基于改进光流场和尺度不变特征变换的非刚性医学图像配准

摘要

该文对传统的变分光流模型进行了改进，结合尺度不变特征变换(SIFT)特征点提取提出一种新颖的非刚性医学图像配准算法。该算法模型使用亮度守恒与梯度守恒假设相结合的数据项，很好地解决了对医学图像中局部病灶异常、亮度不均匀等区域的处理问题；通过采用自适应的各向异性正则项，解决了传统光流模型中的过平滑所导致的图像严重模糊和重要细节丢失的问题；通过结合SIFT特征点提取，并采用多分辨率分层细化、内部不动点迭代以及由粗到细的变形技术求解策略，很好地解决了传统光流场模型无法对大形变医学图像以及细节进行配准的问题。实验证明：该文的模型和算法可以很好地实现对医学图像的非刚性配准。%A novel non-rigid image registration algorithm is proposed based on an improved version of the traditional variational optical flow model and the extraction of the Scale-Invariant Feature Transform (SIFT) feature points. In this model, the issue of processing the regions of localized disease abnormalities and un-uniform brightness is tackled by using a data term combining the brightness conservation and gradient conservation assumptions. To solve the issue of severe image blurring and the loss of important details caused by the over-smoothing of the traditional optical flow model, an adaptive anisotropic regularization term is used. By extracting the SIFT feature points and using a multi-resolution layered refining, internal fixed-point iteration and coarse-to-fine warping strategy, the issue of registration of medical images with relatively larger deformation and also that of the details registration of medical images which can not be processed by the traditional optical flow method are well resolved. Extensive experimental results show the effectiveness of the model for non-rigid medical image registration.