Techniques to derive geometries for image-based Eulerian computations.
OBJECTIVE
The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted.


METHODS
Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures.


RESULTS
While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics.


CONCLUSIONS
It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.
Mechanical and Industrial Engineering, University of Iowa, Iowa City, Iowa, USA.
Seth
S
Dillard
Mechanical and Industrial Engineering, University of Iowa, Iowa City, Iowa, USA.
James
J
Buchholz
Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA.
Sarah
S
Vigmostad
Internal Medicine, Division of Cardiology, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Hyunggun
H
Kim
Mechanical and Industrial Engineering, University of Iowa, Iowa City, Iowa, USA.
H S
HS
Udaykumar
De-noising; Eulerian fluid and solid computation; Image-based modeling; Level sets; Segmentation