Adele P. Peskin

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This paper describes a set of tools for performing measurements of objects in a virtual reality based immersive visualization environment. These tools enable the use of the immersive environment as an instrument for extracting quantitative information from data representations that hitherto had be used solely for qualitative examination. We provide, within(More)
The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five(More)
We present a statistical method that leads to accurate volume measurements of lung tumors from computerized tomographic (CT) data. The method is based on the assumption that a range of pixel intensities in CT data defines the edge of a tumor, and from our statistical model, we assign a probability that a given pixel intensity is included in the tumor(More)
This is the second in a series of articles describing a wide variety of projects at NIST that synergistically combine physical science and information science. It describes, through examples, how the Scientific Applications and Visualization Group (SAVG) at NIST has utilized high performance parallel computing, visualization, and machine learning to(More)
We describe a method for calibrating an electromagnetic motion tracking device. Algorithms for correcting both location and orientation data are presented. In particular we use a method for interpolating rotation corrections that has not previously been used in this context. This method, unlike previous methods, is rooted in the geometry of the space of(More)
Many cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting, identification and measurement of individual cells. Over-segmentation of single(More)
We present a set of synthetic lung tumor data in which synthetic tumors of known volume are embedded in clinical lung computerized tomographic (CT) data in different background settings in the lung. Because the change in pulmonary nodules over time is an important indicator of lung tumor malignancy, it is important to be able to accurately measure changes(More)
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 accuracy. By comparing segmentation results from approximately 40000 cells, we find a linear(More)