基于仿射传播聚类选择的多Atlas右心室精准分割

由于右心室具有易变、壁薄、边界不明显等特点，Atlas成为近年来流行的图像分割方法.针对现存的Atlas选择方法大多基于配准后选择最优的Atlas，比较耗时并且降低了分割性能，提出了利用仿射传播聚类算法进行Atlas选择的方法.首先，将所有的Atlas图像看作一系列数据点，通过数据点之间的消息传递聚类；然后，得到的聚类中心图像和目标图像经过配准得到形变标记结果，采用STAPLE融合策略融合；最后，对融合结果以相似性测度因子为依据进行排序，而用相似性测度因子值最大的聚类中心进行配准分割.重复以上过程，直到得到较精确的分割结果.实验结果表明，提出的方法能有效地进行右心室的分割，与传统选择方法相比，分割精度得到了明显提高.

Accurate Segmentation of Right Ventricles Based on Multi atlas with Affinity Propagation Clustering Selection

Multi altas segmentation methods are currently popular methods to tackle the unique characteristics of right ventricles， e.g. volatile， thin and unobvious boundary. Most existing methods select the best atlas after the whole registration， which makes the process time consuming and poor performing. A new method was proposed based on affinity propagation clustering algorithm. First， all atlas images were treated as a series of data points and were clustered through message propagation. Further， all the exemplars were registered to the target image， and the label image was deformed by the parameters obtained from the registration process. What’s more， the label image was fused by STAPLE label fusion method. At last， the exemplars were sorted according to dice coefficient similarity and were used with largest similarity in the registration and segmentation. The process was repeated until reaching an accurate segmentation. Experimental results showed that the proposed method could segment right ventricle effectively. Comparing to the traditional selection methods， the segmentation accuracy has been considerably improved.