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.

中文翻译：

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磁共振图像中脑组织分割的混合框架

在磁共振图像 (MRI) 处理中，拥有强大的图像分割策略对于有效和早期的疾病检测和诊断非常重要。由于 MRI 可以在形态和功能图像中呈现感兴趣的组织，因此已为此采用了各种分割技术。与其他方法相比，基于马尔可夫随机场（MRF）的算法在处理噪声图像分割方面表现出很强的能力。在本文中，受随机漂移粒子群优化 (RDPSO) 算法的启发，我们提出了一种基于 RDPSO 与隐藏 MRF 模型和期望最大化算法 (HMRF-EM) 组合的新型混合框架，将用于实时环境中的 MRI 分割。将提出的混合框架与独立的 HMRF-EM 方法进行比较，另外两个基于 MRF 的随机松弛算法，以及两个在模拟和真实 MRI 数据集上广泛使用的脑组织分割工具箱。实验结果证明，所提出的混合框架能够获得比大多数竞争对手更好的分割结果，并且比比较随机优化算法具有更快的收敛速度。