用于灰度不均图像分割的自适应灰度拟合模型

doi:10.11772/j.issn.1001-9081.2019020364

计算机应用 ›› 2019, Vol. 39 ›› Issue (9): 2719-2725.DOI: 10.11772/j.issn.1001-9081.2019020364

• 虚拟现实与多媒体计算 • 上一篇下一篇

用于灰度不均图像分割的自适应灰度拟合模型

张栩源, 王艳

重庆师范大学数学科学学院, 重庆 401331

收稿日期:2019-03-06 修回日期:2019-05-24 出版日期:2019-09-10 发布日期:2019-06-17
通讯作者: 王艳
作者简介:张栩源(1993-),男,湖南常德人,硕士研究生,主要研究方向:图像处理的偏微分方程方法;王艳(1984-),女,山东青岛人,副教授,博士,主要研究方向:图像处理的偏微分方程方法。
基金资助:
重庆市教委科学技术研究项目（KJQN201800537）；重庆师范大学国家基金预研项目（16XYY21，16XYY23）；重庆师范大学博士启动基金资助项目（17XLB001）。

Adaptive intensity fitting model for segmentation of images with intensity inhomogeneity

ZHANG Xuyuan, WANG Yan

School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China

Received:2019-03-06 Revised:2019-05-24 Online:2019-09-10 Published:2019-06-17
Supported by:
This work is partially supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN201800537), the National Fund Pre-research Project of Chongqing Normal University (16XYY21, 16XYY23), the Doctor Start-up Foundation of Chongqing Normal University (17XLB001).

摘要/Abstract

摘要：

针对灰度不均图像的分割问题，提出了一个结合全局信息的局部区域自适应灰度拟合模型。首先，分别利用图像的局部和全局信息构造了局部拟合项和全局拟合项；其次，利用像素点邻域内灰度的极差反映该点邻域内灰度的偏差程度，并以此定义了一个自适应权值函数；最后，利用定义的权值函数为局部项和全局项自适应赋权值，得到所提模型的能量泛函，并使用变分法推导出模型的水平集函数迭代方程。数值实现采用有限差分法。实验结果表明，与区域可变灰度拟合（RSF）模型和局部和全局灰度拟合（LGIF）模型相比，所提模型不仅能够稳定、准确地分割多种灰度不均图像，而且对演化曲线初始轮廓的位置、大小和形状具有更强的鲁棒性。

关键词: 图像分割, 自适应权值, 局部区域信息, 灰度不均图像, 水平集方法

Abstract:

For the segmentation of images with intensity inhomogeneity, a region-adaptive intensity fitting model combining global information was proposed. Firstly, the local and global terms were constructed based on local and global image information respectively. Secondly, an adaptive weight function was defined to indicate the deviation degree of the gray scale of a pixel neighborhood by utilizing the extreme difference level in the pixel neighborhood. Finally, the defined weighting function was used to assign weights to local and global terms adaptively to obtain the energy functional of the proposed model and the iterative equation of the model's level set function was deduced by the variational method. The experimental results show that the proposed model can segment various inhomogeneous images stably and accurately in comparison with Region-Scalable Fitting (RSF) model and Local and Global Intensity Fitting (LGIF) model, which is more robust in the position, size and shape of initial contour of evolution curve.

Key words: image segmentation, adaptive weight, local region information, intensity inhomogeneous image, level set method

中图分类号:

TP391.41

张栩源, 王艳. 用于灰度不均图像分割的自适应灰度拟合模型[J]. 计算机应用, 2019, 39(9): 2719-2725.

ZHANG Xuyuan, WANG Yan. Adaptive intensity fitting model for segmentation of images with intensity inhomogeneity[J]. Journal of Computer Applications, 2019, 39(9): 2719-2725.

参考文献

[1] CHEN B, HUANG S, LIANG Z R, et al. A fractional order derivative based active contour model for inhomogeneous image segmentation[J]. Applied Mathematical Modelling, 2019, 65:120-136.
[2] 陈星,王艳,吴漩.结合全局信息的局部图像灰度拟合模型[J].计算机应用,2018,38(12):3574-3579.(CHEN X, WANG Y, WU X. Local image intensity fitting model combining global image information[J]. Journal of Computer Applications, 2018, 38(12):3574-3579.)
[3] 李钢,李海芳,尚方信,等.结合局部灰度差异的噪声图像分割模型[J].计算机应用,2018,38(3):842-847.(LI G, LI H F, SHANG F X, et al. Noise image segmentation model with local intensity difference[J]. Journal of Computer Applications, 2018, 38(3):842-847.)
[4] CAI Q, LIU H Y, ZHOU S, et al. An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation[J]. Pattern Recognition, 2018, 82:79-93.
[5] 韩斌,吴一全,宋昱.利用区域信息融合混合活动轮廓模型的河流遥感图像分割[J].中国图象图形学报,2017,22(2):212-224. (HAN B, WU Y Q, SONG Y. Segmentation of remote sensing images of rivers utilizing a hybrid active contour model with regional information fusion[J]. Journal of Image and Graphics, 2017, 22(2):212-224.)
[6] CHAN T F, VESE L A. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2):266-277.
[7] LI C, KAO C-Y, GORE J C, et al. Implicit active contours driven by local binary fitting energy[C]//Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2007:1-7.
[8] 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.
[9] HE C J, WANG Y, CHEN Q. Active contours driven by weighted region-scalable fitting energy based on local entropy[J]. Signal Processing, 2012, 92(2):587-600.
[10] WANG L, LI C M, SUN Q S, et al. Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation[J]. Computerized Medical Imaging and Graphics, 2009, 33(7):520-531.
[11] ZHANG K H, ZHANG L, LAM K M, et al. A level set approach to image segmentation with intensity inhomogeneity[J]. IEEE Transactions on Cybernetics, 2016, 46(2):546-557.
[12] 蔡青,刘慧英,周三平,等.基于局部和全局信息的自适应水平集图像分割[J].强激光与粒子束,2017,29(2):28-35.(CAI Q, LIU H Y, ZHOU S P, et al. Adaptive level set model based on local and global intensity information for image segmentation[J]. High Power Laser and Particle Beams, 2017, 29(2):28-35.)
[13] 赵怡,邓红霞,张玲,等.基于最大类间方差的权重自适应活动轮廓模型[J].计算机工程与设计,2018,39(2):486-491.(ZHAO Y, DENG H X, ZHANG L, et al. Weight-self adjustment active contour model based on method of maximum classes square error[J]. Computer Engineering and Design, 2018, 39(2):486-491.)
[14] ZHANG K H, SONG H H, ZHANG L. Active contours driven by local image fitting energy[J]. Pattern Recognition, 2010, 43(4):1199-1206.
[15] YU Y, ZHANG C M, WEI Y, et al. Active contour method combining local fitting energy and global fitting energy dynamically[C]//Proceedings of the 2010 International Conference of Medical Biometrics, LNCS 6165. Berlin:Springer, 2010:163-172.
[16] LI C M, XU C Y, GUI C F, et al. Level set evolution without re-initialization:a new variational formulation[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2005, 1:430-436.
[17] SHATTUCK D W, SANDOR-LEAHY S R, SCHAPER K A, et al. Magnetic resonance image tissue classification using a partial volume model[J]. NeuroImage, 2001, 13(5):856-876.
[18] LIU B, CHENG H D, HUANG J H, et al. Probability density difference-based active contour for ultrasound image segmentation[J]. Pattern Recognition, 2010, 43(6):2028-2042.
[19] ANTONIO L, SALAZAR-LICEA, CARLOS J, et al. Location of mammograms ROI's and reduction of false-positive[J]. Computer Methods and Programs in Biomedicine, 2017, 143:97-111.
[20] LIU T Y, LO K T, ZHANG X D, et al. A new cut detection algorithm with constant false-alarm ratio for video segmentation[J]. Journal of Visual Communication and Image Representation, 2004, 15(2):132-144.

用于灰度不均图像分割的自适应灰度拟合模型

Adaptive intensity fitting model for segmentation of images with intensity inhomogeneity

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