title: High performance computing environment for multidimensional image analysis.
abstract: BACKGROUND
  The processing of images acquired through microscopy is a challenging task due to the large size of datasets (several gigabytes) and the fast turnaround time required. If the throughput of the image processing stage is significantly increased, it can have a major impact in microscopy applications.
  
  
  RESULTS
  We present a high performance computing (HPC) solution to this problem. This involves decomposing the spatial 3D image into segments that are assigned to unique processors, and matched to the 3D torus architecture of the IBM Blue Gene/L machine. Communication between segments is restricted to the nearest neighbors. When running on a 2 Ghz Intel CPU, the task of 3D median filtering on a typical 256 megabyte dataset takes two and a half hours, whereas by using 1024 nodes of Blue Gene, this task can be performed in 18.8 seconds, a 478x speedup.
  
  
  CONCLUSIONS
  Our parallel solution dramatically improves the performance of image processing, feature extraction and 3D reconstruction tasks. This increased throughput permits biologists to conduct unprecedented large scale experiments with massive datasets.
authors.affiliation: IBM T,J, Watson Research Center, Yorktown Heights, NY 10598, USA. ravirao@us.ibm.com
authors.forename: A Ravishankar
authors.initials: AR
authors.lastname: Rao
authors.forename: Guillermo A
authors.initials: GA
authors.lastname: Cecchi
authors.forename: Marcelo
authors.initials: M
authors.lastname: Magnasco
mesh_terms: D000465:Algorithms; D003205:Computing Methodologies; D003628:Database Management Systems; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008853:Microscopy