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Search Publications by: Adele Peskin (Fed)

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Displaying 26 - 50 of 50

Comparison of segmentation algorithms for fluorescence microscopy images of cells

June 14, 2011
Author(s)
Alden A. Dima, John T. Elliott, James J. Filliben, Michael W. Halter, Adele P. Peskin, Javier Bernal, Marcin Kociolek, Mary C. Brady, Hai C. Tang, Anne L. Plant
Segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions were compared. Significant variability in the results of segmentation was observed that was due solely to

Tumor volume measurement errors RECIST studied with realistic tumor models

May 16, 2011
Author(s)
Benjamin R. Galloway, Adele Peskin, Zachary H. Levine
RECIST (Response Evaluation Criteria in Solid Tumors) is a linear measure intended to predict tumor volume in medical computed tomography (CT). In this work, using purely geometrical considerations, we estimate how well RECIST can predict the volume of

Tumor Volume Measurement Errors of RECIST Studied With Ellipsoids

May 5, 2011
Author(s)
Zachary H. Levine, Benjamin R. Galloway, Adele P. Peskin, C. P. Heussel, Joseph J. Chen
RECIST (Response Evaluation Criteria in Solid Tumors) is a linear measure intended to predict tumor volume in medical computed tomography (CT). In this work, using purely geometrical considerations, we establish limits for how well RECIST can predict the

Predicting Segmentation Accuracy for Biological Cell Images

December 15, 2010
Author(s)
Adele P. Peskin, Alden A. Dima, Joe Chalfoun, John T. Elliott
We have performed segmentation procedures on a large number of images from two mammalian cell lines that were seeded at low density, in order to study trends in the segmentation results and make predictions about cellular features that affect segmentation

Predicting Segmentation Accuracy for Biological Cell Images

November 29, 2010
Author(s)
Adele P. Peskin, Alden A. Dima, Joe Chalfoun
We have performed image segmentations on a very large number of images, using a wide variety of imaging conditions and cell lines, in order to study trends in the segmentation results and make predictions about segmentation accuracy. Comparing results from

A Human Inspired Local Ratio-Based Algorithm for Edge Detection in Fluorescent Cell Images

November 25, 2010
Author(s)
Adele P. Peskin, Joe Chalfoun, Alden A. Dima, John T. Elliott, James J. Filliben
We have developed a new semi-automated method for segmenting images of biological cells seeded at low density on tissue culture substrates, which we use to improve the generation of reference data for the evaluation of automated segmentation algorithms

A Quality Pre-Processor for Biological Cell Images

November 30, 2009
Author(s)
Adele P. Peskin, Karen Kafadar, Alden A. Dima
We have developed a method to rapidly test the quality of a biological image, to identify appropriate segmentation methods, if any, that will render high quality segmentations for the cells within that image. The key contribution is the development of a

Robust Volume Calculations of Tumors of Various Sizes

July 13, 2009
Author(s)
Adele P. Peskin, Karen Kafadar, A.M. Santos, Gillian Haemer
Many advances in medicine today require the accurate reading of computerized tomographic (CT) images of the body. Tumors in the lung, for example, are classified according to their detected growth, i.e. change in volume, over a period of time. CT data are

Synthetic Lung Tumor Data Sets for Comparison of Volumetric Algorithms

July 13, 2009
Author(s)
Adele P. Peskin, Alden A. Dima, Javier Bernal, David E. Gilsinn, Karen Kafadar
The change in pulmonary nodules over time is an important indicator of malignant tumors. It is therefore important to be able to measure change in the size of tumors from computed tomography (CT) data taken at different times and on potentially different

Extending Measurement Science to Interactive Visualization Environments

January 30, 2009
Author(s)
Judith E. Terrill, William L. George, Terence J. Griffin, John G. Hagedorn, John T. Kelso, Thomas M. Olano, Adele P. Peskin, Steven G. Satterfield, James S. Sims, Jeffrey W. Bullard, Joy P. Dunkers, Nicos Martys, Agnes A. O'Gallagher, Gillian Haemer
We describe a method for creating a visual laboratory to interactively measure and analyze scientific data. We move the normal activities that scientists perform to understand their data into the visualization environment. The visualization environment is

A Low-Cost Density Reference Phantom for Computed Tomography

January 2, 2009
Author(s)
Zachary H. Levine, Ming-Dong Li, Anthony P. Reeves, David F. Yankelevitz, Joseph J. Chen, Eliot L. Siegel, Adele P. Peskin, Diana N. Zeiger
We have characterized a commercially-available polyurethane foam which is marketed for modeling parts in the aircraft, automotive, and related industries. We find that the foam may be suitable for use as a density reference standard in the range below -700

Methods for Quantifying and Characterizing Errors in Pixel-Based 3D Rendering

July 1, 2008
Author(s)
John G. Hagedorn, Judith E. Terrill, Adele P. Peskin, James J. Filliben
We present methods for measuring errors in the rendering of three-dimensional points, line segments, and polygons in pixel-based computer graphics systems. We present error metrics for each of these three cases. These methods are applied to rendering with

Accelerating Scientific Discovery through Computation and Visualization III. Tight-binding Wave Functions for Quantum Dots

June 2, 2008
Author(s)
James S. Sims, John G. Hagedorn, Steven G. Satterfield, Terence J. Griffin, William L. George, Howard Hung, John T. Kelso, Thomas M. Olano, Adele P. Peskin, Judith E. Terrill, Garnett W. Bryant, Jose G. Diaz
This is the third in a series of articles that describe, through examples, how the Scientific Applications and Visualization Group (SAVG) at NIST has utilized high performance parallel computing, visualization, and machine learning to accelerate scientific

Correction of Location and Orientation Errors in Electromagnetic Motion Tracking

August 1, 2007
Author(s)
John G. Hagedorn, Steven G. Satterfield, John T. Kelso, W. Austin, Judith E. Terrill, Adele Peskin
We describe a method for calibrating an electromagnetic motion tracking device. Algorithms for correcting both location and orientation are presented. In particular, we use a method for interpolating rotation corrections at scattered data points that has

Science at the Speed of Thought

February 1, 2005
Author(s)
J E. Devaney, Steven G. Satterfield, John T. Kelso, Adele Peskin, William L. George, John G. Hagedorn, Terence J. Griffin, Howard Hung, R D. Kriz