结合局部能量与改进的符号距离正则项的图像目标分割算法

韩明; 刘教民; 孟军英; 震洲; 王敬涛

doi:10.11999/JEIT141473

结合局部能量与改进的符号距离正则项的图像目标分割算法

doi: 10.11999/JEIT141473 cstr: 32379.14.JEIT141473

2.
(石家庄学院计算机系石家庄 050035) ②(燕山大学信息科学与工程学院河北省计算机虚拟技术与系统集成重点实验室秦皇岛 066004) ③(河北科技大学信息科学与工程学院石家庄 050018)

基金项目:

河北省自然科学基金(F2012208004)，河北省教育厅高等学校科学研究计划自然科学重点项目(ZD20132013)和河北省科技支撑计划项目(14210302D)

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出版历程
- 收稿日期: 2014-11-24
- 修回日期: 2015-03-23
- 刊出日期: 2015-09-19

Local Energy Information Combined with Improved Signed Distance Regularization Term for Image Target Segmentation Algorithm

摘要

摘要: 针对传统C-V模型对颜色不均匀图像分割失败并且对初始轮廓和位置敏感问题，以及现有符号距离正则项存在周期性振荡和局部极值问题。该文提出结合局部能量信息和改进的符号距离正则项的图像目标分割算法。首先，将全局图像信息扩展到HSV空间，并使用局部能量项信息分析每个像素及其领域内的统计特性，从而在较少的迭代次数内有效分割颜色分布不均匀图像。其次，改进现有符号距离正则项，改进后的符号距离正则项在避免水平集函数的重新初始化的同时，提高了计算效率，保证了水平集函数演化过程的稳定性。然后，定义阈值判断法的水平集函数演化的终止准则，使曲线准确演化到目标轮廓。该算法与同类模型的对比实验表明该模型具有较高的分割精度和对初始轮廓的鲁棒性。
- 图像处理 /
- 局部能量 /
- 符号距离约束项 /
- 水平集演化 /
- C-V模型
Abstract: The uneven color image can not be segmented successfully with the traditional C-V model, and the C-V model is sensitive to the initial contour and the location. The existing signed distance regularization term has disadvantages, such as the periodic oscillation and the local extremum. This paper proposes the target segmentation algorithm, which combines the local energy information with improved signed distance regularization term. Firstly, the global image information can be expanded to the HSV space, and each pixels and its statistical properties are analyzed with the local energy information within the neighborhood, which can effectively realize the uneven distribution of color image segmentation in less iteration. Secondly, the improved signed distance regularization term avoids re-initialization of level set function, improving the computational efficiency, and maintains stability in the level set function evolution process. Finally, the termination criterion of threshold evaluation method for the level set function evolution is defined, in order to make the curve accurately evolution to the target contour. The experimental results show that the proposed algorithm has higher segmentation accuracy and robust than other similar models.
- Image processing /
- Local energy /
- Signed distance regularization term /
- Level set evolution /
- C-V model

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