基于加速健壮特征拟合算法和Chan-Vese模型的超声图像腔室分割方法

Echocardiography chamber segmentation based on integration of speeded up robust feature fitting and Chan-Vese model

针对超声心动周期序列图的腔室自动分割过程中,弱边缘轮廓难以有效提取的问题,提出一种基于加速健壮特征(SURF)拟合算法和Chan-Vese模型的超声图像腔室分割方法。首先对序列中第一帧图像进行人工标记弱边缘轮廓;然后,提取弱边缘轮廓周围的SURF点,建立Delaunay三角网;接着,通过相邻两帧之间的特征点匹配,预测后续帧的弱边缘轮廓;之后,用Chan-Vese模型提取粗糙轮廓;最后采用区域生长算法得到精确的目标轮廓。实验结果表明,该算法能较好地完整提取超声序列图像中含弱边缘的腔室轮廓,并且与专家手动分割结果相近。

During the automatic segmentation of cardiac structures in echocardiographic sequences within a cardiac cycle,the contour with weak edges can not be extracted effectively. A new approach combining Speeded Up Robust Feature（ SURF）and Chan-Vese model was proposed to resolve this problem. Firstly, the weak boundary of heart chamber in the first frame was marked manually. Then, the SURF points around the boundary were extracted to build Delaunay triangulation. The positions of weak boundaries of subsequent frames were predicted using feature points matching between adjacent frames. The coarse contour was extracted using Chan-Vese model, and the fine contour of object could be acquired by region growing algorithm.The experiment proves that the proposed algorithm can effectively extract the contour of heart chamber with weak edges, and the result is similar to that by manual segmentation.