Entropic graph estimation integrated with SIFT features for medical image non-rigid registration
融合SIFT特征的熵图估计医学图像非刚性配准

Accuracy is important for the regrstration of medical images.Pixel gray values are a widely used feature in image registration.However,the gray values come from a single source and ignore the spatial information.In some cases,it will cause misalignment.To solve the problem,entropic graph estimation integrated with SIFT features is proposed as a medical image non-rigid registration algorithm.In the algorithm,mutual information based rigid registration is used to roughly register two images.Then the pixel gray value and the SIFT features are extracted to form a k-nearest neighbor graph(kNNG),which is used to estimate α-mutual information(αMI).Comparison results of the images obtained from lung CT images and brain MRI images showed that the proposed algorithm provides better accuracy than both,the conventional rigid registration algorithm based on mutual information and the non-rigid registration algorithm based on entropic graph estimation and single pixel gray values.