Karimian Alireza, Jafari Simin
Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
Department of Telecommunication Engineering, Faculty of Electrical Engineering, Islamic Azad University of Najafabad, Isfahan, Iran.
J Med Signals Sens. 2015 Oct-Dec;5(4):238-44.
Automatic segmentation of multiple sclerosis (MS) lesions in brain magnetic resonance imaging (MRI) has been widely investigated in the recent years with the goal of helping MS diagnosis and patient follow-up. In this research work, Gaussian mixture model (GMM) has been used to segment the MS lesions in MRIs, including T1-weighted (T1-w), T2-w, and T2-fluid attenuation inversion recovery. Usually, GMM is optimized by using expectation-maximization (EM) algorithm. The drawbacks of this optimization method are, it does not converge to optimal maximum or minimum and furthermore, there are some voxels, which do not fit the GMM model and have to be rejected. So, GMM is time-consuming and not too much efficient. To overcome these limitations, in this research study, at the first step, GMM was applied to segment only T1-w images by using 100 various starting points when the maximum number of iterations was considered to be 50. Then segmentation results were used to calculate the parameters of the other two images. Furthermore, FAST-trimmed likelihood estimator algorithm was applied to determine which voxels should be rejected. The output result of the segmentation was classified in three classes; White and Gray matters, cerebrospinal fluid, and some rejected voxels which prone to be MS. In the next phase, MS lesions were detected by using some heuristic rules. This new method was applied on the brain MRIs of 25 patients from two hospitals. The automatic segmentation outputs were scored by two specialists and the results show that our method has the capability to segment the MS lesions with dice similarity coefficient score of 0.82. The results showed a better performance for the proposed approach, in comparison to those of previous works with less time-consuming.
近年来，为了辅助多发性硬化症（MS）的诊断和患者随访，脑磁共振成像（MRI）中MS病灶的自动分割受到了广泛研究。在这项研究工作中，高斯混合模型（GMM）已被用于分割MRI中的MS病灶，包括T1加权（T1-w）、T2加权（T2-w）和T2液体衰减反转恢复序列图像。通常，GMM通过期望最大化（EM）算法进行优化。这种优化方法的缺点是，它不会收敛到最优的最大值或最小值，而且存在一些不适合GMM模型的体素，必须将其剔除。因此，GMM耗时且效率不高。为了克服这些局限性，在本研究中，第一步，当最大迭代次数设为50时，使用GMM并通过100个不同的起始点来分割仅T1-w图像。然后，分割结果用于计算其他两张图像的参数。此外，应用快速修剪似然估计器算法来确定哪些体素应被剔除。分割的输出结果分为三类：白质和灰质、脑脊液以及一些易于成为MS病灶的被剔除体素。在下一阶段，通过一些启发式规则检测MS病灶。这种新方法应用于来自两家医院的25名患者的脑部MRI。两名专家对自动分割输出进行评分，结果表明我们的方法能够以0.82的骰子相似系数得分分割MS病灶。结果显示，与先前的工作相比，所提出的方法具有更好的性能，且耗时更少。
