A hybrid framework for brain tissue segmentation in magnetic resonance images

Chao Li, Jun Sun, Li Liu, Vasile Palade

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Abstract

Having a robust image segmentation strategy is very important in magnetic resonance image (MRI) processing for an effective and early disease detection and diagnosis. Since MRI can present tissues of interest in both morphological and functional images, various segmentation techniques have been employed for this. The algorithms based on Markov random field (MRF) have shown strong abilities in dealing with noisy image segmentation compared to other methods. In this article, inspired by the random drift particle swarm optimization (RDPSO) algorithm, we propose a novel hybrid framework based on a combination of the RDPSO with the hidden MRF model and the expectation–maximization algorithm (HMRF-EM), to be used for MRI segmentation in real-time environments. The proposed hybrid framework is compared with the standalone HMRF-EM method, two other MRF-based stochastic relaxation algorithms, and two widely used brain tissue segmentation toolboxes on both simulated and real MRI datasets. The experimental results prove that the proposed hybrid framework can obtain better segmentation results than most of its competitors and has faster convergence speed than the compared stochastic optimization algorithms.

Original languageEnglish
Pages (from-to)2305-2321
Number of pages17
JournalInternational Journal of Imaging Systems and Technology
Volume31
Issue number4
Early online date3 Aug 2021
DOIs
Publication statusPublished - Dec 2021

Bibliographical note

This is the peer reviewed version of the following article: Li, C, Sun, J, Liu, L & Palade, V 2021, 'A hybrid framework for brain tissue segmentation in magnetic resonance images', International Journal of Imaging Systems and Technology, vol. 31, no. 4, pp. 2305-2321, which has been published in final form at https://dx.doi.org/10.1002/ima.22637. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

Funder

Funding Information:
This work was supported by the National Natural Science Foundation of China (Project Numbers: 61672263, 61702226, 61672265), by the Natural Science Foundation of Jiangsu Province, China (Project Numbers: BK20170200), by the National First-Class Discipline Program of Light Industry Technology and Engineering (Project Number: LITE2018-25), and by the Research Projects of Wuxi Health Commission (Project Number: MS201903).

Funding Information:
This work was supported by the National Natural Science Foundation of China (Project Numbers: 61672263, 61702226, 61672265), by the Natural Science Foundation of Jiangsu Province, China (Project Numbers: BK20170200), by the National First‐Class Discipline Program of Light Industry Technology and Engineering (Project Number: LITE2018‐25), and by the Research Projects of Wuxi Health Commission (Project Number: MS201903).

Funding Information:
National First‐Class Discipline Program of Light Industry Technology and Engineering, Grant/Award Number: LITE2018‐25; National Natural Science Foundation of China, Grant/Award Numbers: 61672263, 61672265, 61702226; Natural Science Foundation of Jiangsu Province, Grant/Award Number: BK20170200; Research Projects of Wuxi Health Commission, Grant/Award Number: MS201903 Funding information

Publisher Copyright:
© 2021 Wiley Periodicals LLC.

Keywords

  • brain tissue segmentation
  • expectation–maximization
  • hidden Markov random field
  • magnetic resonance image
  • random drift particle swarm optimization

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Software
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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