通常G IS技术应用到野外水文地质调查工作时需要花费大量时间准备数据,导致其不能及时参与到野外工作中。为了使G IS技术及时在1∶5万岩溶水文地质综合调查野外工作中发挥作用,笔者以湖南新田河调查为例,在M apG IS系统中根据原始资料...通常G IS技术应用到野外水文地质调查工作时需要花费大量时间准备数据,导致其不能及时参与到野外工作中。为了使G IS技术及时在1∶5万岩溶水文地质综合调查野外工作中发挥作用,笔者以湖南新田河调查为例,在M apG IS系统中根据原始资料复杂程度的不同对数据进行处理和配准,及时制作了信息丰富的野外工作用图,建立了工作区专业数据和数据录入、分析平台,在野外调查工作开展时及时录入数据并加以分析,及时修订地质边界,检查野外定点准确度,发现调查区遗漏点,优化取样点及线路等,提高了工作效益和准确度。展开更多
A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography(CT) and magnetic resonance(MR) images of a liver.This hierarchical framework consisted of an affine transformation and a B-sp...A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography(CT) and magnetic resonance(MR) images of a liver.This hierarchical framework consisted of an affine transformation and a B-splines free-form deformation(FFD).The affine transformation performed a rough registration targeting the mismatch between the CT and MR images.The B-splines FFD transformation performed a finer registration by correcting local motion deformation.In the registration algorithm,the normalized mutual information(NMI) was used as similarity measure,and the limited memory Broyden-Fletcher- Goldfarb-Shannon(L-BFGS) optimization method was applied for optimization process.The algorithm was applied to the fully automated registration of liver CT and MR images in three subjects.The results demonstrate that the proposed method not only significantly improves the registration accuracy but also reduces the running time,which is effective and efficient for nonrigid registration.展开更多
文摘通常G IS技术应用到野外水文地质调查工作时需要花费大量时间准备数据,导致其不能及时参与到野外工作中。为了使G IS技术及时在1∶5万岩溶水文地质综合调查野外工作中发挥作用,笔者以湖南新田河调查为例,在M apG IS系统中根据原始资料复杂程度的不同对数据进行处理和配准,及时制作了信息丰富的野外工作用图,建立了工作区专业数据和数据录入、分析平台,在野外调查工作开展时及时录入数据并加以分析,及时修订地质边界,检查野外定点准确度,发现调查区遗漏点,优化取样点及线路等,提高了工作效益和准确度。
基金Project(61240010)supported by the National Natural Science Foundation of ChinaProject(20070007070)supported by Specialized Research Fund for the Doctoral Program of Higher Education of China
文摘A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography(CT) and magnetic resonance(MR) images of a liver.This hierarchical framework consisted of an affine transformation and a B-splines free-form deformation(FFD).The affine transformation performed a rough registration targeting the mismatch between the CT and MR images.The B-splines FFD transformation performed a finer registration by correcting local motion deformation.In the registration algorithm,the normalized mutual information(NMI) was used as similarity measure,and the limited memory Broyden-Fletcher- Goldfarb-Shannon(L-BFGS) optimization method was applied for optimization process.The algorithm was applied to the fully automated registration of liver CT and MR images in three subjects.The results demonstrate that the proposed method not only significantly improves the registration accuracy but also reduces the running time,which is effective and efficient for nonrigid registration.
