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Separation of Brain Tissues in MRI based on Multi-Dimensional FCM and Spatial Information

Ghasemi, Jamal and Ghaderi, Reza and Karami Mollaei, Mohamed Reza and Hojjatoleslami, Ali (2011) Separation of Brain Tissues in MRI based on Multi-Dimensional FCM and Spatial Information. In: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 247 -251. ISBN 978-1-61284-180-9. E-ISBN 978-1-61284-181-6. (doi:10.1109/FSKD.2011.6019589) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/FSKD.2011.6019589

Abstract

Due to intensity non-uniformity (INU) and noise brain magnetic resonance image (MRI) segmentation is a complicated concern. Many methods have been presented to overcome brain MRI segmentation. Among these methods, using fuzzy c-means (FCM) is introduced as an effective strategy. Spatial information cannot be considered at a standard FCM. Therefore, many methods have been presented to optimize standard FCM with optimization of objective function. In this research work, a novel method has been proposed for brain MRI segmentation (BMS) based on multi-dimensional standard FCM. In this technique, different features of neighboring pixels such as mean, standard deviation and singular value in combination with pixel intensity has been used for typical pixel segmentation. The results have been evaluated against manual segmentation on a publicly available dataset.

Item Type: Book section
DOI/Identification number: 10.1109/FSKD.2011.6019589
Uncontrolled keywords: image segmentation; magnetic resonance imaging; accuracy; biomedical imaging; noise; brain; educational institutions
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science) > QA297 Numerical analysis
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
R Medicine > RC Internal medicine > RC321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculties > Sciences > School of Biosciences > Biomedical Research Group
Depositing User: S.A. Hojjatoleslami
Date Deposited: 12 May 2011 17:04 UTC
Last Modified: 04 Oct 2019 08:45 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/27766 (The current URI for this page, for reference purposes)
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