CAJ | 학술논문

为了提高多模态医学图像的配准精度，利用参考图像和浮动图像的结构信息，提出了一种基于结构图像表示和微分同胚 Demons 算法的多模态医学图像配准算法。该算法由结构图像表示和图像配准组成。在结构图像表示阶段，采用 Arimoto 熵图像来描述参考图像和浮动图像的结构信息，首先计算2幅图像中所有像素点周围指定大小邻域的熵值，然后把计算的熵值作为熵图像中对应点的灰度值以生成2幅熵图像。在图像配准阶段，使用微分同胚 Demons 配准算法对2幅熵图像进行配准。基于 Brainweb 数据库中磁共振数据的测试结果表明：与微分同胚 Demons 算法和基于香农熵的 Demons 算法相比，利用 Arimoto 熵表示图像的结构信息可以进一步提高配准精度。
위료제고다모태의학도상적배준정도，이용삼고도상화부동도상적결구신식，제출료일충기우결구도상표시화미분동배 Demons 산법적다모태의학도상배준산법。해산법유결구도상표시화도상배준조성。재결구도상표시계단，채용 Arimoto 적도상래묘술삼고도상화부동도상적결구신식，수선계산2폭도상중소유상소점주위지정대소린역적적치，연후파계산적적치작위적도상중대응점적회도치이생성2폭적도상。재도상배준계단，사용미분동배 Demons 배준산법대2폭적도상진행배준。기우 Brainweb 수거고중자공진수거적측시결과표명：여미분동배 Demons 산법화기우향농적적 Demons 산법상비，이용 Arimoto 적표시도상적결구신식가이진일보제고배준정도。
In order to improve the registration accuracy of multi-modal medical images,a multi-mo-dal medical image registration algorithm based on structural image representation and diffeomorphic Demons algorithm is proposed by using the structural information of the reference image and the float image.This algorithm contains structural image representation and image registration.In the process of structural image representation,the Arimoto entropy is used to describe the structural information of the reference image and the float image.First,the entropic values of the neighbourhoods with the designed size of all pixel points in the two images are calculated.Then,these entropic values are re-garded as the intensity values of the corresponding points in the entropy image and two entropy ima-ges can be obtained.In the process of image registration,the diffeomorphic Demons registration ap-proach is used to register these two entropy images.The experimental results of the MRI (magnetic resonance imaging)data in Brainweb database show that applying Arimoto entropy to represent the structural information can further improve the registration accuracy compared with the diffeomorphic Demons algorithm and the Demons method based on Shannon entropy.