Magnetic Resonance Imaging (MRI) plays an important role in neurology, particularly in the precise segmentation of brain tissues. Accurate segmentation is crucial for diagnosing brain injuries and neurodegenerative conditions. We introduce an Enhanced Spatial Fuzzy C-means (esFCM) algorithm for 3D T1 MRI segmentation to three tissues, i.e. White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). The esFCM employs a weighted least square algorithm utilizing the Structural Similarity Index (SSIM) for polynomial bias field correction. It also takes advantage of the information from the membership function of the last iteration to compute neighborhood impact. This strategic refinement enhances the algorithm’s adaptability to complex image structures, effectively addressing challenges such as intensity irregularities and contributing to heightened segmentation accuracy. We compare the segmentation accuracy of esFCM against four variants of FCM, Gaussian Mixture Model (GMM) and FSL and ANTs algorithms using four various dataset, employing three measurement criteria. Comparative assessments underscore esFCM’s superior performance, particularly in scenarios involving added noise and bias fields.The obtained results emphasize the significant potential of the proposed method in the segmentation of MRI images.

磁共振成像 (MRI) 在神经病学中发挥着重要作用，特别是在脑组织的精确分割方面。准确的分割对于诊断脑损伤和神经退行性疾病至关重要。我们引入了增强型空间模糊 C 均值 (esFCM) 算法，用于对三种组织进行 3D T1 MRI 分割，即白质 (WM)、灰质 (GM) 和脑脊液 (CSF)。 esFCM 采用加权最小二乘算法，利用结构相似性指数 (SSIM) 进行多项式偏差场校正。它还利用来自最后一次迭代的隶属函数的信息来计算邻域影响。这种策略性的改进增强了算法对复杂图像结构的适应性，有效解决了强度不规则等挑战，并有助于提高分割精度。我们使用四种不同的数据集，采用三种测量标准，将 esFCM 与 FCM、高斯混合模型 (GMM) 以及 FSL 和 ANTs 算法的四种变体的分割精度进行比较。比较评估强调了 esFCM 的优越性能，特别是在涉及添加噪声和偏差场的情况下。获得的结果强调了所提出的方法在 MRI 图像分割中的巨大潜力。