中文摘要：

磁共振成像（Magnetic resonance imaging,MRI）技术以其非介入、无损伤以及不受目标运动影响等特点,已成为临床诊断的重要辅助手段。精确的脑MR图像分割对生物医学研究和临床应用具有重要的指导意义。在实际应用中,脑MR图像中存在的噪声、灰度不均匀性、部分容积效应和低对比度等缺陷,都给脑MR图像的精确分割带来了巨大困难和挑战。本文基于模糊聚类模型的脑MR图像分割问题,从聚类类别数的确定、模型初始化、克服噪声、估计偏移场、克服部分容积效应、数据不确定性描述以及模型扩展7个方面深入阐述了国内外发展现状、应对技巧及改进策略,并分析存在的不足,指出进一步的研究方向。

英文摘要：

Magnetic resonance imaging （MRI） has several advantages over other medical imaging modali- ties, including high contrast among different soft tissues, relatively high spatial resolution across the entire field of view and multi-spectral characteristics. Hence, it has been widely used in quantitative brain imaging studies. Quantitative volumetric measurement and three-dimensional visualization of brain tissues are helpful for pathological evolution analyses, where image segmentation plays an important role. However, MR images suffer from several major artifacts, including intensity inhomogeneity, noise, par tial volume effect and low contrast, which makes MR segmentation remain a challenging topic. Therefore, this paper reviews brain MR image segmentation based on fuzzy clustering model from seven aspects, i. e. , the determination of cluster number, the initialization of model, the robustness to noise, the estimation of intensity inhomogeneity and partial volume, the uncertainty description of data and the model extension. Limitations existing in the available methods are analyzed, and problems in further research are discussed as well.