一种基于广义梯度矢量流Snake模型的心脏MR图像分割方法

提出了一种基于广义梯度矢量流Snake模型的心脏核磁共振图像左心室内、外膜分割方法。首先构造了一种基于目标边缘的方向广义梯度矢量流(edge-based directional generalized gradient vector flow, EDGGVF) Snake模型，该模型在传统GGVF的基础上，结合目标边缘图梯度方向信息，将左心室内、外膜区分为正边缘和负边缘，从而实现左心室内外膜的全自动分割。其次，根据左心室近似为圆形的形状特点，引入了圆形能量约束，有利于克服由于图像灰度不均、乳突肌等引起的局部极小。实验结果表明，该方法可以高效准确地自动分割出左心室内、外膜。

A Method for Segmentation of the Cardiac MR Images Based on GGVF Snake

In this paper, a novel method based on generalized gradient vector flow (GGVF) Snake model is proposed for segmentation of the left ventricle cardiac magnetic resonance (MR) images. Firstly, an edge-based directional generalized gradient vector flow (EDGGVF) Snake model is proposed as an improvement to GGVF, which differentiates cardiac endocardium and epicardium into positive and negative boundaries by incorporating the gradient orientation information of the images edge map. In addition, a circle-shape based energy for the Snake model is adopted considering the shape of the left ventricle. With this energy, the Snake contour can overcome the unexpected local minimum stemming from image inhomogeneity and papillary muscle. Experimental results show the method is able to segment LV endocardium and epicardium accurately and effectively.