| 引用本文: | 程丹松,何仕文,石大明,刘晓芳.基于Bregman散度和RSF模型的水平集图像分割方法[J].哈尔滨工业大学学报,2018,50(5):52.DOI:10.11918/j.issn.0367-6234.201703160 |
| CHENG Dansong,HE Shiwen,SHI Daming,LIU Xiaofang.The level set methodbased on Bregman divergence and RSF model for image segmentation[J].Journal of Harbin Institute of Technology,2018,50(5):52.DOI:10.11918/j.issn.0367-6234.201703160 |
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| 摘要: |
| 在自然图像中经常会出现亮度不均匀的现象，虽然基于局部信息的水平集方法在不均匀图像的分割方面取得了较好的效果，但是该类方法在主动轮廓的能量上存在局部最小值和计算复杂度高等问题.针对这些问题，本文提出了基于Bregman散度分布和区域可伸缩拟合能量模型(Region-Scalable Fitting，RSF)相结合的水平集图像分割方法.本方法利用包含特征点信息(Bregman散度)的全局信息项加快远离目标边界曲线的演化速度，提高算法对初始位置的鲁棒性；利用RSF模型的局部信息项提高对亮度不均匀图像的分割能力，吸引轮廓曲线向物体边界收敛，并停止在目标对象的边界处.通过对合成图像、医学图像和其它真实图像的对比实验，可以看出本文模型与现有模型(LCV、RSF和LGIF)相比，对亮度不均匀图像具有更强的处理能力和更高的处理效率，且对噪声具有更强的鲁棒性.
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| 关键词: 水平集 图像分割 Bregman散度 RSF模型 |
| DOI：10.11918/j.issn.0367-6234.201703160 |
| 分类号:TP391 |
| 文献标识码:A |
| 基金项目:国家自然科学基金项目 (61402133) |
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| The level set methodbased on Bregman divergence and RSF model for image segmentation |
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CHENG Dansong1,HE Shiwen1,SHI Daming1,LIU Xiaofang2
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(1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China; 2. School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)
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| Abstract: |
| Local information based level set methods are comparatively effective in segmention of images with intensity inhomogeneity which often occurs in real images. However, there are problems such as local minima in the active contour energy and the considerable computing-consuming in these models in practice. A novel level set method based on improved region-scalable fitting is proposed for such image segmentation. The global information containing local feature term, measured by Bregman divergence, is utilized to accelerate the contour evolution when the contour is far away from object boundaries, in order to improve the robustness of the algorithm to the initial position. The local feature item of the RSF model is used to promote the convergence of the contour curve to the boundary of the object, thereby increase the capability to cope with intensity inhomogeneity. Finally, comparative experiments on synthetic images, mediation images and the real images validate the performance of the algorithm.
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| Key words: level set Image segmentation Bregman divergence Region-Scalable Fitting model |
