CAJ | 학술논문

由于互信息不需要对图像进行预处理,因此被广泛地应用于医学图像配准中.但是,配准过程中的局部极大值难以克服.本文引进了梯度信息,用于解决局部极值问题.并将这种方法应用到人体的非刚性形变的医学图像配准中.同时,给出了一些用于改进精度的方法,如:Powell搜索算法、灰度插值和出界点问题,提高了匹配精度.采用此方法对脑部和肺部的多模图像进行配准,实验结果表明该方法对非刚体医学图像的配准有很大的可行性.
유우호신식불수요대도상진행예처리,인차피엄범지응용우의학도상배준중.단시,배준과정중적국부겁대치난이극복.본문인진료제도신식,용우해결국부겁치문제.병장저충방법응용도인체적비강성형변적의학도상배준중.동시,급출료일사용우개진정도적방법,여:Powell수색산법、회도삽치화출계점문제,제고료필배정도.채용차방법대뇌부화폐부적다모도상진행배준,실험결과표명해방법대비강체의학도상적배준유흔대적가행성.
Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient info rmation to solve this problem and apply it to the non-rigid deformation image r egistration. To improve the accuracy, we provide some implemental issues, for ex ample, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.