Literature DB >> 18841864

Live level set: a hybrid method of livewire and level set for medical image segmentation.

Jianhua Yao1, David Chen.   

Abstract

Livewire and level set are popular methods for medical image segmentation. In this article, the authors propose a hybrid method of livewire and level set, termed the live level set (LLS). The LLS replaces the one graph update iteration in the classic livewire with two iterations of graph updates. The first iteration generates an initial contour for a level set computation. The level set distance is then factored back into the cost function in the second iteration of graph update. The authors validated LLS using synthetic images. The results show that the performance of the LLS is superior to both the classic live wire and traditional level set methods in terms of accuracy, reproducibility, smoothness and running time. They also qualitatively evaluated the LLS using real clinical data.

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Year:  2008        PMID: 18841864      PMCID: PMC2673655          DOI: 10.1118/1.2968876

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  4 in total

1.  A shape-based approach to the segmentation of medical imagery using level sets.

Authors:  Andy Tsai; Anthony Yezzi; William Wells; Clare Tempany; Dewey Tucker; Ayres Fan; W Eric Grimson; Alan Willsky
Journal:  IEEE Trans Med Imaging       Date:  2003-02       Impact factor: 10.048

2.  An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision.

Authors:  Yuri Boykov; Vladimir Kolmogorov
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2004-09       Impact factor: 6.226

3.  A nonparametric statistical method for image segmentation using information theory and curve evolution.

Authors:  Junmo Kim; John W Fisher; Anthony Yezzi; Müjdat Cetin; Alan S Willsky
Journal:  IEEE Trans Image Process       Date:  2005-10       Impact factor: 10.856

4.  Adaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification.

Authors:  Suyash P Awate; Tolga Tasdizen; Norman Foster; Ross T Whitaker
Journal:  Med Image Anal       Date:  2006-08-21       Impact factor: 8.545

  4 in total
  3 in total

1.  Iterative mesh transformation for 3D segmentation of livers with cancers in CT images.

Authors:  Difei Lu; Yin Wu; Gordon Harris; Wenli Cai
Journal:  Comput Med Imaging Graph       Date:  2015-01-28       Impact factor: 4.790

2.  Automatic anatomy recognition via multiobject oriented active shape models.

Authors:  Xinjian Chen; Jayaram K Udupa; Abass Alavi; Drew A Torigian
Journal:  Med Phys       Date:  2010-12       Impact factor: 4.071

3.  GC-ASM: Synergistic Integration of Graph-Cut and Active Shape Model Strategies for Medical Image Segmentation.

Authors:  Xinjian Chen; Jayaram K Udupa; Abass Alavi; Drew A Torigian
Journal:  Comput Vis Image Underst       Date:  2013-05       Impact factor: 3.876

  3 in total

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