Yazdani S, Yusof R, Karimian A, Riazi A H, Bennamoun M
Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100 Jalan Semarak, Kuala Lumpur, Malaysia.
Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan 81745, Iran.
Comput Math Methods Med. 2015;2015:829893. doi: 10.1155/2015/829893. Epub 2015 May 18.
Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets.
脑磁共振成像（MRI）分割是发现脑结构以及诊断不同脑部疾病中细微解剖变化的重要课题。然而，由于存在多种伪影，脑组织分割仍然是一项具有挑战性的任务。本文的目的是改进在磁共振图像（MRI）中将脑自动分割为灰质、白质和脑脊液的方法。我们提出了一种自动混合图像分割方法，该方法将改进的统计期望最大化（EM）方法与空间信息相结合，并结合支持向量机（SVM）。通过在真实数据和模拟图像上进行实验表明，该组合方法比其单独技术能获得更准确的结果。在合成MRI和真实MRI上均进行了实验。将所提技术的结果与手动分割结果以及基于来自互联网脑分割库（IBSR）的真实T1加权扫描和来自BrainWeb的模拟图像的其他方法进行了评估比较。计算卡帕指数以评估所提框架相对于真实情况和专家分割的性能。结果表明，所提组合方法在模拟MRI和真实脑数据集上均取得了令人满意的结果。
