By Topic

A Parallelized Surface Extraction Algorithm for Large Binary Image Data Sets Based on an Adaptive 3-D Delaunay Subdivision Strategy

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
YingLiang Ma ; King's Coll. London, London ; Kurt Saetzler

In this paper, we describe a novel 3D subdivision strategy to extract the surface of binary image data. This iterative approach generates a series of surface meshes that capture different levels of detail of the underlying structure. At the highest level of detail, the resulting surface mesh generated by our approach uses only about 10 percent of the triangles in comparison to the Marching Cube (MC) algorithm, even in settings where almost no image noise is present. Our approach also eliminates the so-called "staircase effect," which voxel-based algorithms like the MC are likely to show, particularly if nonuniformly sampled images are processed. Finally, we show how the presented algorithm can be parallelized by subdividing 3D image space into rectilinear blocks of subimages. As the algorithm scales very well with an increasing number of processors in a multithreaded setting, this approach is suited to process large image data sets of several gigabytes. Although the presented work is still computationally more expensive than simple voxel-based algorithms, it produces fewer surface triangles while capturing the same level of detail, is more robust toward image noise, and eliminates the above-mentioned "staircase" effect in anisotropic settings. These properties make it particularly useful for biomedical applications, where these conditions are often encountered.

Published in:

IEEE Transactions on Visualization and Computer Graphics  (Volume:14 ,  Issue: 1 )