Anatomic modeling from unstructured samples using variational implicit surfaces

Authors:
Terry S. Yoo;Bryan Morse;K. R. Subramanian;Penny Rheingans;Michael J. Ackerman
Affiliations:
National Institutes of Health, Bethesda;Brigham Young University, Provo UT;Univ. of North Carolina Charlotte, Charlotte NC;Univ. Maryland Baltimore County, Baltimore MD;National Institutes of Health, Bethesda, MD
Venue:
SIGGRAPH '05 ACM SIGGRAPH 2005 Courses
Year:
2005

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Abstract

We describe the use of variational implicit surfaces (level sets of an embedded generating function modeled using radial basis interpolants) in anatomic modeling. This technique allows the practitioner to employ sparsely and unevenly sampled data to represent complex biological surfaces, including data acquired as a series of non-parallel image slices. The method inherently accommodates interpolation across irregular spans. In addition, shapes with arbitrary topology are easily represented without interpolation or aliasing errors arising from discrete sampling. To demonstrate the medical use of variational implicit surfaces, we present the reconstruction of the inner surfaces of blood vessels from a series of endovascular ultrasound images.