article

Review of brain MRI image segmentation methods

Authors:

S. MashohorAuthors Info & Claims

Artificial Intelligence Review, Volume 33, Issue 3

Pages 261 - 274

https://doi.org/10.1007/s10462-010-9155-0

Published: 01 March 2010 Publication History

Abstract

Brain image segmentation is one of the most important parts of clinical diagnostic tools. Brain images mostly contain noise, inhomogeneity and sometimes deviation. Therefore, accurate segmentation of brain images is a very difficult task. However, the process of accurate segmentation of these images is very important and crucial for a correct diagnosis by clinical tools. We presented a review of the methods used in brain segmentation. The review covers imaging modalities, magnetic resonance imaging and methods for noise reduction, inhomogeneity correction and segmentation. We conclude with a discussion on the trend of future research in brain segmentation.

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Cited By

Dos Santos PScoczynski MAmorim Nogueira SMaffei Loureiro RGonçalves CPacheco Calixto WLi XHandl J(2024)A novel unsupervised segmentation approach for brain computed tomography employing hyperparameter optimizationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664126(2091-2094)Online publication date: 14-Jul-2024
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Antonelli LDe Simone VViola M(2023)Segmenting MR Images Through Texture Extraction and Multiplicative Components OptimizationScale Space and Variational Methods in Computer Vision10.1007/978-3-031-31975-4_39(511-521)Online publication date: 21-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-31975-4_39
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Review of brain MRI image segmentation methods
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Published In

cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 33, Issue 3

March 2010

98 pages

ISSN:0269-2821

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Copyright © Copyright © 2010 Springer Science+Business Media B.V.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 March 2010

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Cited By

Dos Santos PScoczynski MAmorim Nogueira SMaffei Loureiro RGonçalves CPacheco Calixto WLi XHandl J(2024)A novel unsupervised segmentation approach for brain computed tomography employing hyperparameter optimizationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664126(2091-2094)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664126
Sudhish DNair LSivan S(2024)Hybrid similarity measure-based image indexing and Gradient Ladybug Beetle optimization for retrieval of brain tumor using MRIThe Journal of Supercomputing10.1007/s11227-024-06350-z80:16(24051-24078)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s11227-024-06350-z
Antonelli LDe Simone VViola M(2023)Segmenting MR Images Through Texture Extraction and Multiplicative Components OptimizationScale Space and Variational Methods in Computer Vision10.1007/978-3-031-31975-4_39(511-521)Online publication date: 21-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-31975-4_39
Bian Z(2022)MR brain tissue classification based on the spatial information enhanced Gaussian mixture modelTechnology and Health Care10.3233/THC-22800830:S1(81-89)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/THC-228008
Li XZhang WKang ZWang N(2022)Accurate brain extraction on MRI using U-Net trained in two stagesProceedings of the 14th International Conference on Bioinformatics and Biomedical Technology10.1145/3543377.3543381(20-26)Online publication date: 27-May-2022
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Kumar A(2022)Study and analysis of different segmentation methods for brain tumor MRI applicationMultimedia Tools and Applications10.1007/s11042-022-13636-y82:5(7117-7139)Online publication date: 16-Aug-2022
https://dl.acm.org/doi/10.1007/s11042-022-13636-y
Zhang GHuang CJiang JXu WChen JXu X(2022)Denoising of brain magnetic resonance images using a MDB networkMultimedia Tools and Applications10.1007/s11042-021-11521-881:29(41751-41763)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11042-021-11521-8
Khatri IKumar DGupta A(2022)A noise robust kernel fuzzy clustering based on picture fuzzy sets and KL divergence measure for MRI image segmentationApplied Intelligence10.1007/s10489-022-04315-453:13(16487-16518)Online publication date: 8-Dec-2022
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Jyothi PSingh A(2022)Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a reviewArtificial Intelligence Review10.1007/s10462-022-10245-x56:4(2923-2969)Online publication date: 16-Aug-2022
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Kumar DKhatri IGupta AGusain R(2022)Kernel picture fuzzy clustering with spatial neighborhood information for MRI image segmentationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07269-426:22(12717-12740)Online publication date: 1-Nov-2022
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