一种基于边缘与区域信息的先验水平集图像分割方法

传统的水平集图像分割方法仅考虑了图像的数据信息,因此对被遮盖的目标以及与背景灰度相近的目标无法达到理想的分割效果.针对这个问题,提出了一种基于边缘和区域信息的先验水平集图像分割方法.该方法首先将图像的区域信息融入基于边缘的水平集方法,然后将其与形状先验结合.对比实验表明该文方法由于综合考虑了多种信息,能够更好地完成被遮盖目标的分割,对于与背景灰度相近的目标也能达到更好的效果.

An Edge- and Region-Based Level Set Method with Shape Priors for Image Segmentation

Traditional level set methods could not deal with the segmentation of overlapped objects and the ones having the similar gray value with background,since these methods just take image data into consideration.Aiming for this problem,we propose a new edge-and region-based level set method for image segmentation.Our method firstly fuses the regional information into the edge-based level set method,and then incorporates shape priors to design a new level set method with shape priors.The comparison results show that the proposed method can segment the overlapped objects better and also achieve better performance on the objects with the similar gray value as background since it integrates multiple kinds of information together.