自适应形状约束Graph cuts算法在腹部CT图像分割中的应用

Adaptive shape constraint based graph cuts and its application on CT images segmentation

针对CT图像对比度低,组织边缘模糊,器官轮廓不规则等特点造成组织器官难以分割的问题,提出了一种自适应形状约束的Graph cuts算法.首先使用基于多图谱配准的分割方法分割原始图像,将得到的初始分割结果作为形状先验加入到Graph cuts算法的能量函数中,同时根据配准分割过程得到的目标概率图自适应选择形状约束项系数,最后通过最大流最小割算法分割出CT图像中的肝脏、肾脏和脾脏.实验结果表明,该方法能够较好地分割出肝脏等组织器官,有效减轻传统图割算法分割图像时造成的过分割和欠分割现象.

Because of the low contrast, the blurred edges and the irregular contours of the organs in CT images, it is hard to extraction the target organ from CT images. To address this problem, a graph cuts algorithm which combines shape constraint adaptively is proposed. Firstly, the original image is segmented using the segmentation method based on multi-atlas registration, and the initial segmentation is obtained. Then the initial segmentation is added to the graph cuts energy function as a shape constraint. At the same time, the shape constraint coefficients are adaptively selected according to the target probability map which obtained in the process of initial segmentation. Finally the targets such as liver, kidney and spleen in the CT image are extracted by the max-flow min-cut algorithm which minimize the energy function. The experimental results show that this method can segment the targets well, and it can effectively reduce the over-segmentation and under-segmentation caused by the traditional graph cut algorithms.