A Topology Preserving Level Set Method for Geometric Deformable Models

By Xiao Han, Chenyang Xu, Jerry L Prince. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003

563 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e728c62d36f95ace734fccfb4be3a2c02d002a35

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Digital Topology and Geometry in Medical Imaging: A Survey (2015, unknown): “Also, it has been pursued in the context of segmentation [98], [155], [237], tissue classification [177], homotopic dilation and erosion [33], [238], registration [34]–[36] etc.”
An Automatic Learning-Based Framework for Robust Nucleus Segmentation (2016, background): “In addition, although topology preserving level set [31], [32] is proposed, generally implicit models that allow one initial contour to split or merge do not take advantage of known topological constraints.”
Level set evolution without re-initialization: a new variational formulation (2005, background): “In recent years, a large body of work on geometric active contours, i.e., active contours implemented via level set methods, has been proposed to address a wide range of image segmentation problems in image processing and computer vision (cf. [3, 5, 7]).”
Boundary Learning by Optimization with Topological Constraints (2010, background): “In certain medical imaging situations, control of such aspects of topology is especially important [15].”
Cell Population Tracking and Lineage Construction with Spatiotemporal Context (2007, unknown): “Although the topological control of level sets has been studied extensively for image segmentation problems (Han et al., 2003; Ségonne, 2005), its potential for tracking has yet to be fully exploited.”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, methodology): “common approach for topology correction has been the use of deformable surface models [297]–[299].”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “Although topology preserving level set model [245] is proposed, generally it might be more efficient to use parametric models for this case.”
Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces (2005, background): “D surfaces as complex as the cortex of the brain [36]–[38].”
Longitudinal Changes in Cortical Thickness Associated with Normal Aging (2010, methodology): “The final stage in CRUISE uses a topology preserving geometric deformable surface model (Han et al., 2003), initialized at the GM/WM surface, which is driven toward the pial surface using forces derived from the edited GM membership function.”
Topology-Preserving Deep Image Segmentation (2019, methodology): “, connectivity and loop-freeness, have been incorporated into variational [19, 24, 38, 35, 42, 18] and MRF/CRF-based segmentation methods [40, 30, 43, 6, 2, 37, 31, 15].”
Anatomically motivated modeling of cortical laminae (2014, background): “Moreover the surfaces are able to maintain the topology of the cortical boundaries if desired (Han et al., 2003).”
Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach (2006, unknown): “nately, not topology-preserving [49], [53].”
Graph cut based image segmentation with connectivity priors (2008, unknown): “[9] proposed a topology preserving level set method which allows to specify more general topologies.”
Silhouette and stereo fusion for 3D object modeling (2003, background): “Controlling the topology is a more difficult problem but, the authors of [31] have recently proposed an interesting way of avoiding topology changes in level set methods.”