“To Measure or Not To Measure Terabyte-Sized Images?”

Wed Feb 24, 2016 2:00 PM

Location: A.V. Williams Building, Room 2460

Peter Bajcsy
Ph.D., Information Technology Laboratory, National Institute of Standards and Technology

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 scientists that operate imaging instruments capable of acquiring large quantities of images. However, manual analyses of terabyte-sized images, 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 do this, multiple computer and computational science problems have to be overcome including image correction, 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 here.

Peter Bajcsy is a computer scientist in the Information Technology Laboratory at the National Institute of Standards and Technology (NIST).

He worked for machine vision, government contracting, and research and educational institutions before joining NIST in 2011.

At NIST, Bajcsy has been leading a project focusing on the application of computational science in biological metrology—specifically stem cell characterization at very large scales. His 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.

Bajcsy has co-authored more than 27 journal papers and eight books or book chapters and approximately 100 conference papers.

He earned his doctorate in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 1997.