结合非下采样Contourlet变换的改进图论MST图像分割

Improved Graph MST-Based Image Segmentation with Non-Subsampled Contourlet Transform

为提高图论最小生成树的分割精度,保留更多边缘细节,提出了一种结合非下采样Contourlet变换(NSCT)及改进图论最小生成树(MST)的图像分割方法.首先,将图像进行NSCT分解,得到一个低频子带和多个高频方向子带,对各高频方向子带采用改进的贝叶斯萎缩阈值抑制噪声,通过模极大值检测关联边缘的像素点,结合低频子带灰度值和高频子带系数构造多尺度多方向的MST边权,并加重关联边缘的边权重;然后,从区域内部和区域间差异函数以及合并机制方面对MST分割算法进行改进,降低噪声或孤立点的影响;最后,改进和声搜索算法的"调音"策略,自适应获取MST分割算法的最优参数,得到全局最优分割.实验结果表明:与其他改进图论MST分割方法相比,文中方法的抗噪声性能好,提高了分割精度,且错分率低,所得图像边缘细节明显,分割效果较好.

In order to improve the segmentation accuracy of graphs minimum spanning tree and reserve more edge details, a new image segmentation method, which is on the basis of non-subsampled Contourlet transform （NSCT） and improved graphs minimum spanning tree （MST） is proposed. Firstly, an image is decomposed into a low-fre-quency sub-band and several high-frequency direction sub-bands through NSCT decomposition. Secondly, the high- frequency direction sub-bands are denoised according to the improved Bayes shrink threshold, and edge points are detected according to the module maxima. Then, a multi-scale multi-direction MST edge weight is constructed ac-cording to the grey value of low-frequency sub-band and the coefficients of high-frequency sub-bands, and the edge weight of edge points is increased. Moreover, MST algorithm is improved in two main a sp e c ts , one is the function of intra-regional and inter-regional differences, and the other is the re-merge mechanism after segmentation. Th u s , the impact of noises or isolated points can be reduced. Finally, the optimal position adjustment strategy of harmony search is improved and adopted to find the optimal parameters of global optimal MST segmentation results adaptive-ly. Experimental results show th a t, in comparison with other improved MST algorithms, the proposed method im-proves both anti-noise performance and segmentation accuracy, and helps obtain images with higher segmentation accuracy and better edge details.