Avoiding mesh folding in 3D optimal surface segmentation

Authors:
Christian Bauer;Shanhui Sun;Reinhard Beichel
Affiliations:
Deptartment of Electrical and Computer Engineering and The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA;Deptartment of Electrical and Computer Engineering and The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA;Deptartment of Electrical and Computer Engineering and The Iowa Institute for Biomedical Imaging and Deptartment of Internal Medicine, The University of Iowa, Iowa City, IA
Venue:
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Year:
2011

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Abstract

The segmentation of 3D medical images is a challenging problem that benefits from incorporation of prior shape information. Optimal Surface Segmentation (OSS) has been introduced as a powerful and flexible framework that allows segmenting the surface of an object based on a rough initial prior with robustness against local minima. When applied to general 3D meshes, conventional search profiles constructed for the OSS may overlap resulting in defective segmentation results due to mesh folding. To avoid this problem, we propose to use the Gradient Vector Flow field to guide the construction of non-overlapping search profiles. As shown in our evaluation on segmenting lung surfaces, this effectively solves the mesh folding problem and decreases the average absolute surface distance error from 0.82±0.29 mm (mean±standard deviation) to 0.79 ± 0.24 mm.