A modified fuzzy C-means method for segmenting MR images using non-local information
A modified fuzzy C-means method for segmenting MR images using non-local information
复制标题
一种使用非局部信息分割 MR 图像的改进模糊 C 均值方法
DOI：
10.3233/thc-161208
复制
发表时间：
2016-01-01
影响因子：
1.6
通讯作者：
Hu, Yanle
中科院分区：
文献类型：
--
作者：
Feng, Yuan;Guo, Hao;Hu, Yanle
背景：近年来，MR 图像越来越多地应用于图像引导放射治疗（IGRT）等治疗应用。然而，低对比度值和噪声的图像给图像分割带来了挑战。 目的：本研究的目的是开发一种基于模糊C均值（FCM）方法的鲁棒方法，该方法可以分割受高斯噪声污染的MR图像。方法：开发了一种通过Hausdorff距离容纳非局部像素信息的改进FCM算法来分割MR图像。隶属度和目标函数进行了相应修改。比较了不同权重Hausdorff距离的分割结果。结果：使用合成图像和MR图像的分割测试表明，该算法能够更好地解决边界问题，并且对高斯噪声具有更强的鲁棒性。通过分割肿瘤的样本 MR 图像，我们进一步展示了该方法捕获目标区域质心的能力。结论：带有邻近信息的改进 FCM 算法可用于分割模糊图像，在图像引导放射治疗（IGRT）中分割运动 MR 图像方面具有潜在应用。
BACKGROUND: In recent years, MR images have been increasingly used in therapeutic applications such as image-guided radiotherapy (IGRT). However, images with low contrast values and noises present challenges for image segmentation.OBJECTIVE: The objective of this study is to develop a robust method based on fuzzy C-means (FCM) method which can segment MR images polluted with Gaussian noise.METHODS: A modified FCM algorithm accommodating non-local pixel information via Hausdorff distance was developed for segmenting MR images. The membership and objective functions were modified accordingly. Segmentations with different weights of the Hausdorff distance were compared.RESULTS: Segmentation tests using synthetic and MR images showed that the proposed algorithm was better at resolving boundaries and more robust to Gaussian noise. By segmenting a sample MR image of a tumor, we further showed the capability of the method in capturing the centroid of the target region.CONCLUSIONS: The modified FCM algorithm with neighboring information can be used to segment blurry images with potential applications in segmenting motion MR images in image-guided radiotherapy (IGRT).