基于先验知识和几何主动轮廓线的三维超声瓣膜分割

The Prior-based Geodesic Active Contour for Mitral Valve Segmentation in Echocardiographic Sequences

几何主动轮廓线模型是一种有效的图像分割方法。但对于被噪声严重干扰的目标依然很难准确分割。特定目标的先验知识可以有效地指导目标的准确分割。我们把特定目标的区域和形状先验知识表示成一种速度场,把此速度场嵌入到几何主动轮廓线模型中,指导目标的快速准确分割。先验区域限制水平集在特定区域迭代,先验形状使曲线向理想轮廓演化。我们把该算法应用于三维超声图像的二尖瓣自动分割,结果表明该分割算法是快速和高效的。

Geodesic active contour is a useful image segmentation method, but it may fail to segment the objects disturbed by complex noises. Prior knowledge on certain object is a powerful guidance in image segmentation. In this respect, we represent the prior knowledge of region and shape of certain object in a form of speed field and incorporate it into Geodesic Active Contours. The prior region constrains the zero level set evolving in certain region and the prior shape pulls the curve to the ideal contour. Applications in a large quantity of cardiac valve echocardiographic sequences have shown that the algorithm is a more accurate and efficient image segmentation method.