Roy Snehashis, Agarwal Harsh, Carass Aaron, Bai Ying, Pham Dzung L, Prince Jerry L
Image Analysis and Communications Laboratory, Electrical and Computer Engineering, The Johns Hopkins University.
Proc IEEE Int Symp Biomed Imaging. 2008;4541030:452. doi: 10.1109/ISBI.2008.4541030.
Fuzzy c-means (FCM) clustering has been extensively studied and widely applied in the tissue classification of biomedical images. Previous enhancements to FCM have accounted for intensity shading, membership smoothness, and variable cluster sizes. In this paper, we introduce a new parameter called "compactness" which captures additional information of the underlying clusters. We then propose a new classification algorithm, FCM with variable compactness (FCMVC), to classify three major tissues in brain MRIs by incorporating the compactness terms into a previously reported improvement to FCM. Experiments on both simulated phantoms and real magnetic resonance brain images show that the new method improves the repeatability of the tissue classification for the same subject with different acquisition protocols.
模糊 c 均值（FCM）聚类已得到广泛研究，并在生物医学图像的组织分类中得到广泛应用。先前对 FCM 的改进考虑了强度阴影、隶属度平滑度和可变聚类大小。在本文中，我们引入了一个名为“紧致性”的新参数，该参数捕获了底层聚类的额外信息。然后，我们提出了一种新的分类算法——可变紧致性模糊 c 均值（FCMVC），通过将紧致性项纳入先前报道的 FCM 改进方法中，对脑部磁共振成像中的三种主要组织进行分类。在模拟体模和真实磁共振脑图像上的实验表明，新方法提高了同一受试者在不同采集协议下组织分类的可重复性。
