TY - GEN
T1 - A multi-compartment segmentation framework with homeomorphic level sets
AU - Fan, Xian
AU - Bazin, Pierre Louis
AU - Prince, Jerry L.
PY - 2008
Y1 - 2008
N2 - The simultaneous segmentation of multiple objects is an important problem in many imaging and computer vision applications. Various extensions of level set segmentation techniques to multiple objects have been proposed; however, no one method maintains object relationships, preserves topology, is computationally efficient, and provides an object-dependent internal and external force capability. In this paper, a framework for segmenting multiple objects that permits different forces to be applied to different boundaries while maintaining object topology and relationships is presented. Because of this framework, the segmentation of multiple objects each with multiple compartments is supported, and no overlaps or vacuums are generated. The computational complexity of this approach is independent of the number of objects to segment, thereby permitting the simultaneous segmentation of a large number of components. The properties of this approach and comparisons to existing methods are shown using a variety of images, both synthetic and real.
AB - The simultaneous segmentation of multiple objects is an important problem in many imaging and computer vision applications. Various extensions of level set segmentation techniques to multiple objects have been proposed; however, no one method maintains object relationships, preserves topology, is computationally efficient, and provides an object-dependent internal and external force capability. In this paper, a framework for segmenting multiple objects that permits different forces to be applied to different boundaries while maintaining object topology and relationships is presented. Because of this framework, the segmentation of multiple objects each with multiple compartments is supported, and no overlaps or vacuums are generated. The computational complexity of this approach is independent of the number of objects to segment, thereby permitting the simultaneous segmentation of a large number of components. The properties of this approach and comparisons to existing methods are shown using a variety of images, both synthetic and real.
UR - http://www.scopus.com/inward/record.url?scp=52249098280&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=52249098280&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2008.4587475
DO - 10.1109/CVPR.2008.4587475
M3 - Conference contribution
C2 - 23223164
AN - SCOPUS:52249098280
SN - 9781424422432
T3 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
BT - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
T2 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Y2 - 23 June 2008 through 28 June 2008
ER -