CAJ | 학술논문

目的探讨有效地利用图谱的先验信息和待分割图像的灰度与结构信息,得到光滑、准确的分割结果的脑部图像分割方法.方法利用配准的局部相似性测度、标号图像的距离场、待分割图像的自相似性计算多权重概率图谱,然后对多权重概率图谱进行阈值处理得到最终的分割结果.通过配准的相似性测度加权,保证概率图谱计算的准确性;利用标号图像的距离场加权,引入图谱标号图像提供的位置先验信息;经过待分割图像的自相似性加权,引入了待分割图像提供的灰度与结构信息.结果对大量脑部MR图像中的海马进行分割实验,并与国际上主流的分割算法进行了比较,对左海马的分割精度提高到87%,对右海马的分割精度提高到87.5%.结论基于多权重概率图谱的脑部图像分割能有效的提高分割精度.
목적 탐토유효지이용도보적선험신식화대분할도상적회도여결구신식,득도광활、준학적분할결과적뇌부도상분할방법.방법 이용배준적국부상사성측도、표호도상적거리장、대분할도상적자상사성계산다권중개솔도보,연후대다권중개솔도보진행역치처리득도최종적분할결과.통과배준적상사성측도가권,보증개솔도보계산적준학성;이용표호도상적거리장가권,인입도보표호도상제공적위치선험신식;경과대분할도상적자상사성가권,인입료대분할도상제공적회도여결구신식.결과 대대량뇌부MR도상중적해마진행분할실험,병여국제상주류적분할산법진행료비교,대좌해마적분할정도제고도87%,대우해마적분할정도제고도87.5%.결론 기우다권중개솔도보적뇌부도상분할능유효적제고분할정도.
We propose a multi-weighted probabilistic atlas to obtain accurate, robust, and reliable segmentation. The local similarity measure is used as the weight to compute the probabilistic atlas, and the distance field is used as the weight to incorporate the locality information of the atlas;the self-similarity is used as the weight to incorporate the local information of target image to refine the probabilistic atlas. Experimental results with brain MRI images showed that the proposed algorithm outperforms the common brain image segmentation methods and achieved a median Dice coefficient of 87.1% on the left hippocampus and 87.6%on the right.