基于引导滤波的多图谱医学图像分割

目的为了有效的利用图谱的先验信息和待分割图像的灰度信息，并在融合标号图像的过程中校正配准引起的误差，得到
光滑、准确的分割结果，提出了一种新的基于引导滤波的多图谱医学图像分割方法。方法本文将多图谱配准与引导滤波相结
合。该方法包含4个部分：第一部分为多图谱配准，通过配准将图谱中存储的形状先验信息映射到待分割图像；第二部为标号融
合，利用配准的相似性作为权重，将形变后的标号图像融合在一起；第三部分为引导滤波，利用引导滤波引入待分割图像的灰度
信息，可以校正配准引起的误差；最后通过阈值处理，得到最终的分割结果。结果对15例脑部MR图像数据中的海马体进行分
割实验，左、右海马体分别达到了86%及87.4%的分割精度，与传统的标号融合算法相比，平均分割精度提升了2.4%。结论本
文方法结合多配谱配准与引导滤波的优势，提高了海马的分割精度，并得到光滑有效的分割精度。

Medical image segmentation based on guided filtering and multi-atlas

A novel medical automatic image segmentation strategy based on guided filtering and multi-atlas is proposed to
achieve accurate, smooth, robust, and reliable segmentation. This framework consists of 4 elements: the multi-atlas
registration, which uses the atlas prior information; the label fusion, in which the similarity measure of the registration is used
as the weight to fuse the warped label; the guided filtering, which uses the local information of the target image to correct the
registration errors; and the threshold approaches used to obtain the segment result. The experimental results showed part
among the 15 brain MRI images used to segment the hippocampus region, the proposed method achieved a median Dice
coefficient of 86% on the left hippocampus and 87.4% on the right hippocampus. Compared with the traditional label fusion
algorithm, the proposed algorithm outperforms the common brain image segmentation methods with a good efficiency and
accuracy.