Distance Regularized Level Set Image Segmentation Algorithm by Means of Dislocation Theory
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摘要: 把材料科学中的位错理论引入到水平集方法中.图像中水平集曲线的演化被看作刃位错中位错线的滑移过程，运用位错动力学机制推导出驱使水平集曲线演化的位错组态力.结合距离正则化水平集方法，把水平集方法的边缘检测函数替换为基于位错动力学理论的速度停止函数，并构建了新的距离正则化水平集函数演化方程.水平集曲线在位错组态力和速度停止函数的驱使下移动.位错组态力反映了单位长度曲线上的平均受力情况，不仅包括了图像梯度信息，也包括了位错组态力的作用范围等信息，因此可以有效地避免在局部图像梯度异常的情况下发生曲线停止演进的现象，或者避免在弱边缘处由于图像梯度较小发生局部边界泄漏的现象.实验结果表明，本文算法对弱边缘图像具有较好的分割效果.Abstract: Dislocation theory of materials science is introduced into the level set method. Curve evolution of the level set method is viewed as a slipping process of edge dislocation, and the evolution of zero level set is driven by the dislocation configuration force which is derived from the dislocation dynamics mechanism. Combined with distance regularized level set method, the edge indicator function is replaced by the speed stopping function, and a new evolution equation of distance regularized level set method is constructed with the dislocation theory. In the proposed algorithm, the dislocation configuration force and the speed stopping function drive the curve evolution of level set. The dislocation configuration force reflects the average force on the unit length curve, not only including the gradient information of image but also including the information of the effective range of the dislocation configuration force. The proposed algorithm can effectively avoid the phenomenon that the level set function stops evolution because of abnormal image gradient, and the phenomenon of boundary leakage because of the smaller image gradient. Experimental results show that the proposed algorithm has good segmentation performance for images with weak boundaries.
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Key words:
- Image segmentation /
- level set method /
- dislocation /
- distance regularized evolution
1) 本文责任编委 张长水 -
图 6 弱边缘图像分割对比实验结果
Fig. 6 The comparison of experimental results of weak boundary image segmentation
表 1 图 6所示实验中各算法的迭代次数和运算时间的对比
Table 1 The comparison of the iteration number and the operation time for the experiments shown in Fig. 6
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表 2 图 7所示实验中各算法的迭代次数、运算时间、面积重叠误差以及边界平均距离的对比
Table 2 The comparison of the iteration number, the operation time, area overlap error, and average boundary distance for the experiments shown in Fig. 7
算法 迭代次数 运算时间(秒) $E_{\rm overlap}$ $D_{\rm mean}$ Caselles[1] 5 000 159.536302 - - CV[2] 2 915 103.690744 21.19 $\%$ 13.62 Bernard[3] 18 121.678605 14.68 $\%$ 9.54 Shi[4] 798 184.167387 18.81 $\%$ 11.71 DRLSE[11] 2 320 383.120423 0.42 $\%$ 0.31 Min[6] 500 71.325241 - - Wang[7] 2 032 258.625021 0.65 $\%$ 0.48 本文算法 1 865 285.869702 0.36 $\%$ 0.27
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