National Institute of Standards and Technology

Pyramid-Based 3D Reprojection Computations

Summary

With the current limitations of desktop computers in terms of memory, disk storage and computational power, scientists face several challenges when inspecting terabyte-sized (TB) images. These challenges result from the following:

  • TB images are typically stored as thousands to millions of smaller files that have to be rendered seamlessly and interactively;
  • Images need to be inspected with their surroundings of spatial, temporal and spectral information, and the neighboring image information has to be viewed from multiple viewpoints;
  • Images need to be shared and then accessed by a team of experts.
For volumetric images that are too large for viewing interactively in a browser, our objective is to use the Deep Zoom representation and pre-compute multiple view points of a 3D data set. Figure 1 illustrates the computations needed for converting image representations and for changing view points. We have benchmarked algorithms for such conversions on a Hadoop cluster and on a desktop. In addition, we designed a theoretical model for cluster node utilization in order to request optimal distributed computing resources for such computations based on the parameters of input data.

3d re-projection
Computations involved in re-projecting 3D image from a set of either 2D image cross sections or the Deep Zoom pyramids.

Description

When handling very large images (larger than gigapixel images), there is a need to build a multi-resolution pyramid representation for fast rendering. In addition, the pyramid representations are built for multiple orthogonal view points to expedite rendering. We address this problem by designing five re-projection algorithms for three-dimensional (3D) images to enable their interactive visualization from multiple orthogonal viewpoints using the Deep Zoom pyramid representation. TB-sized 3D images consisting of [x, y, z or t or λ] are represented as an ordered set of either 2D cross sections [x, y] or Deep Zoom pyramids and then re-projected to [x, z] or [y, z] sets.

Five re-projection algorithms for the two 3D image representations are analyzed in terms of their computational time and memory complexities on a single machine and on a computer cluster with the Hadoop platform. We introduce a computational node utilization coefficient that relates algorithmic memory complexity and parameters of computational nodes.

Our contributions lie in the following:

  • designing a new approach to 3D re-projection from a set of Deep Zoom pyramids,
  • characterizing computational memory complexities of re-projection algorithms, and
  • maximizing the speed-up of 3D re-projection computations on Hadoop computer clusters.

Major Accomplishments

The paper: Peter Bajcsy, Antoine Vandecreme, Mary Brady, “Re-projection of Terabyte-Sized Images, “ IEEE International Conference on Big Data, October 6-9, 2013, Santa Clara, CA, USA (poster)(download pdf)

Lead Organizational Unit:

ITL

Staff:

ITL-Software and Systems Division
Information Systems Group

Publications:

The paper: Peter Bajcsy, Antoine Vandecreme, Mary Brady, “Re-projection of Terabyte-Sized Images, “ IEEE International Conference on Big Data, October 6-9, 2013, Santa Clara, CA, USA (poster)(download pdf)

Date created: April 10, 2014 | Last updated: