浏览全部资源
扫码关注微信
收稿：2019-06-21，
修回：2019-8-31，
录用：2019-9-7，
纸质出版：2020-02-16
移动端阅览
目的
2
图像因各种因素的影响存在一定程度的噪声，而噪声会在图像分割时影响待分割目标的边缘识别，导致分割结果难以达到理想状态。针对以上问题，在距离规则化水平集（DRLSE）演化模型的基础上，提出一种将各向异性扩散散度场信息融合到DRLSE模型中的新模型。
方法
2
将水平集函数初始化为分段常数表达式，设定演化方程中的参数和水平集函数演化过程中的迭代时间步长Δ
$$t$$
。随后将常值权系数
$$α$$
替换为融合各项异性扩散散度场信息的变权系数
$$α$$
（
$$I$$
），对水平集函数的演化方程进行迭代演化，直至收敛到目标边缘。输出最终演化轮廓。
结果
2
对选自Weizmann数据库的图像和经过人为改造的的图像进行图像分割实验，采用迭代时间和评价分割结果相似性的J系数（Jaccard相似性系数）和D系数（Dice相似性系数）等定量指标进行评价。对无噪声图像和噪声图像分割时，本文模型的J系数和D系数均比DRLSE模型的值大，表明本文模型的分割结果与真值图像的相似性较高。在分割时间方面，仅在分割背景简单边缘清晰的无噪声图像时，本文模型较DRLSE模型略长；在分割边缘清晰、背景灰度不均匀和边缘模糊、背景灰度不均匀的无噪声图像以及人为添加噪声的各种情况下，本文模型分割时间均明显短于DRLSE模型。其中，对边缘模糊、背景灰度不均匀的无噪声图像，本文模型分割时间为3.718 s，较DRLSE模型短9.523 s；对存在噪声、待分割目标存在凹区域且边缘模糊背景灰度不均匀图像，本文模型分割时间为4.235 s，较DRLSE模型短35.165 s。
结论
2
实验结果表明，融合了各向异性扩散信息的DRLSE模型在图像分割尤其是噪声图像分割方面，具有明显的有效性、高效性和鲁棒性。
Objective
2
Image segmentation is based on the grayscale information of the image
and the homogenous regions with different properties of the image are divided using different methods without overlapping each other. When image segmentation is performed to obtain the region of interest
the image will inevitably have a certain degree of noise due to various factors
the noise will cause the image edge to be weakened
the false edge will be generated during segmentation
the segmentation curve will fall into the local minimum
and the evolution will stop. The situation
in turn
affects the accuracy of the edge recognition of the target to be segmented during image segmentation
and the segmentation result encounters difficulty in achieving the desired effect. The level set method based on active set is widely used in image segmentation
but it is still affected by noise. Thus
a new method is proposed to solve the noise problem.
Method
2
Since given that the anisotropic diffusion model is applied to image segmentation
including denoising and maintaining the edge of the target to be segmented
a new model is proposed to fuse the anisotropic diffusion divergence field information into the DRLSE model to improve the efficiency and accuracy of the existing segmentation algorithm of the noise image based on the distance regularization level set (DRLSE) evolution model.The model can overcome the problems of the distance regularized level set model
such as slow convergence speed
easy to fall into the false boundary and leak from the weak edge. The improved model can accelerate the initial contour evolution to the edge of the target to be segmented when segmenting the noise image. The main improvement is to change the constant coefficient α of the control area term in the DRLSE evolution model to the variable weight coefficient α(
$$I$$
) on the basis of the anisotropic diffusion divergence field information. Given that the improved variable weight coefficient is based on the anisotropic diffusion information
the improved nonedge force is considerably smoothed down
the vector force at the edge is strengthened
and the edge interfered by noise is strengthened. The improved model can accelerate the initial contour evolution to the edge of the target to be segmented when segmenting the noise image.
Result
2
Image segmentation experiments are performed on several images selected from the Weizmann database and artificially modified images. Segmentation time
D coefficient
and J coefficient are compared (the closer the D and J coefficients to the 1 segmentation result and true value image similarity
the higher the ratio
and the greater the similarity between the segmentation efficiency and result). The noise-free image is segmented. If the image background is simple and the edge is clear
then the D-factor and J-factor of the model segmentation result are 0.996 4 and 0.992 4
respectively. The D-factor and J-factor of the DRLSE model segmentation result are 0.995 6 and 0.990 6
respectively. The model segmentation time is 3.809 s. Compared with the 3.809 s of the DRLSE model
the D-factor and J-factor of the model segmentation result are 0.994 6 and 0.993 1
respectively
and the D-factor and J-factor of the DRLSE model are 0.991 1 and 0.981 8
respectively. The model segmentation time of 4.294 s is shorter than the DRLSE model's 4.966 s. For the edge part
the fuzzy background gray image is uneven. The D and J coefficients of the model segmentation result are 0.997 1 and 0.989 3
respectively. The D and J coefficients of the DRLSE model segmentation result are 0.970 4 and 0.931 5
respectively. The model segmentation time of 3.718 s is shorter than the DRLSE model's 13.241 s. The noise image is segmented. If the background of the image is simple
then the edge is only an artificially added noise. The D and J coefficients of the model segmentation result are 0.997 6 and 0.995 0
respectively
and the D and J coefficients of the DRLSE model segmentation result are 0.997 2 and 0.994 2
respectively. The time of 4.182 s is shorter than the DRLSE model's 11.274 s. For the image with noise and uneven edge gray
the D and J coefficients of the model segmentation are 0.990 3 and 0.980 2
respectively. The D and J coefficients of the DRLSE model segmentation result are 0.980 5 and 0.960 3
respectively. The model segmentation time of 4.294 s is shorter than the DRLSE model's 4.966 s. For the image where the noise is to be segmented
a concave region is observed
and the edge blurred background gray is uneven. The D and J coefficients of the model segmentation result are 0.985 7 and 0.970 9
respectively. The D and J coefficients of the DRLSE model are 0.885 3 and 0.747 3
respectively. The model segmentation time of 4.235 s is shorter than the DRLSE model's 39.400 s. Results of the segmentation experiments show that the improved model is considerably better in terms of segmentation accuracy and runtime than the original model for noisy and noise-free images.
Conclusion
2
In this paper
we introduce the DRLSE evolution model based on the anisotropic diffusion divergence field model. The improved model can accelerate the curve evolution to the target boundary given gray unevenness in the background. The image can overcome the noise and edge weakening caused by the noise figure in evolution and several shortcomings of the DRLSE evolution model during evolution. Experimental results show the effectiveness
efficiency
and robustness of the fusion anisotropic diffusion information DRLSE model in image segmentation
especially noise image segmentation.
Alpert S, Galun M, Brandt A and Basri R. 2012. Image segmentation by probabilistic bottom-up aggregation and cue integration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(2):315-327[DOI:10.1109/TPAMI.2011.130]
Caselles V, Catté F, Coll T and Dibos F. 1993. A geometric model for active contours in image processing. Numerische Mathematik, 66(1):1-31[DOI:10.1007/BF01385685]
Cohen L D. 2010. On active contour models and balloons. CVGIP:Image Understanding, 53(2):211-218[DOI:10.1016/1049-9660(91)90028-N]
Kass M, Witkin A and Terzopoulos D. 1988. Snakes:active contour models. International Journal of Computer Vision, 1(4):321-331[DOI:10.1007/BF00133570]
Li C, Kao C Y and Gore J C. 2008. Minimization of region-scalable fitting energy for image segmentation. IEEE Transactions on Image Processing, 17(10):1940-1949[DOI:10.1109/TIP.2008.2002304]
Li C M, Xu C Y and Gui C F. 2005. Level set evolution without re-initialization: a new variational formulation//IEEE Computer Society Conference on Computer Vision Pattern Recognition.[ DOI: 10.1109/CVPR.2005.213 http://dx.doi.org/10.1109/CVPR.2005.213 ]
Li C M and Xu C Y. 2010. Distance regularized level set evolution and its application to image segmentation. Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA:IEEE, 2005(1):430-436[DOI:10.1109/TIP.2010.2069690]
Li M. 2014. Geometric active contours based on structure tensor for image segmentation. Application Research of Computer, 31(12):3890-3893
李梦. 2014.图像分割的结构张量几何活动轮廓模型.计算机应用研究, 31(12):3890-3893)[DOI:10.3969/j.issn.1001-3695.2014.12.095]
Lu Y Y, Qiang J R and Wang C. 2018. Image segmentation algorithm based on improved CV model. Modern electronic technology, 41(21):71-75
鲁圆圆, 强静仁, 汪朝.2018.基于改进CV模型的图像分割算法.现代电子技术, 41(21):71-75)[DOI:10.16652/j.issn.1004-373x.2018.21.016]
Malladi R, Sethian J A and Vemuri B C. 1995. Shape modeling with front propagation:a level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(2):158-175[DOI:10.1109/34.368173]
Perona P and Malik J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629-639
Wu L H, Hong Z Q and Yan X T. 2018. Anisotropic ddiffusion algorithm combined with local variance information for the research of image denoising. Jiangxi Science, 36(3):500-505
吴龙华, 洪志强, 闫晓天. 2018.结合局部方差信息的各向异性扩散图像去噪算法研究.江西科学, 36(3):500-505)[DOI:10.13990/j.issn1001-3679.2018.03.030]
Zhang G M, Zhou F F and Chu J. 2015. An improved variational level set used in image segmentation algorithm. Journal of Graphics, 36(5):740-746
张桂梅, 周飞飞, 储珺. 2015.一种改进的变分水平集的图像分割算法.图学学报, 36(5):740-746
Zhu Y L and Weng G R. 2008. Improved distance regularized level set evolution model by enhancing energy of area term. Journal of Graphics, 39(1):12-20
朱云龙, 翁桂荣. 2018.面积项能量加强的距离规则水平集演化模型.图学学报, 39(1):12-20)[DOI:10.11996/JG.j.2095-302X.2018010012]
Zijdenbos A P, Dawant B M, Margolin R A and Palmer A C. 1994. Morphometric analysis of white matter lesions in MR images:method and validation. IEEE Transactions on Medical Imaging, 13(4):716-724[DOI:10.1109/42.363096]
相关作者
相关机构
京公网安备11010802024621
