摘要
A key step of constructing active appearance model is requiring a set of appropriate training shapes with well-defined correspondences.In this paper,we introduce a novel point correspondence method(FB-CPD),which can improve the accuracy of coherent point drift(CPD) by using the information of image feature.The objective function of the proposed method is defined by both of geometric spatial information and image feature information,and the origin Gaussian mixture model in CPD is modified according to the image feature of points.FB-CPD is tested on the 3D prostate and liver point sets through the simulation experiments.The registration error can be reduced efficiently by FB-CPD.Moreover,the active appearance model constructed by FB-CPD can obtain fine segmentation in 3D CT prostate image.Compared with the original CPD,the overlap ratio of voxels was improved from 88.7% to 90.2% by FB-CPD.
A key step of constructing active appearance model is requiring a set of appropriate training shapes with well-defined correspondences. In this paper, we introduce a novel point correspondence method （FB-CPD）, which can improve the accuracy of coherent point drift （CPD） by using the information of image feature. The objective function of the proposed method is defined by both of geometric spatial information and image feature information, and the origin Gaussian mixture model in CPD is modified according to the image feature of points. FB-CPD is tested on the 3D prostate and liver point sets through the simulation experiments. The registration error can be reduced efficiently by FB-CPD. Moreover, the active appearance model constructed by FB-CPD can obtain fine segmentation in 3D CT prostate image. Compared with the original CPD, the overlap ratio of voxels was improved from 88.7% to 90.2% by FB-CPD.
基金
National Basic Research Program of China(973 Program)
grant number:2010CB732505
National Natural Science Foundation of China
grant number:30900380
