[CSEE Talk] talk: To Measure or not to Measure Terabyte-Sized Images? 3pm 3/9

[Tim Finin]

                             CHMPR Seminar

          TO MEASURE OR NOT TO MEASURE TERABYTE-SIZED IMAGES?

                           Peter Bajcsy, PhD
                   Information Technology Laboratory
            National Institute for Standards and Technology

              3:00pm Wednesday, 9 March 2016, ITE325b, UMBC


This talk will elaborate on a basic question "To Measure or Not To
Measure Terabyte-Sized Images?" posed by William Shakespeare if he
were a bench scientist at NIST. This basic question is a dilemma for
many traditional scientists that operate imaging instruments capable
of acquiring very large quantities of images. However, manual analyses
of terabyte-sized images and insufficient software and computational
hardware resources prevent scientists from making new discoveries,
increasing statistical confidence of data-driven conclusions, and
improving reproducibility of reported results.

The motivation for our work comes from experimental systems for
imaging and analyzing human pluripotent stem cell cultures at the
spatial and temporal coverages that lead to terabyte-sized image
data. The objective of such an unprecedented cell study is to
characterize specimens at high statistical significance in order to
guide a repeatable growth of high quality stem cell colonies. To
pursue this objective, multiple computer and computational science
problems have to be overcome including image correction (flat-field,
dark current and background), stitching, segmentation, tracking,
re-projection, feature extraction, data-driven modeling and then
representation of large images for interactive visualization and
measurements in a web browser.

I will outline and demonstrate web-based solutions deployed at NIST
that have enabled new insights in cell biology using TB-sized
images. Interactive access to about 3TB of image and image feature
data is available at https://isg.nist.gov/deepzoomweb/.


Peter Bajcsy received his Ph.D. in Electrical and Computer Engineering
in 1997 from the University of Illinois at Urbana-Champaign (UIUC) and
a M.S. in Electrical and Computer Engineering in 1994 from the
University of Pennsylvania. He worked for machine vision, government
contracting, and research and educational institutions before joining
the National Institute of Standards and Technology in 2011. At NIST,
he has been leading a project focusing on the application of
computational science in biological metrology, and specifically stem
cell characterization at very large scales. Peter's area of research
is large-scale image-based analyses and syntheses using mathematical,
statistical and computational models while leveraging computer science
fields such as image processing, machine learning, computer vision,
and pattern recognition. He has co-authored more than more than 27
journal papers and eight books or book chapters, and close to 100
conference papers.