基于PSO和Level Set的快速曲线演化算法

A New Fast Algorithm of Curve Evolution Based on PSO and Level Set

提出了一种基于微粒群算法(PSO)、水平集(Level Set)的快速曲线演化算法.该算法将图像分割分为两阶段,利用PSO方法对Snake模型中的控制点进行寻优,使控制点能快速地收敛到图像的边缘附近;利用插值算法,得到目标较粗糙的大致轮廓,以此目标轮廓作为初始的零水平集曲线;使用Level Set窄带方法得到准确的轮廓.从而克服了参数主动轮廓线模型对初始曲线敏感、噪声敏感和不能收敛到凹陷的边缘问题和几何主动轮廓线模型水平集方法计算量大等问题.实例研究结果表明了该算法的正确性、有效性.

This paper presents a new method for curve evolution based on PSO and Level Set. PSO is first proposed to find the optima of snake points for rapidly converging near image edge. Then the interpolation algorithm is applied to gaining the object＇s rough contour that is used as the initial zero level set. The accurate contour can be obtained by the approach of narrow band level set. The new method overcomes the disadvantages of the sensitivity of active contour models to its initial position, the poor convergence to boundary concavities which existed in the traditional Snake algorithm, sensitivity to noise and complex computation. Experimental results show that the new algorithm is efficient and the evolution result is precise.