Techniques to derive geometries for image‐based Eulerian computations. Issue 3 (2014) - Seth Dillard; James Buchholz; Sarah Vigmostad; Hyunggun Kim; H.S. Udaykumar
<x xml:space="preserve"> Abstract </x> Purpose – 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.Design/methodology/approach – 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. Findings – 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. Originality/value – 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. <ack> <x xml:space="preserve"> Acknowledgements </x> This work was in part supported by grants from the AFOSR Computational Mathematics program (Program Manager: Dr Fariba Fahroo) and Flow Interactions and Control program (Program Manager: Dr Doug Smith), from the AFRL‐RWPC (Computational Mechanics Branch, Eglin AFB, Program Manager: Dr Michael E. Nixon), and from the National Institutes of Health (R01 HL109597). The authors would also like to express their gratitude to Dr Michael Nixon, Dr Martin Schmidt and Dr Joel Stewart from AFRL‐RWPC for providing the X‐ray CT microstructure images used in this work, to Dr Eric Tytell for providing the swimming American eel video sequence, to Jim Akkala for providing the flexible panel video sequence, and to Dr Yonghoon Rim for providing the echocardiographic image of the mitral valve. Without their generosity this study would not have been possible. </ack>