基于核聚类的砂岩图像孔隙分割方法

Sandstone pore image segmentation based on kernel clustering

砂岩孔隙识别是研究孔隙结构的一个重要步骤,采用通用的图像分割算法不易得到理想的图像孔隙分割效果,为此提出了一种使用EfficientNetV2-S模型和核K-Means聚类技术对孔隙进行分割的方法。首先,获得砂岩图像的超像素集合,使用超像素方法预分割输入的致密砂岩图像,构建带标签的孔隙与非孔隙图像库;然后,应用EfficientNetV2-S模型提取砂岩图像的孔隙和非孔隙的语义特征,并结合迁移学习的方法,使用有限的砂岩图像的孔隙和非孔隙样本进行EfficientNetV2-S模型参数学习;最后,设计了一种基于K-Means聚类的区域合并方法——NTK-KCoP方法,根据超像素的语义特征、灰度特征和边缘特征构建目标函数,再由聚类结果合并超像素得到完整的孔隙区域。砂岩CT图像的实验结果验证了所提出的孔隙分割方法的适用性和有效性。

Sandstone pore identification is an important step in studying pore structure.It is difficult to achieve ideal results using general image segmentation algorithms.This paper proposes a method for pore segmentation using EfficientNetV2-S and kernel K-Means clustering.First,a superpixel collection of sandstone images is obtained,and the input tight sandstone images are pre-segmented using a superpixel method to construct a library of labeled pore and non-pore images.Then,the EfficientNetV2-S model is applied to extract the semantic features of pores and non-pores in sandstone images.The semantic features are combined with a transfer learning method to learn EfficientNetV2-S model parameters using a limited number of pore and non-pore samples in sandstone images.Finally,a region merging method based on K-Means clustering is designed to construct an objective function by combining the semantic features,grayscale features and edge features of superpixels,which are then merged according to clustering results to obtain a complete pore image.Experiments on sandstone CT images verify the applicability and effectiveness of the pore segmentation method proposed in this paper.