High-Throughput Image Reconstruction and Analysis

Explore the latest developments in bioimaging, image analysis, and data mining with the first comprehensive book on innovative high-performance computing (HPC) techniques that are facilitating never-before research capabilities and applications. This unique resource demonstrates how HPC can solve the data bottleneck created by today's microscopy techniques. It provides state-of-the-art methods and algorithms for building complex models from large biological datasets and solving data analysis and interpretation problems.
This innovative volume surveys the latest image acquisition advances in serial block face techniques in scanning electron microscopy, knife-edge scanning microscopy, and 4D imaging of multi-component biological systems. The book introduces parallel processing for biological applications. You learn advanced parallelization techniques for decomposing a problem domain and mapping it onto a parallel processing architecture using the message-passing interface (MPI) and OpenMP. Case studies show how these techniques have been successfully used in simulation tasks, data mining, and graphical visualization of biological datasets. You also find coverage of methods for developing scalable biological image databases and for facilitating greater interactive visualization of large image sets.

"Explore the latest developments in bioimaging, image analysis, and data mining with the first comprehensive book on innovative high-performance computing (HPC) techniques that are facilitating never-before research capabilities and applications. This unique resource demonstrates how HPC can solve the data bottleneck created by today's microscopy techniques. It provides state-of-the-art methods and algorithms for building complex models from large biological datasets and solving data analysis and interpretation problems." This innovative volume surveys the latest image acquisition advances in serial block face techniques in scanning electron microscopy, knife-edge scanning microscopy, and 4D imaging of multi-component biological systems. The book introduces parallel processing for biological applications. You learn advanced parallelization techniques for decomposing a problem domain and mapping it onto a parallel processing architecture using the message-passing interface (MPI) and OpenMP. Case studies show how these techniques have been successfully used in simulation tasks, data mining, and graphical visualization of biological datasets. You also find coverage of methods for developing scalable biological image databases and for facilitating greater interactive visualization of large image sets.

A. Ravishankar Rao is a research staff member with the Biometaphorical Computing Group at the IBM T.J. Watson Research Center, Yorktown Heights, New York. He is also an associate editor of the journals Pattern Recognition and Machine Vision and Applications. Dr. Rao received his B.Tech degree in electrical engineering from the Indian Institute of Technology (Kanpur), and his M.S. and Ph.D. degrees in computer engineering from the University of Michigan, Ann Arbor. He was named a Master Inventor at IBM Research in 2004.

Guillermo A. Cecchi is a research staff member with the Biometaphorical Computing Group at the IBM T.J. Watson Research Center, Yorktown Heights, New York. Dr. Cecchi received his M.Sc. in physics at the University of La Plata, Argentina and his Ph.D. in physics and biology at The Rockefeller University. He also served as a postdoctoral fellow at Cornell University.