Level-set evolution with region competition: automatic 3-D segmentation of brain tumors

By Sean Ho, Elizabeth Bullitt, Guido Gerig. Object recognition supported by user interaction for service robots, 2002

308 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b43c95029359db1c3de52d0cec818fb5c3d0ce9c

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Citations in the corpus

A brain tumor segmentation framework based on outlier detection (2004, background): “The level set evolution is governed by the following equation (Ho et al., 2002):”
Lesion identification using unified segmentation-normalisation models and fuzzy clustering (2008, background): “For example, the level-set evolution framework, with deformable models is based on cortical thickness and signal gradients (Colliot et al., 2006b; Ho et al., 2002; Xie et al., 2005).”
Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR (2012, methodology): “Mostly, a learning method is combined with a regularization step, e.g. by modeling the boundary [8,11], or by applying a variant of a random ﬁeld spatial prior (MRF/CRF) [4,7,16].”
Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification (2008, unknown): “, level-sets [13] and active contours [16]) is promising but is generally iterative in nature and therefore sensitive to initialization, which, unless interactive, is nearly as difficult as the entire segmentation for brain tumor.”
Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation (2011, methodology): “Thirdly, a new strategy, directed by fuzzy clustering, is proposed to regularize level set evolution, which is different from other methods [10,13,14].”
A survey of MRI-based brain tumor segmentation methods (2014, background): “Since snakes with constant propagation need careful initialization and can leak through weak or missing boundary parts, level-set snakes were proposed and had more signiﬁcant advantages than conventional statistical classiﬁcation and mathematical morphology [121] .”
State of the art survey on MRI brain tumor segmentation. (2013, methodology): “Model based techniques such as parametric and geometric deformable models (level sets), are mostly employed in volumetric (3 D ) image segmentation [16,43,44].”
Unbiased diffeomorphic atlas construction for computational anatomy. (2004, background): “Most digital brain atlases so far are based on a single subject's anatomy (Ho et al., 2002; Warfield et al., 2002).”
A survey of MRI-based medical image analysis for brain tumor studies (2013, methodology): “Ho et al. (2002) derived a tumor probability map from the difference image between T1 and T1c, which formed the basis for the evolution of a region-competition level-set algorithm applied.”