距离保持水平集演化模型的快速实现算法

Fast algorithm for distance regularized level set evolution model

针对梯度下降法收敛性较差、对局部极小值比较敏感的问题,提出一种改进NAG算法,并以此替换距离保持水平集演化(DRLSE)模型中的梯度下降算法,进而得到一个基于NAG的图像快速分割算法。首先,给出初始水平集演化方程;其次,用改进NAG算法计算梯度;最后,对水平集函数进行不断更新,从而避免水平集函数陷入局部极小值。实验结果表明,与DRLSE模型中的原算法相比,所提算法迭代次数减少了约30%,CPU运行时间减少了30%以上。该算法实现简单,能够对实时性要求较高的红外图像、医学图像进行快速、有效的分割。

The gradient descent method has poor convergence and is sensitive to local minimum.Therefore,an improved NAG(Nesterov’s Accelerated Gradient)algorithm was proposed to replace the gradient descent algorithm in the Distance Regularized Level Set Evolution(DRLSE)model,so as to obtain a fast image segmentation algorithm based on NAG algorithm.First,the initial level set evolution equation was given.Second,the gradient was calculated by using the NAG algorithm.Finally,the level set function was updated continuously,avoiding the level set function falling into local minimum.Experimental results show that compared with the original algorithm in the DRLSE model,the proposed algorithm has the number of iterations reduced by about 30%,and the CPU running time reduced by more than 30%.The algorithm is simple to implement,and can be applied to segment the images with high real-time requirement such as infrared images and medical images.