X-ray computed tomography is an efficient method for quantitatively estimating the characteristics and heterogeneity of shales in three dimensions. A threshold is commonly used to separate pore-fractures from the background image. However, few studies have focused on the multi-component segmentation of computed tomography images. To obtain the distribution characteristics of different components in three dimensions, a segmentation method was proposed that combines a multi-Otsu thresholding algorithm with scanning electron microscopy. The gray value distributions of different components were first determined using this method. Then, the shale components were divided into several groups based on these gray values. The threshold of each component group was determined using the multi-Otsu thresholding algorithm. The computed tomography image stacks of two shale samples were processed using this segmentation method, and another computed tomography image stack was used to verify the method. The results showed that (1) the multi-component segmentation method can successfully segment computed tomography image stacks using the calculated values determined by computed tomography, which agree well with the measured values obtained from X-ray diffraction, total organic carbon, and porosity tests in the laboratory; (2) samples with similar provenances and mineral compositions have the same gray value distribution in the back scattering scanning electron microscopy and computed tomography images; (3) this method is superior in both the effectiveness and efficiency of the computed tomography image stack segmentation of samples according to the gray value distribution, as determined by samples with similar provenances and mineral compositions.
Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography image
