Lynch M, Ghita O, Whelan P F
Vision Systems Group, School of Electronic Engineering, Dublin City University, Dublin 9, Ireland.
Comput Biol Med. 2006 Apr;36(4):389-407. doi: 10.1016/j.compbiomed.2005.01.005. Epub 2005 May 31.
A novel approach for the automatic segmentation has been developed to extract the epi-cardium and endo-cardium boundaries of the left ventricle (lv) of the heart. The developed segmentation scheme takes multi-slice and multi-phase magnetic resonance (MR) images of the heart, transversing the short-axis length from the base to the apex. Each image is taken at one instance in the heart's phase. The images are segmented using a diffusion-based filter followed by an unsupervised clustering technique and the resulting labels are checked to locate the (lv) cavity. From cardiac anatomy, the closest pool of blood to the lv cavity is the right ventricle cavity. The wall between these two blood-pools (interventricular septum) is measured to give an approximate thickness for the myocardium. This value is used when a radial search is performed on a gradient image to find appropriate robust segments of the epi-cardium boundary. The robust edge segments are then joined using a normal spline curve. Experimental results are presented with very encouraging qualitative and quantitative results and a comparison is made against the state-of-the art level-sets method.
一种用于自动分割的新方法已被开发出来，用于提取心脏左心室（LV）的心外膜和心内膜边界。所开发的分割方案采用心脏的多层多相位磁共振（MR）图像，从心底到心尖横越短轴长度。每张图像在心脏相位的一个时刻采集。使用基于扩散的滤波器对图像进行分割，随后采用无监督聚类技术，并检查所得标签以定位LV腔。从心脏解剖学来看，与LV腔最接近的血池是右心室腔。测量这两个血池之间的壁（室间隔）以给出心肌的近似厚度。当在梯度图像上进行径向搜索以找到心外膜边界的合适稳健段时，使用该值。然后使用普通样条曲线连接稳健的边缘段。给出了实验结果，其定性和定量结果都非常令人鼓舞，并与当前最先进的水平集方法进行了比较。
