摘要
我们针对医学辅助诊断系统中从M R图像分割脑肿瘤的问题,改进了区域竞争算法,并利用它实现了医学图像序列间的连续自动分割,特别是脑肿瘤的分割和脑膜瘤的自动识别。模糊化区域竞争算法是为了更好的适应医学图像的模糊与不均匀的特点,而用区域增长做初始化可以给区域竞争提供用来竞争和合并的过分割区域。为了实现医学图像序列的自动连续分割,每一副切片的分割结果都会被用作初始化下一张切片;并且我们根据脑膜瘤的特点实现了它的自动识别。实验表明,我们的自动分割算法对仿真脑图像和真实脑图像均有较好的分割精度,并能满足系统对分割快速性的需要。
We propose an improved version of regional competition algorithm in this paper, and apply it to the automatic segmentation of medical image series, particularly in the segmentation and recognition of brain tumor. The traditional regional competition is enhanced by combining the attractive aspects of fuzzy segmentation, and thus it provides an efficient approach to segment the fuzzy and heterogeneous medical images. In order to perform regional competition on medical image series, we utilize the segmentation result of a slice to initiate the next segmented slice, while the first slice is initialized using regional growing algorithm. Moreover, we develop an algorithm to recognize the tumors automatically, taking into account its characters. Experimental results show that our algorithm performs well on the segmentation of magnetic resonance imaging （MRI） image series with high speed and precision.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2006年第4期699-703,共5页
Journal of Biomedical Engineering
基金
中法先进计划资助项目(PRA503-02)
关键词
医学图像分割
区域竞争
肿瘤分割
模糊
Medical image segmentation Region competition Tumor segmentation Fuzzy
