自适应区域拟合的非均匀图像分割

Non-Uniform Image Segmentation Based on Adaptive Region Fitting

针对传统水平集方法在分割灰度不均匀图像的过程中存在分割精度低的问题,提出一种自适应区域拟合的非均匀图像分割算法。首先构建自适应的区域拟合能量项来保留更多待分割图像局部区域内的细节信息,实现图像的准确分割;其次引入非凸正则项来平滑曲线并保护图像的边缘;然后添加能量惩罚项对水平集函数进行约束,提高算法的分割效率;最后对合成图像和真实图像进行实验验证。实验结果表明,所提算法的Dice相似系数平均值为88.62%,Jaccard相似系数平均值为79.86%,准确率平均值为92.48%,比Local Binary Fitting(LBF)、Local and Global Intensity Fitting(LGIF)、Local Pre-fitting(LPF)三种模型的总体平均值分别高18.19个百分点、16.10个百分点、13个百分点。

In this paper,we proposed an adaptive region fitting nonuniform image segmentation algorithm to solve the low segmentation accuracy of the traditional level set method in segmenting uneven grayscale images.First,we constructed an adaptive region fitting energy term to retain more detailed information in the local region of the image to be segmented,and to achieve accurate image segmentation.Second,we applied a nonconvex regular term to smooth the curve and protect the edges of the image.Furthermore,we added an energy penalty term to constrain the level set function and improve the algorithm’s segmentation efficiency.Finally,the synthetic and real images were verified by experiments.Experimental results show that the average Dice similarity coefficient,Jaccard similarity coefficient,and accuracy of the proposed algorithm are 88.62%,79.86%,and 92.48%,respectively,which are 18.19 percentage points,16.10 percentage points,and 13 percentage points higher than those of Local Binary Fitting(LBF),Local and Global Intensity Fitting(LGIF),and Local Prefitting(LPF)respectively.