Accurate extraction of pores and fractures is a prerequisite for constructing digital rocks for physical property simulation and microstructural response analysis.However,fractures in CT images are similar in grayscal...Accurate extraction of pores and fractures is a prerequisite for constructing digital rocks for physical property simulation and microstructural response analysis.However,fractures in CT images are similar in grayscale to the rock matrix,and traditional algorithms have difficulty to achieve accurate segmentation results.In this study,a dataset containing multiscale fracture information was constructed,and a U-Net semantic segmentation model with a scSE attention mechanism was used to classify shale CT images at the pixel level and compare the results with traditional methods.The results showed that the CLAHE algorithm effectively removed noise and enhanced the fracture information in the dark parts,which is beneficial for further fracture extraction.The Canny edge detection algorithm had significant false positives and failed to recognize the internal information of the fractures.The Otsu algorithm only extracted fractures with a significant difference from the background and was not sensitive enough for fine fractures.The MEF algorithm enhanced the edge information of the fractures and was also sensitive to fine fractures,but it overestimated the aperture of the fractures.The U-Net was able to identify almost all fractures with good continuity,with an MIou and Recall of 0.80 and 0.82,respectively.As the image resolution increases,more fine fracture information can be extracted.展开更多
This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a ...This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a method for contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. The experimental results showes the segmentation based on 3D brain volume and 2D CT slices. The main creative contributions in this paper are: (1) contours segmentation algorithm; (2) edge detector; (3) algorithm evaluation.展开更多
基金funded by the Natural Science Basis Research Plan in Shaanxi Province of China(No.2022JM-147).
文摘Accurate extraction of pores and fractures is a prerequisite for constructing digital rocks for physical property simulation and microstructural response analysis.However,fractures in CT images are similar in grayscale to the rock matrix,and traditional algorithms have difficulty to achieve accurate segmentation results.In this study,a dataset containing multiscale fracture information was constructed,and a U-Net semantic segmentation model with a scSE attention mechanism was used to classify shale CT images at the pixel level and compare the results with traditional methods.The results showed that the CLAHE algorithm effectively removed noise and enhanced the fracture information in the dark parts,which is beneficial for further fracture extraction.The Canny edge detection algorithm had significant false positives and failed to recognize the internal information of the fractures.The Otsu algorithm only extracted fractures with a significant difference from the background and was not sensitive enough for fine fractures.The MEF algorithm enhanced the edge information of the fractures and was also sensitive to fine fractures,but it overestimated the aperture of the fractures.The U-Net was able to identify almost all fractures with good continuity,with an MIou and Recall of 0.80 and 0.82,respectively.As the image resolution increases,more fine fracture information can be extracted.
文摘This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a method for contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. The experimental results showes the segmentation based on 3D brain volume and 2D CT slices. The main creative contributions in this paper are: (1) contours segmentation algorithm; (2) edge detector; (3) algorithm evaluation.
