%0 Journal Article
%T A Bias Based Adaptive Fuzzy Segmentation Algorithm<br>基于有偏场的适配模糊聚类分割算法
%A LUO Shu qian
%A TANG Yu
%A <br>罗述谦
%A 唐宇
%J 中国图象图形学报
%D 2002
%I 
%X A number of supervised and unsupervised pattern recognition techniques have been proposed in recent years for the tissue segmentation and quantitative analysis of magnetic resonance images. However, the accuracy of these methods is affected seriously by the intensity inhomogeneities of images. In this paper, We present a novel algorithm(BAFCM) for fuzzy segmentations of images that are subject to intensity inhomogeneities, such as magnetic resonance image. The algorithm is formulated by modifying the objective function in the fuzzy c means algorithm to include a gain field, which models image intensity inhomogeneities. First and second order regulation terms in AFCM algorithm ensure that the gain field is both slowly varying and smooth, but increase complexity of computation greatly. Instead of computing gain field, we compute bias field first, then convert bias field to gain field. With BAFCM, we can correct the intensity inhomogeneities and implement fast classification of human brain tissue of MR image automatically.
%K Magnetic resonance imaging
%K Tissue classification
%K Bias field
%K Fuzzy C
%K means image segmentation<br>核磁共振图象
%K 组织分类
%K 有偏场
%K 适配模糊聚类分割
%K 图象分割
%K 图象处理
%K 医学影像学
%K FCM算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=0175D3CF81599663&yid=C3ACC247184A22C1&vid=DF92D298D3FF1E6E&iid=0B39A22176CE99FB&sid=480C51B1F0CE0AB6&eid=DDD31293A7C7D057&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=8&reference_num=3