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Search Publications by: Alden A. Dima (Fed)

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Displaying 51 - 75 of 90

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

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

Image Classification of Vascular Smooth Muscle Cells

November 11, 2010
Author(s)
Michael Grasso, Ronil Mokashi , Alden A. Dima, Antonio Cardone, Kiran Bhadriraju, Anne L. Plant, Mary C. Brady, Yaacov Yesha, Yelena Yesha
The traditional method of cell microscopy can be subjective, due to observer variability, a lack of standardization, and a limited feature set. To address this challenge, we developed an image classifier using a machine learning approach. Our system was

AN AUTOMATIC OVERLAP-BASED CELL TRACKING SYSTEM

February 26, 2010
Author(s)
Joe Chalfoun, Antonio Cardone, Alden A. Dima, Michael Halter, Daniel P. Allen
In order to facilitate the extraction of quantitative data from live cell image sets, automated image analysis methods are needed. This paper presents an overlap-based cell tracking algorithm that has the ability to track cells across a set of time-lapse

Overlap-Based Cell Tracker

February 2, 2010
Author(s)
Joe Chalfoun, Antonio Cardone, Alden A. Dima, Michael Halter, Daniel P. Allen
In order to facilitate the extraction of quantitative data from live cell image sets, automated image analysis methods are needed. This paper presents an introduction to the general principle of an overlap cell tracking software developed by NIST. This

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

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