基于改进CV模型的金相图像分割

Segmentation of metallographic images based on improved CV model

对金相图像进行快速精确分割是金相晶粒评级的关键步骤,利用传统Chan-Vese(CV)模型很难将晶粒精确地提取出来.为了更加精确地对金相图像进行分割,提出一种基于改进CV模型的金相图像分割方法.初始化水平集函数,对曲线内外两部分分别计算其倒数坎贝拉距离,并将该距离的大小作为拟合中心的权重系数,有效抑制了噪声点对区域拟合中心准确性的影响;引入指数熵自适应调节曲线内外能量权重,减少固定能量权重对曲线演化的影响;同时加入距离规范项以避免水平集函数的重新初始化,加速该模型的收敛.实验结果表明,与传统CV模型、测地线活动轮廓模型、距离规范项的水平集模型以及偏置场修正水平集模型相比,所提方法分割出的金相图像更加精确,分割效率较高且模型收敛性较好.

The segmentation of metallographic images plays a key role in grain grading,but it is difficult to extract grains accurately using the traditional Chan-Vese（CV） model. To segment metallographic images more accurately,a metallographic image segmentation method based on an improved CV model was proposed. First,the level set function was initialized,and its reciprocal Canberra distance from inside and outside the curve was calculated. Then,these distances were used as weight coefficients of the fitting centers to restrain the influence of noise points on their accuracy. In addition,adding exponential entropy to adjust the energy inside and outside the curve reduces the influence of the fixed energy weight on the evolution of the curve. Lastly,to accelerate the convergence of the model,a distance-regularized term was introduced to avoid re-initialization of the level set function. The experimental results show that,compared with the traditional CV model,the geodesic active contour model,the distance-regularized level set evolution model,and the bias level correction level set model,the segmentation of the metallographic images based on the proposed model is more accurate and efficient,and the proposed model has better convergence.