PoreSeg: An unsupervised and interactive-based framework for automatic segmentation of X-ray images of porous materials

X-ray imaging technology has seen immense progress in extracting the internal structure of geomaterials, but the segmentation of images into voids and solids has remained a challenge due to the stochastic nature of sediments. The traditional segmentation techniques suffer from operator bias; deep learning methods entail time/hardware expenses and a large number of segmented images as ground truth (GT). To address these issues, we introduce PoreSeg, a segmentation framework that leverages the convolutional layers and ensemble learning to automatically segment images via one-shot learning. The framework offers two approaches for implementation: (I) unsupervised-based (UB), in which the imported images are segmented automatically without user intervention, and (II) interactive-based (IB), in which the algorithm uses the manually selected regions of interest (ROI) by the user. Both approaches can be used for binarization and multi-mineral segmentation. To evaluate the performance of the algorithm, a comprehensive databank including the X-ray images of 15 sandstones and 5 carbonates was collected. Our findings indicated the promising performance of UB PoreSeg in the majority of cases with an average Intersection Over Union (IoU) score of 0.97. Moreover, it was construed that the IB approach can cover the UB bottlenecks for complex images and multi-mineral segmentation tasks. The IB PoreSeg outperformed trainable Weka and deep learning based on the visual examination. The PoreSeg is a resource saver, does not need GT masks, and minimizes user bias, making it a suitable tool for the segmentation of X-ray images.