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改进K-means活动轮廓模型

DOI: 10.11834/jig.20151206

Keywords: 图像分割,活动轮廓,水平集方法,C-V模型,K-means,灰度非同质

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Abstract:

目的通过对C-V模型能量泛函的Euler-Lagrange方程进行变形,建立其与K-means方法的等价关系,提出一种新的基于水平集函数的改进K-means活动轮廓模型。方法该模型包含局部自适应权重矩阵函数,它根据像素点所在邻域的局部统计信息自适应地确定各个像素点的分割阈值,排除灰度非同质对分割目标的影响,进而实现对灰度非同质图像的精确分割。结果通过分析对合成以及自然图像的分割结果,与传统及最新经典的活动轮廓模型相比,新模型不仅能较准确地分割灰度非同质图像,而且降低了对初始曲线选取的敏感度。结论提出了包含权重矩阵函数的新活动轮廓模型,根据分割目的和分割图像性质,制定不同的权重函数,该模型具有广泛的适用性。文中给出的一种具有局部统计特性的权重函数,对灰度非同质图像的效果较好,且对初始曲线位置具有稳定性。

References

[1]  Kass M, Witkin A, Terzopoulos D. Snakes:active contour models[J]. International Journal of Computer Vision, 1988, 1(4):321-331.
[2]  Caselles V, Kimmel R, Sapiro G. Geodesic active contours[J]. International Journal of Computer Vision, 1997, 22(1):61-79.
[3]  Cohen L D. On active contour models and balloons[J]. CVGIP:Image Understanding, 1991, 53(2):211-218.
[4]  Xu C Y, Prince J L. Snakes, shapes, and gradient vector flow[J]. IEEE Transactions on Image Processing,1998, 7(3):359-369.
[5]  Xu C Y, Prince J L. Generalized gradient vector flow external force for active contours[J]. Signal Processing, 1988, 71:131-139.
[6]  Chan T F, Vese V. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2):266-277.
[7]  Lie J, Lysaker M, Tai X C. A binary level set model and some application to Mumford-Shah image segmentation[J]. IEEE Transactions on Image Processing, 2006, 15(5):1171-1181.
[8]  Mukherjee S. Region based segmentation in presence of intensity inhomogeneity using legendre polynomials[J]. Signal Processing Letters, IEEE, 2015:298-302.
[9]  Dubrovina A. Multi-region active contours with a single level set function[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 99:1-19.
[10]  Vese L, Chan T F. A multiphase level set frame work for image segmentation using the Mumford and Shah model[J]. International Joumal of Computer Vision, 2002, 50(3):271-293.
[11]  Tsai A, Yezzi A, Willsky A. Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification[J]. IEEE Transactions on Image Processing, 2001, 10(8):1169-1186.
[12]  Li C M, 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.
[13]  Zhang K H, Song H H, Zhang L. Active contours driven by local image fitting energy[J]. Pattern Recognition, 2010, 43(4):1199-1206.
[14]  Dong F F, Chen Z S, Wang J W. A new level set method for inhomogeneous image segmentation[J]. Image and Vision Computing, 2013, 31:809-822.
[15]  Zhang K H, Zhang L, Song H H, et al. Active contours with selective local or global segmentation:a new formulation and level set method[J]. Image and Vision Computing, 2010, 28(4):668-676.
[16]  Liu T T, Xu H Y, Jin W, et al. Medical image segmentation based on a hybrid region-based active contour model[J]. Computational and Mathematical Methods in Medicine, 2014:1-10.
[17]  Wang L F, Pan C H. Robust level set image segmentation via a local correntropy-based K-means clustering[J]. Pattern Recognition, 2014, 5:1917-1925.
[18]  Huang C C, Zeng L. Robust image segmentation using local robust statistics and correntropy-based Kmeans clustering[J]. Optics and Lasers in Engineering, 2015, 66:187-203.
[19]  Osher S, Fedkiw R. Level Set Methods and Dynamic Implicit Surfaces[M]. Berlin:Springer-Verlag, 2002.
[20]  Chan T, Zhu W. Level set based shape prior segmentation[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA:IEEE Computer Society 2005, 2005, 2:1164-1170.
[21]  更多...
[22]  Gibou F, Fedkiw R. A fast hybrid k-means level set algorithm for segmentation[C]//Proc. of 4th Annu. Hawaii Int. Conf. Statistics and Mathematics. Honolulu, Hawaii, USA:American Statistical Association-Hawaii Chapter, 2005:281-291.

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