基于多属性的空间连续模糊聚类算法的血管分割

血管系统的3维显示对于图像导航神经外科和手术计划非常重要。提出了一种基于多属性的空间连续模糊聚类算法的血管分割算法来提取时飞磁共振血管造影（TOF MRA)图像中的血管，该聚类算法同时利用了图像的灰度信息和几何信息来提取血管，而目前已有算法仅采用灰度信息。在该算法中又提出了一个融合了灰度和几何形状的不相似性度量准则, 由于几何形状的采用，使得该算法可以区分具有相似灰度但位于不同几何形状组织里的像素。为了验证该算法，分别对2维和3维图像进行了分割，实验结果表明，该算法能够获得更好的分割结果。

Multi-attribute Based Spatial Continuity Fuzzy Clustering Algorithm for Blood Vessels Segmentation

A three-dimensional representation of vasculature system can be extremely important in image-guided neurosurgery, pre-surgical planning. In this paper, a multi-attribute based spatial continuity fuzzy clustering algorithm (multi-attribute based spatial continuity fuzzy clustering algorithm, MASCFCM) is proposed for segmenting entire blood vessels from the time of flight magnetic resonance angiography (TOF MRA) images. This clustering method takes both the intensity information and the geometrical information into account, while most of the current clustering methods only deal with the former. In this method, a new dissimilarity method, which integrates the intensity and the geometry shape dissimilarity, is introduced. Because of the presence of the geometrical information, the new measure is able to differentiate the pixels with similar intensity values within different geometrical shape structures. To evaluate the algorism, the algorithm is exerted on both 2D and 3D images and the experimental results show that the new algorithm can achieve better segmentation results.