中文

Journal of Applied Sciences ›› 2026, Vol. 44 ›› Issue (1): 134-148.doi: 10.3969/j.issn.0255-8297.2026.01.009

• Special Issue on Computer Application • Previous Articles     Next Articles

Three-Dimensional Fuzzy Clustering Algorithm Integrating Spatial Texture Features

JIN Zhengyang1, YAN Shaohong1,2,3, ZHANG Yanbo2,3, YAO Xulong2,3, TAO Zhigang4, CHEN Zhiyuan5   

  1. 1. College of Science, North China University of Science and Technology, Tangshan 063210, Hebei, China;
    2. College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, Hebei, China;
    3. Green Intelligent Mining Technology Innovation Center of Hebei Province, Tangshan 063210, Hebei, China;
    4. State Key Laboratory for Deep Geomechanics and Underground Engineering, Beijing 100083, China;
    5. College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, Hebei, China
  • Received:2025-08-01 Published:2026-02-03

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Abstract: 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.

Key words: image segmentation, fuzzy C-means clustering algorithm, gray-level co-occurrence matrix, texture features, rock fractures, human skeleton

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