神经目标自动分割是神经树脊检测、识别和重建的核心技术之一。提出了一种新颖的三维荧光共焦图像神经目标的水平集分割方法。第一步，采取各向异性曲率流平滑和增强图像, 然后生成水平集速度图像。第二步，通过计算局部等值面曲率的极大值自动生成神经目标的种子点，该种子点对应于局部的脊点或者谷点。第三步，快速行进算法计算初始水平集形状图像。最后，利用初始水平集速度图像作为输入，利用基于形状的水平集算法自动分割神经目标。该方法通过采取对应于局部的脊点或者谷点种子点生成定为目标位置，加速了水平集演化算法的计算时间。双光子或荧光激光图像中神经目标分割实验证明了算法的有效性。

Automatic segmentation is a core technology for dendritic spines detection, identification and reconstruction. A novel level set segmentation method is proposed for neuron object in 3D fluorescence confocal images. In the fist step, 3D image are smoothed and enhanced by curvature anisotropic diffusion filter, and then the level set speed images are computed. In the second step, the seed points of neuron object are computed automatically located at the extreme local iso-surface curvature, which correspond to the local ridge or valley points on neuron object. Then in the third step, the fast marching method is used to produce the initial level set shape images. In the last step, the initial level set shape images are passed as input to the shape detection based level set algorithm to compute the final 3D neuron object. This method reduces the computation time by minimizing level set propagation, which converges at the optimal object within a fixed iteration number. Experiments on 2-photon lasers or 3D fluorescence confocal images demonstrate that this method is effective and efficient.