一种基于多特征的距离正则化水平集快速分割方法

于海平, 何发智, 潘一腾, 陈晓

电子学报 ›› 2017, Vol. 45 ›› Issue (3) : 534-539.

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电子学报 ›› 2017, Vol. 45 ›› Issue (3) : 534-539. DOI: 10.3969/j.issn.0372-2112.2017.03.004
学术论文

一种基于多特征的距离正则化水平集快速分割方法

  • 于海平1,2,3, 何发智1,2, 潘一腾1,2, 陈晓1,2
作者信息 +

A Fast Distance Regularized Level Set Method for Segmentation Based on Multi-features

  • YU Hai-ping1,2,3, HE Fa-zhi1,2, PAN Yi-teng1,2, CHEN Xiao1,2
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摘要

现有的图像分割模型存在对初始化信息敏感,分割速率慢,图像弱边界区的泄露等现象.提出了一种混合快速分割方法.该方法利用偏压场近似估计图像的局部统计信息,并结合全局信息相容性及改进的距离正则化方法建立模型,最后将模型嵌入水平集框架中,与此同时,引入双重终止准则以提高分割的速度.最后利用合成图像和真实图像进行分割实验,并与CV(Chan-Vese)模型、非线性自适应水平集方法以及局部尺度拟合模型对比,表明本方法不仅对初始化信息敏感度降低,而且分割速度提高3~5倍.

Abstract

The existing image segmentation models have problems of being sensitive to initialization information,slower segmentation and leaked weak image boundary regions.This paper presents a hybrid fast segmentation model which utilizes the local statistics of bias field approximated images,the global information of compatibility and the distance regularization method.Then the model is embedded into level set framework.In addition,a dual termination standard is constructed to improve the speed of segmentation.Experiments on synthetic and real images are conducted to verify the efficiency of our model.Moreover,comparisons with the well-known CV model,nonlinear adaptive level set model and region scalable fitting model demonstrate that the proposed model reduces the sensitivity to the initialization and improves the segmentation speed by 3~5 times.

关键词

图像分割 / 水平集 / 距离正则化 / 近似估计 / 多特征

Key words

image segmentation / level set / distance regularization / approximately estimate / multi-features

引用本文

导出引用
于海平, 何发智, 潘一腾, 陈晓. 一种基于多特征的距离正则化水平集快速分割方法[J]. 电子学报, 2017, 45(3): 534-539. https://doi.org/10.3969/j.issn.0372-2112.2017.03.004
YU Hai-ping, HE Fa-zhi, PAN Yi-teng, CHEN Xiao. A Fast Distance Regularized Level Set Method for Segmentation Based on Multi-features[J]. Acta Electronica Sinica, 2017, 45(3): 534-539. https://doi.org/10.3969/j.issn.0372-2112.2017.03.004
中图分类号: TP391   

参考文献

[1] Bleau A,Leon L J.Watershed-based segmentation and region merging [J].Computer Vision and Image Understanding,2000,77(3):317-370.
[2] Peng B,Zhang L,Zhang D.Automatic image segmentation by dynamic region merging [J].IEEE Transactions on Image Processing,2011,20(12):3592-3605.
[3] Boykov Y,Kolmogorov V.An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision [J].Pattern Analysis and Machine Intelligence,2004,26(9):1124-1137.
[4] Vese L A,Chan T F.A multiphase level set framework for image segmentation using the Mumford and Shah Model [J].International Journal of Computer Vision,2002,50(3):271-293.
[5] Osher S,Sethian J A.Fronts propagating with curvature dependent speed:algorithms based on Hamilton-Jacobi formulations [J].Journal of Computational Physics,1988,79(1):12-49.
[6] Chan T F,Vese L.Active contours without edges [J].IEEE Transactions on Image Processing,2001,10(2):266-277.
[7] Chen Y T,Tseng D C.Medical Image Segmentation Based on the Bayesian Level Set Method [M].Berlin:Springer-Verlag,2008.25-34.
[8] Wang B,Gao X,Tao D.A nonlinear adaptive level set for image segmentation [J].IEEE Transactions on Cybernetics,2014,44(3):418-428.
[9] Ni B,He F Z,Yuan Z Y.Segmentation of uterine fibroid ultrasound images using a dynamic statistical shape model in HIFU therapy [J].Computerized Medical Imaging and Graphics,2015,46:302-314.
[10] 李亚峰.基于图像分解的稀疏正则化多区域图像分割方法 [J].电子学报,2015,43(9):1841-1849. Li Ya-feng.A sparsity regularized multiregion image segmentation method based on image decomposition [J].Acta Electronica Sinica,2015,43(9):1841-1849.(in Chinese)
[11] Milione G,Dudley A,Nguyen T A.Measuring the self-healing of the spatially inhomogeneous states of polarization of vector Bessel beams [J].Journal of Optics,2015,17(3):1-7.
[12] 许新征,丁世飞,史忠植.图像分割的新理论和新方法 [J].电子学报,2010,38(2):76-82. Xu Xin-zheng,Ding Shi-fei,Shi Zhong-zhi.New theories and methods of image segmentation [J].Acta Electronica Sinica,2010,38(2):76-82.(in Chinese)
[13] Hou Z.A review on MR image intensity inhomogeneity correction [J].International Journal of Biomedical Imaging,2006,2006:1-11.
[14] Li C M,Kao C Y,Gore J C.Minimization of region-scalable fitting energy for image segmentation [J].IEEE Transactions on Image Processing,2008,17(10):1940-1949.

基金

国家自然科学基金 (No.61472289); 湖北省自然科学基金 (No.2015CFB254)

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