基于多权重概率图谱的脑部图像分割
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。
Objective 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.
冯前进、卢振泰、张雷、张明慧、陈武凡
计算技术、计算机技术自动化技术、自动化技术设备
图像分割概率图谱相似性测度距离场自相似性海马
冯前进,卢振泰,张雷,张明慧,陈武凡.基于多权重概率图谱的脑部图像分割[EB/OL].(2017-12-07)[2025-08-24].https://chinaxiv.org/abs/201712.00821.点此复制
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