CaltechAUTHORS
  A Caltech Library Service

Level set segmentation from multiple non-uniform volume datasets

Museth, Ken and Breen, David E. and Zhukov, Leonid and Whitaker, Ross T. (2002) Level set segmentation from multiple non-uniform volume datasets. In: IEEE Visualization, 2002. VIS 2002. IEEE , Piscataway, NJ, pp. 179-186. ISBN 0-7803-7498-3. https://resolver.caltech.edu/CaltechAUTHORS:20161206-145623876

[img] PDF - Published Version
See Usage Policy.

1952Kb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20161206-145623876

Abstract

Typically 3-D MR and CT scans have a relatively high resolution in the scanning X-Y plane, but much lower resolution in the axial Z direction. This non-uniform sampling of an object can miss small or thin structures. One way to address this problem is to scan the same object from multiple directions. In this paper we describe a method for deforming a level set model using velocity information derived from multiple volume datasets with non-uniform resolution in order to produce a single high-resolution 3D model. The method locally approximates the values of the multiple datasets by fitting a distance-weighted polynomial using moving least-squares. The proposed method has several advantageous properties: its computational cost is proportional to the object surface area, it is stable with respect to noise, imperfect registrations and abrupt changes in the data, it provides gain-correction, and it employs a distance-based weighting to ensures that the contributions from each scan are properly merged into the final result. We have demonstrated the effectiveness of our approach on four multi-scan datasets, a Griffin laser scan reconstruction, a CT scan of a teapot and MR scans of a mouse embryo and a zucchini.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1109/VISUAL.2002.1183773DOIPaper
http://ieeexplore.ieee.org/document/1183773/PublisherPaper
Additional Information:© 2002 IEEE. We would like to thank Dr. Alan Barr for his technical assistance, Dr. Jason Wood of the University of Leeds for creating several useful visualization tools, and Ms. Cici Koenig for helping us with our figures. We would also like to thank Dr. John Wood of Childrens Hospital Los Angeles for providing the zucchini dataset, Dr. J. Michael Tyszka of the Division of Diagnostic Radiology at the City of Hope National Medical Center for providing the teapot dataset, Dr, Russell Jacobs of the Caltech Biological Imaging Center for providing the mouse embryo dataset, and the Caltech MultiRes Modeling Group and the Stanford Computer Graphics Laboratory for providing the griffin dataset. This work was supported by National Science Foundation grants #ASC-89-20219, #ACI-9982273 and #ACI-0089915, and the National Institute on Drug Abuse and the National Institute of Mental Health, as part of the Human Brain Project.
Funders:
Funding AgencyGrant Number
NSFASC-89-20219
NSFACI-9982273
NIHACI-0089915
National Institute of Mental Health (NIMH)UNSPECIFIED
National Institute on Drug AbuseUNSPECIFIED
Subject Keywords:Segmentation, visualization, level set models, 3D reconstruction.
Classification Code:1.3.5 [Computer Graphics}: Computational Ge­ometry and Object Modeling-Surface and object representations;
Record Number:CaltechAUTHORS:20161206-145623876
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20161206-145623876
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72604
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:06 Dec 2016 23:24
Last Modified:03 Oct 2019 16:19

Repository Staff Only: item control page