Abstract: In order to improve the segmentation accuracy of graph's 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 graph's 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 aspects,one is the function of intra-regional and inter-regional differences,and the other is the re-merge mechanism after segmentation.Thus,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 that,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.

Key words: image segmentation, non-subsampled Contourlet transform, minimum spanning tree, Bayes shrink threshold, harmony search algorithm, segmentation accuracy, anti-noise