基于改进遗传模糊聚类和水平集的图像分割算法

针对传统的模糊聚类算法(FCM)容易陷入局部最优,水平集方法 (Level set)容易受初始边界和控制参数的影响等问题,引入具有全局搜索能力的遗传算法(GA)初始化聚类中心,提出改进的模糊聚类算法分割得到目标的粗边缘,利用水平集方法强大的演化能力收敛到目标边缘。该算法可以减少水平集方法控制参数的个数,降低计算的复杂度,提高分割速度。实验在多目标轮廓图像、轮廓不清晰图像上进行,实验结果表明,该方法能够很好地检测出多目标及弱边缘图像的轮廓,在乳腺X线图像中,肿块的分割精度、过分割率和欠分割率分别为98.35%,0.27%和1.12%,优于同类算法。

Image segmentation algorithm based on improved genetic fuzzy clustering and level set

The traditional fuzzy clustering algorithm (FCM) is easy to fall into the local optimum,the level set method is easy to be affected by the initial boundary and control parameters and so on.To solve the problems,the genetic algorithm (GA) with global search ability was introduced to initialize the cluster center,and an improved fuzzy clustering algorithm was proposed to get the rough edge of the target.The strong evolutionary ability of the level set method was used to get the target edge.The algorithm can reduce the number of control parameters of the level set method,reduce the computational complexity,and improve the segmentation speed,effectively.Experiments were performed on multi-target contour images and unconspicuous contour ima- ges.The results show that the proposed method can detect the contour of multi target and weak edge image.The segmentation accuracy,over segmentation rate and under segmentation rate of lump in the mammography are 98.35%,0.27%,and 1.12% respectively,which are better than those of the similar algorithms.