Image segmentation is one of the most critical tasks in Magnetic Resonance (MR) images analysis. Since the performance of most current image segmentation methods is suffered by noise and intensity non-uniformity artifact (INU), a precise and artifact resistant method is desired. In this work, we propose a new segmentation method combining a new Hidden Markov Random Field (HMRF) model and a novel hybrid metaheuristic method based on Cuckoo search (CS) and Particle swarm optimization algorithms (PSO). The new model uses adaptive parameters to allow balancing between the segmented components of the model. In addition, to improve the quality of searching solutions in the Maximum a posteriori (MAP) estimation of the HMRF model, the hybrid metaheuristic algorithm is introduced. This algorithm takes into account both the advantages of CS and PSO algorithms in searching ability by cooperating them with the same population in a parallel way and with a solution selection mechanism. Since CS and PSO are performing exploration and exploitation in the search space, respectively, hybridizing them in an intelligent way can provide better solutions in terms of quality. Furthermore, initialization of the population is carefully taken into account to improve the performance of the proposed method. The whole algorithm is evaluated on benchmark images including both the simulated and real MR brain images. Experimental results show that the proposed method can achieve satisfactory performance for images with noise and intensity inhomogeneity, and provides better results than its considered competitors.

中文翻译：

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通过隐马尔可夫随机场和混合元启发式算法分割 MR 脑图像


图像分割是磁共振（MR）图像分析中最关键的任务之一。由于大多数当前图像分割方法的性能受到噪声和强度不均匀伪影（INU）的影响，因此需要一种精确且抗伪影的方法。在这项工作中，我们提出了一种新的分割方法，结合了新的隐马尔可夫随机场（HMRF）模型和基于布谷鸟搜索（CS）和粒子群优化算法（PSO）的新型混合元启发式方法。新模型使用自适应参数来实现模型分段组件之间的平衡。此外，为了提高HMRF模型最大后验（MAP）估计中搜索解的质量，引入了混合元启发式算法。该算法综合了CS和PSO算法在搜索能力方面的优势，通过并行方式与相同种群进行协作，并具有解选择机制。由于CS和PSO分别在搜索空间中进行探索和利用，因此以智能方式将它们混合可以在质量方面提供更好的解决方案。此外，仔细考虑了群体的初始化，以提高所提出方法的性能。整个算法在基准图像上进行评估，包括模拟和真实的 MR 大脑图像。实验结果表明，该方法对于具有噪声和强度不均匀性的图像可以取得令人满意的性能，并且比其考虑的竞争对手提供更好的结果。