传统的模糊C均值（fuzzy C-means,FCM）聚类算法受初始聚类中心和噪声点的影响较大，且这些影响在复杂环境或是高维度空间中会被进一步放大。针对这一问题提出了一种融合空间纹理特征的三维FCM算法，旨在提取研究对象内部因组成成分分布不均匀而形成的密度差异显著区域。首先，参考二维空间灰度共生矩阵及平面纹理特征理论，将其延拓到三维空间，用以刻画空间纹理特征；其次，利用对比度纹理特征来优选出初始聚类中心；最后，将相异性纹理特征与传统FCM算法目标函数相融合，以提高算法的抗噪能力。在裂隙提取仿真模拟实验中，本文算法的目标提取准确率达到99.39 %，较传统FCM算法（准确率为65.31 %）提高了34 %，验证了新型算法提取研究对象内部密度差异显著区域的可行性。在实际应用中，新型算法对于人体胸部骨骼的识别与提取也表现出优越的适用性。
The traditional fuzzy C-means (FCM) clustering algorithm is highly sensitive to the initial cluster centers and the noise points. These limitations become more pronounced in complex environments or high-dimensional spaces. To overcome these issues, this study proposed a three-dimensional FCM algorithm integrating spatial texture features. The algorithm was designed to identify regions with noticeable density differences caused by uneven distribution of internal components in the analyzed objects. First, the method extended the two-dimensional gray-level co-occurrence matrix and planar texture feature theory into three-dimensional space to describe spatial texture features. Next, contrast texture features were used to improve the selection of initial cluster centers. Finally, dissimilarity texture features were integrated into the conventional objective function of FCM algorithm to enhance noise resistance. In a simulated experiment on fracture extraction, the proposed algorithm achieved an accuracy of 99.39%, representing a 34% improvement over the traditional FCM algorithm (accuracy of 65.31%). These results confirm the effectiveness of the new algorithm in extracting regions with noticeable density differences inside the analyzed objects. In practical applications, the new algorithm shows superior performance in identifying and extracting human thoracic skeleton.
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