全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

融合全局和局部相关熵的图像分割

DOI: 10.11834/jig.20151207

Keywords: 相关熵,变分法,水平集,动态组合

Full-Text   Cite this paper   Add to My Lib

Abstract:

目的针对LCK(localcorrentropy-basedK-means)模型对初始轮廓敏感的问题,提出了新的基于全局和局部相关熵的GLCK(globalandlocalcorrentropy-basedK-means)动态组合模型。方法首先将相关熵准则引入到CV(Chan-Vese)模型中,得到新的基于全局相关熵的GCK(globalcorrentropy-basedK-means)模型。然后,结合LCK模型,提出GLCK组合模型,并给出一种动态组合算法来优化GLCK模型。该模型分两步来完成分割:第1步,用GCK模型分割出目标的大致轮廓;第2步,将上一步得到的轮廓作为LCK模型的初始轮廓,对图像进行精确分割。结果主观上,对自然图像和人工合成图像进行分割,并同LCK模型、LBF模型以及CV模型进行对比,结果表明本文所提模型的鲁棒性比上述模型都要好;客观上,对BSD库中的两幅自然图像进行分割,并采用Jaccard相似性比率进行定量分析,准确率分别为91.37%和89.12%。结论本文算法主要适用于分割含有未知噪声及灰度分布不均匀的医学图像及结构简单的自然图像,并且分割结果对初始轮廓具有鲁棒性。

References

[1]  Caselles V, Kimmel R, Sapiro G. Geodesic active contours[J]. International Journal of Computer Vision,1997,22(1):61-79.[DOI:10.1023/A:1007979827043]
[2]  Kass M, Witkin A, Terzopoulos D. Snakes:active contour models[J]. International journal of computer vision, 1988, 1(4):321-331.[DOI:10.1007/BF00133570]
[3]  Chan T F, Vese L A. Active contours without edges[J].IEEE Transactions on Image Processing, 2001, 10(2):266-277.[DOI:10.1109/83.902291]
[4]  Li C, Kao C Y, Gore J C, et al. Minimization of region-scalable fitting energy for image segmentation[J].IEEE Transactions on Image Processing, 2008, 17(10):1940-1949.[DOI:10.1109/TIP.2008.2002304]
[5]  Zhang K, Song H, Zhang L. Active contours driven by local image fitting energy[J]. Pattern Recognition, 2010, 43(4):1199-1206.[DOI:10. 1016/j.patcog.2009.10.010]
[6]  Wang L, Pan C. Robust level set image segmentation via a local correntropy-based K-means clustering[J]. Pattern Recognition, 2014, 47(5):1917-1925.[DOI:10.1016/j.patcog.2013.11. 014]
[7]  Wang H, Huang T Z, Xu Z, et al. An active contour model and its algorithms with local and global Gaussian distribution fitting energies[J]. Information Sciences,2014,263:43-59.[DOI:10.1016/j.ins.2013.10.033]
[8]  Wang H, Huang T Z. An adaptive weighting parameter estimation between local and global intensity fitting energy for image segmentation[J]. Communications in Nonlinear Science and Numerical Simulation, 2014,19(9):3098-3105.[DOI:10.1016/j.cnsns.2014.02.015]
[9]  Du X, Cho D, Bui T D. Image segmentation and inpainting using hierarchical level set and texture mapping[J]. Signal Processing, 2011, 91(4):852-863.[DOI:10.1016/j.sigpro.2010.09.002]
[10]  Dydenko I, Jamal F, Bernard O, et al. A level set framework with a shape and motion prior for segmentation and region tracking in echocardiography[J]. Medical image analysis, 2006, 10(2):162-177.[DOI:10.1016/j.media.2005.06.004]
[11]  Xiao C, Gan J, Hu X. Fast level set image and video segmentation using new evolution indic-ator operators[J]. The Visual Computer, 2013, 29(1):27-39.[DOI:10.1007/s00371-012-0672-5]
[12]  Cremers D, Rousson M, Deriche R. A review of statistical approaches to level set segment-ation:integrating color, texture, motion and shape[J]. International Journal of Computer Vision, 2007, 72(2):195-215.[DOI:10.1007/s11263-006-8711-1]
[13]  Zhou H, Zheng J, Wei L. Texture aware image segmentation using graph cuts and active contours[J]. Pattern Recognition, 2013, 46(6):1719-1733.[DOI:10.1016/j.patcog.2012.12.005]
[14]  Cui Y L. Segmentation algorithm signed distan-ce function variational level set based image[J]. Pattern Recognition and Artificial Intelligence, 2013,26(11):1034-1040.[崔玉玲.基于改进符号距离函数的变分水平集图像分割算法[J].模式识别与人工智能, 2013, 26(11):1034-1040.]
[15]  Brox T, Rousson M, Deriche R, et al. Colour, texture, and motion in level set based segmentation and tracking[J]. Image and Vision Computing, 2010, 28(3):376-390.[DOI:10.1016/j.imavis.2009.06.009]
[16]  Liu W, Pokharel P P, Príncipe J C. Correntropy:properties and applications in non-Gaussian signal processing[J].IEEE Transactions on Signal Processing, 2007, 55(11):5286-5298.[DOI:10.1109/TSP.2007.896065]
[17]  Li C, Xu C, Gui C, et al. Level set evolution without re-initialization:a new variational formulation[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington DC, USA:IEEE, 2005, 1:430-436.[DOI:10.1109/CVP R.2005.213]
[18]  Li C, Xu C, Gui C, et al. Distance regularized level set evolution and its application to image segmentation[J]. IEEE Transactions on Image Processing, 2010, 19(12):3243-3254.[DOI:10.1109/TIP.2010.2069690]
[19]  Zhang K, Zhang L, Song H, et al. Reinitialization-free level set evolution via reaction diffusion[J]. IEEE Transactions on Image Processing, 2013, 22(1):258-271.[DOI:10.1109/TIP.2012.2214046]

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133