Altered igneous reservoirs have low porosity and permeability,compact structure and certain heterogeneity.A simple digital core with certain generality and multi-parameter constraints can be con-structed to characteri...Altered igneous reservoirs have low porosity and permeability,compact structure and certain heterogeneity.A simple digital core with certain generality and multi-parameter constraints can be con-structed to characterize the microscopic pore structure and mineral composition.In this paper,based on core X-ray,CT images and whole-rock mineral analysis,threshold segmentation of mass content and grayscale distribution of various minerals in different lithologies of igneous rocks in the buried hill of Huizhou depression is carried out to construct digital core of altered igneous rocks.The results show that after converting the mineral mass content into volume content,the minerals of altered igneous rocks in Huizhou depression can be classified into components.According to the range of grayscale value,components can be divided into six parts.Due to the difference of the content of components in different lithologies of igneous rocks,differentiated grayscale threshold segmentation is needed to obtain the digital core for a single lithology.The final digital core generation process includes two steps:building a single component digital core,and stacking and combining.This kind of universal digital core model can support the subsequent pore scale numerical simulation and comprehensive rock physics research.展开更多
Spectral computed tomography(CT) based on photon counting detectors(PCDs) is a well-researched topic in the field of X-ray imaging. When PCD is applied in a spectral CT system, the PCD energy thresholds must be carefu...Spectral computed tomography(CT) based on photon counting detectors(PCDs) is a well-researched topic in the field of X-ray imaging. When PCD is applied in a spectral CT system, the PCD energy thresholds must be carefully selected, especially for K-edge imaging, which is an important spectral CT application. This paper presents a threshold selection method that yields better-quality images in K-edge imaging. The main idea is to optimize the energy thresholds ray-by-ray according to the targeted component coefficients, followed by obtaining an overall optimal energy threshold by frequency voting. A low-dose pre-scan is used in practical implementations to estimate the line integrals of the component coefficients for the basis functions. The variance of the decomposed component coefficients is then minimized using the Cramer–Rao lower bound method with respect to the energy thresholds. The optimal energy thresholds are then used to take a full scan and gain better image reconstruction with less noise than would be given by a full scan using the non-optimal energy thresholds. Simulations and practical experiments on imaging iodine and gadolinium solutions, which are commonly used as contrast agents in medical applications, were used to validate the method. The noise was significantly reduced with the same dose relative to the non-optimal energy thresholds in both simulations and in practical experiments.展开更多
Knee Osteoarthritis(OA)is a joint disease that is commonly observed in people around the world.Osteoarthritis commonly affects patients who are obese and those above the age of 60.A valid knee image was generated by C...Knee Osteoarthritis(OA)is a joint disease that is commonly observed in people around the world.Osteoarthritis commonly affects patients who are obese and those above the age of 60.A valid knee image was generated by Computed Tomography(CT).In this work,efficient segmentation of CT images using Elephant Herding Optimization(EHO)optimization is implemented.The initial stage employs,the CT image normalization and the normalized image is incited to image enhancement through histogram correlation.Consequently,the enhanced image is segmented by utilizing Niblack and Bernsen algorithm.The(EHO)optimized outcome is evaluated in two steps.The initial step includes image enhancement with the measure of Mean square error(MSE),Peak signal to noise ratio(PSNR)and Structural similarity index(SSIM).The following step includes the segmentation which includes the measure ofAccuracy,Sensitivity and Specificity.The comparative analysis of EHO provides 95%of accuracy,94%of specificity and 93%of sensitivity than that of Active contour and Otsu threshold.展开更多
基金Supported by Project of the National Natural Science Foundation of China (No. 42072323)
文摘Altered igneous reservoirs have low porosity and permeability,compact structure and certain heterogeneity.A simple digital core with certain generality and multi-parameter constraints can be con-structed to characterize the microscopic pore structure and mineral composition.In this paper,based on core X-ray,CT images and whole-rock mineral analysis,threshold segmentation of mass content and grayscale distribution of various minerals in different lithologies of igneous rocks in the buried hill of Huizhou depression is carried out to construct digital core of altered igneous rocks.The results show that after converting the mineral mass content into volume content,the minerals of altered igneous rocks in Huizhou depression can be classified into components.According to the range of grayscale value,components can be divided into six parts.Due to the difference of the content of components in different lithologies of igneous rocks,differentiated grayscale threshold segmentation is needed to obtain the digital core for a single lithology.The final digital core generation process includes two steps:building a single component digital core,and stacking and combining.This kind of universal digital core model can support the subsequent pore scale numerical simulation and comprehensive rock physics research.
基金supported by Grants from National key research and development program(No.2016YFF0101304)the National Natural Science Foundation of China(Nos.61771279,11435007)
文摘Spectral computed tomography(CT) based on photon counting detectors(PCDs) is a well-researched topic in the field of X-ray imaging. When PCD is applied in a spectral CT system, the PCD energy thresholds must be carefully selected, especially for K-edge imaging, which is an important spectral CT application. This paper presents a threshold selection method that yields better-quality images in K-edge imaging. The main idea is to optimize the energy thresholds ray-by-ray according to the targeted component coefficients, followed by obtaining an overall optimal energy threshold by frequency voting. A low-dose pre-scan is used in practical implementations to estimate the line integrals of the component coefficients for the basis functions. The variance of the decomposed component coefficients is then minimized using the Cramer–Rao lower bound method with respect to the energy thresholds. The optimal energy thresholds are then used to take a full scan and gain better image reconstruction with less noise than would be given by a full scan using the non-optimal energy thresholds. Simulations and practical experiments on imaging iodine and gadolinium solutions, which are commonly used as contrast agents in medical applications, were used to validate the method. The noise was significantly reduced with the same dose relative to the non-optimal energy thresholds in both simulations and in practical experiments.
基金This research work was fully supported by King Khalid University,Abha,Kingdom of Saudi Arabia,for funding this work through a General Research Project under grant number RGP/119/42.
文摘Knee Osteoarthritis(OA)is a joint disease that is commonly observed in people around the world.Osteoarthritis commonly affects patients who are obese and those above the age of 60.A valid knee image was generated by Computed Tomography(CT).In this work,efficient segmentation of CT images using Elephant Herding Optimization(EHO)optimization is implemented.The initial stage employs,the CT image normalization and the normalized image is incited to image enhancement through histogram correlation.Consequently,the enhanced image is segmented by utilizing Niblack and Bernsen algorithm.The(EHO)optimized outcome is evaluated in two steps.The initial step includes image enhancement with the measure of Mean square error(MSE),Peak signal to noise ratio(PSNR)and Structural similarity index(SSIM).The following step includes the segmentation which includes the measure ofAccuracy,Sensitivity and Specificity.The comparative analysis of EHO provides 95%of accuracy,94%of specificity and 93%of sensitivity than that of Active contour and Otsu threshold.
基金国家重点基础研究发展规划(973)(the National Grand Fundamental Research 973 Program of China under Grant No.2003CB716104)国家自然科学基金(the National Natural Science Foundation of China under Grant No.30730036)+1 种基金广东省科技计划项目(No.2007B010400058)广州市科技计划项目(No.2007Z3- E0031)
