基于局部和非局部空间信息的图像分割算法
|
摘要
传统的SCM算法没有充分利用图像的空间信息,存在明显不足。结合空间信息的FCM算法已有效的用于处理含噪图像。文章介绍了一种新的图像分割算法,通过在隶属度函数中引入局部和非局部空间信息,提出了结合局部空间信息的SCM算法,基于非局部空间信息的SCM算法,以及结合局部和非局部空间信息的SCM算法。相比传统的FCM算法和SCM算法,该算法能有效地解决重叠和噪声问题。经过观察比较,该算法的分割结果更加准确,抗噪声能力更明显。
SCM does not take full use of spatial information. This paper introduces some new image segmentation methods in the framework of shadowed c-means clustering. By implanting the local and non-local spatial information in the membership value estimation procedure,it proposes the local spatial shadowed C-means algorithm,non-local spatial shadowed C-means algorithm and their combination. Compared with the traditional fuzzy c-means and shadowed c-means based approaches,the proposed algorithm can effectively tackle the problem of overlapping among segments and the noise in images. By observation,the proposed algorithms can obtain better segmentation results on test images.
SCM does not take full use of spatial information. This paper introduces some new image segmentation methods in the framework of shadowed c-means clustering. By implanting the local and non-local spatial information in the membership value estimation procedure,it proposes the local spatial shadowed C-means algorithm,non-local spatial shadowed C-means algorithm and their combination. Compared with the traditional fuzzy c-means and shadowed c-means based approaches,the proposed algorithm can effectively tackle the problem of overlapping among segments and the noise in images. By observation,the proposed algorithms can obtain better segmentation results on test images.
引文
[1]Buades A,Coll B,Morel J-M.A non-local algorithm for image denoising[G].In CVPR,2005.
[2]Balafar M A.Fuzzy c-mean based brain MRI segmentation algorithms[J].Springer Science+Business Media B.V,2014(41):40-349.
[3]Buades A,Coll B,Morel J-M.On image denoising methods[G].Technical Report 2004-15,CMLA,2004.
[4]Pedrycz W,Fernando G.An introduction to fuzzy sets:analysis and design[M].Cambridge,MA:MIT Press,1998.
[5]Wang X,Wang Y,Wang L.Improving fuzzy c-means clustering based on feature-weight learning.Pattern Recogn Lett,25:90-130.
[2]Balafar M A.Fuzzy c-mean based brain MRI segmentation algorithms[J].Springer Science+Business Media B.V,2014(41):40-349.
[3]Buades A,Coll B,Morel J-M.On image denoising methods[G].Technical Report 2004-15,CMLA,2004.
[4]Pedrycz W,Fernando G.An introduction to fuzzy sets:analysis and design[M].Cambridge,MA:MIT Press,1998.
[5]Wang X,Wang Y,Wang L.Improving fuzzy c-means clustering based on feature-weight learning.Pattern Recogn Lett,25:90-130.