基于曲线聚类的动态PET影像感兴趣区域提取新方法

New method for extracting region of interest of dynamic PET images based on curve clustering

针对目前动态正电子发射断层扫描(PET)影像的感兴趣区域(ROI)提取的聚类方法忽略了时间放射性曲线(TAC)的时间序列特征,提出一种基于曲线聚类的ROI提取方法。首先用K-均值(K-Means)聚类去除背景得到心脏的位置,然后对心脏进行曲线聚类提取出心肌,最后根据像素点的空间位置关系提取血池。将该方法应用于14只小鼠的PET影像ROI勾画,实验结果表明,与K-Means和混合型的聚类方法 HCM相比,该方法能够更准确地提取出14只小鼠的血池,且具有更高的精确度和稳定性。

Concerning the problem that many current clustering methods based on kinetic characteristics ignore the continuous temporal information of Time Activity Curve （TAC）, a method for Region Of Interest （ROI） extraction based on curve clustering was proposed in the paper. The proposed method contains three steps. Firstly, K-Means algorithm was used to remove the background to obtain a coarse mask of the heart. Secondly, curve clustering was used to extract myocardium from the heart obtained in the first step. Finally, blood cavity was delineated based on spatial relationship between pixels. The method was applied to extract the ROI from fourteen mouse PET images. The experimental results indicate that the proposed method is more accurate in delineating blood cavity of the fourteen mice than K-Means and Hybrid Clustering Method （HCM）, and it is more precise and stable.