A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model

By Luminita A. Vese, Tony F. Chan. International Journal of Computer Vision, 2002

2,747 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/35f87841b45e820bc1e1bfc66ac85b6d313d6f05

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In Computational Theory and Mathematics

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Citations in the corpus

Video Segmentation with Superpixels (2012, background): “[3–9]) and there is common agreement to use multiple similarities based on brightness, color and texture over local image patches to achieve best image segmentation performance.”
A multi-material level set-based topology optimization of flexoelectric composites (2018, positive): “To remove these shortcomings, we follow [21] and adopt the vector LS approach [22] where a number of m level set function partitions the design domain D into n = 2m overlapping regions, ωk(k = 1, .”
A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI (2011, methodology): “In [20], [22], level set methods were proposed as PS models for image segmentation.”
Level set and total variation regularization for elliptic inverse problems with discontinuous coefficients (2004, methodology): “Here, we state some of the details of the level set idea, following Osher and Sethian [35], Osher and Fedkiw [34] and Vese and Chan [ 44 ].”
Simultaneous Monocular 2D Segmentation, 3D Pose Recovery and 3D Reconstruction (2012, methodology): “A first such attempt was made in [1], where a level set-based Chan-Vese like energy function [5], with an added 2D shape term, is minimised alternately, first in an unconstrained manner, then as a function of the 6 DoF 3D pose of the known 3D model.”
Design of multimaterial compliant mechanisms using level-set methods (2005, methodology): “developed in [40] as an adaptation of an elegant multi-phase level set scheme developed in [ 41 ] for digital image segmentation.”
Robust Object Tracking with Online Multiple Instance Learning (2011, neutral): “It is plausible that the ideas presented here can be applied to other types of tracking problems like tracking multiple objects (e.g., [19]), tracking contours (e.g., [20], [21]), or tracking deformable objects (e.g., [22]), but this is outside the scope of our work.”
State-of-the-Art Methods for Brain Tissue Segmentation: A Review (2017, background): “ima irrespective of initial contour choice [12], [43], [53], [54].”
Shape optimization with a level set based mesh evolution method (2014, positive): “As exampliﬁed in [5], this model extends to the optimization of the interfaces between more than two materials: one then relies on the use of m level set functions for representing up to 2 m materials, according to the so-called color level set method [66].”
Active contours driven by local Gaussian distribution fitting energy (2009, methodology): “4(c), the intermediate results of the PS model [14], LBF model, and our method are shown in the second, third and fourth row, respectively.”
Electrical impedance tomography using level set representation and total variational regularization (2005, methodology): “Applying idea from Vese and Chan [ 39 ], we can extend the level set function representation of q(x )i n(3) in the case where the domain X contains more than two materials.”
Implicit Active Contours Driven by Local Binary Fitting Energy (2007, methodology): “Similarly, Vese and Chan's piecewise constant model in a multiphase level set framework [17] is still not suitable for images with intensity inhomogeneity.”
A Level Set Approach to Image Segmentation With Intensity Inhomogeneity (2016, positive): “Many methods have been proposed to simplify or modify the functional [1], [8], which will be reviewed afterward.”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “On the other hand, recently the region-based energies [223], [224], which are based on the Mumford–Shah functional [225],”
Wound intensity correction and segmentation with convolutional neural networks (2017, methodology): “In 2002, Chan et al. extended the CV model by using a multiphase energy initial framework scheme for piecewise constants (PCs) [13] and piecewise smooth (PS) [14].”
Non-local Deep Features for Salient Object Detection (2017, background): “(1) and (2), the most common unsupervised approaches to optimize these employ level set base curve evolution techniques [8, 42], generalized Bayesian criteria using the variational principle, and simulated annealing [51].”