Visualization of Biological Images
Both visualization and mathematical computations were used to study, measure, and understand phenomena tracked through image data. We are working on image analysis in several different fields.
In medical image analysis, we are working with CT lung data containing lung tumors. Projects include new methods to measure lung tumor size change and the creation of reference data sets for lung tumor size change measurements.
Biological cell image analysis projects include methods to measure cell segmentation accuracy and new segmentation methods to track live cells.
To see more images and video, see the Multimedia Page for this project.
A. Peskin, K. Kafadar, A. Dima, J. Bernal and D. Gilsinn, Synthetic Lung Tumor Data Sets for Comparison of Volumetric Algorithms in 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, NV, July 2009.
A. Peskin, K. Kafadar, A. M. Santos and G. Haemer, Robust Volume Calculations of Tumors of Various Sizes in 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, NV, July 2009.
A. Peskin, K. Kafadar and A. Dima, A Quality Pre-Processor for Biological Cell Images in Computational Bioimaging 2009, 5th International Symposium on Visual Computing, Las Vegas, NV, November-December 2009.
A. Peskin, A. Dima, J. Chalfoun and J. Elliot, Predicting Segmentation Accuracy for Biological Cell Images in Bioimage Informatics 2010, Pittsburgh, PA, September 2010.
J. Chalfoun, A. Dima, A. Peskin, J. Elliot and J. Filliben, A Human Inspired Local Ratio-Based Algorithm for Edge Detection in Fluorescent Cell Images in The 6th International Symposium on Visual Computing 2010, Las Vegas, NV., November-December 2010.
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