为提取人体肝脏CT图像中的肿瘤区域,提出一种基于动态自适应区域生长的算法进行肿瘤分割.通过自适应区域生长算法对CT图像进行预分割,得到感兴趣区域(region of interest,ROI),利用数学形态学滤波填充ROI中的空洞区域,最终提取肿瘤区域.通过对多组病人的CT图像进行实验,结果显示该算法对肝脏肿瘤的分割效果良好.
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.
