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Literature DB >> 19163571

Comparison of tissue segmentation algorithms in neuroimage analysis software tools.

On Tsang¹, Ali Gholipour, Nasser Kehtarnavaz, Kaundinya Gopinath, Richard Briggs, Issa Panahi.

Abstract

Accurate segmentation of different brain tissues is of much importance in magnetic resonance imaging. This paper presents a comparison of the existing segmentation algorithms that are deployed in the neuroimaging community as part of two widely used software packages. The results obtained in this comparison can be used to select the appropriate segmentation algorithm for the neuroimaging application of interest. In addition to the entire brain area, a comparison is carried out for the subcortical region of the brain in terms of its gray matter composition.

Mesh：

Year: 2008 PMID： 19163571 DOI： 10.1109/IEMBS.2008.4650068

Source DB: PubMed Journal: Conf Proc IEEE Eng Med Biol Soc ISSN： 1557-170X

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Cited

15 in total

1. Foibles, follies, and fusion: web-based collaboration for medical image labeling.

Authors: Bennett A Landman; Andrew J Asman; Andrew G Scoggins; John A Bogovic; Joshua A Stein; Jerry L Prince
Journal: Neuroimage Date: 2011-08-02 Impact factor: 6.556

2. Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights.

Authors: Yangming Ou; Hamed Akbari; Michel Bilello; Xiao Da; Christos Davatzikos
Journal: IEEE Trans Med Imaging Date: 2014-06-13 Impact factor: 10.048

Comparison of tissue segmentation algorithms in neuroimage analysis software tools.

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2. Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights.

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7. Simple paradigm for extra-cerebral tissue removal: algorithm and analysis.

8. Robust Intensity Standardization in Brain Magnetic Resonance Images.

9. Multi-atlas learner fusion: An efficient segmentation approach for large-scale data.

10. Non-local statistical label fusion for multi-atlas segmentation.