CAJ | 학술논문

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.
해문대전통적변분광류모형진행료개진,결합척도불변특정변환(SIFT)특정점제취제출일충신영적비강성의학도상배준산법.해산법모형사용량도수항여제도수항가설상결합적수거항,흔호지해결료대의학도상중국부병조이상、량도불균균등구역적처리문제;통과채용자괄응적각향이성정칙항,해결료전통광류모형중적과평활소도치적도상엄중모호화중요세절주실적문제;통과결합SIFT특정점제취,병채용다분변솔분층세화、내부불동점질대이급유조도세적변형기술구해책략,흔호지해결료전통광류장모형무법대대형변의학도상이급세절진행배준적문제.실험증명:해문적모형화산법가이흔호지실현대의학도상적비강성배준.
@@@@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.