segment the image. In all cases, the operators were able to find clusters for normal and abnormal tissues. Also, clusters for different zones of the tumor were found. Based on the clusters marked for each zone, the method successfully segmented the image into normal tissues (white matter, gray matter, and CSF) and different zones of the lesion (tumor, cyst, edema, radiation necrosis, necrotic core, and infiltrated tumor). The results agreed with those obtained from the biopsy samples. Comparison of pre- to post-surgery and radiation feature spaces confirmed that the tumor was not present in the second study but radiation necrosis was generated as a result of radiation.

Proceedings Article | 12 May 1995 Paper

Reproducibility study for volume estimation in MRI of the brain using the Eigenimage algorithm

Joe Windham, Donald Peck, Hamid Soltanian-Zadeh

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208720

KEYWORDS: Tissues, Image segmentation, Brain, Magnetic resonance imaging, Image filtering, Image processing algorithms and systems, Image analysis, Imaging systems, Image processing, Chemical elements

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Proceedings Article | 11 May 1994 Paper

Semiautomatic brain morphometry from CT images

Hamid Soltanian-Zadeh, Joe Windham, Donald Peck

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175074

KEYWORDS: Image segmentation, Tissues, Brain, Neuroimaging, Computed tomography, Image processing, 3D image processing, Skull, Computing systems, Bone

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Proceedings Article | 14 September 1993 Paper

MRI feature extraction using a linear transformation

Hamid Soltanian-Zadeh, Joe Windham, Donald Peck

Proceedings Volume 1898, (1993) https://doi.org/10.1117/12.154536

KEYWORDS: Tissues, Magnetic resonance imaging, Feature extraction, Brain, Visualization, 3D image processing, Image segmentation, Principal component analysis, Image processing, 3D acquisition

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Showing 5 of 9 publications

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