计算机应用 ›› 2011, Vol. 31 ›› Issue (05): 1249-1251.DOI: 10.3724/SP.J.1087.2011.01249

• 图形图像技术 • 上一篇    下一篇

快速稳定的局部二元拟合分割算法

林亚忠1,顾金库2,郝刚2,蔡茜2   

  1. 1.解放军第175医院(厦门大学 附属东南医院),福建 漳州363000
    2.厦门大学 计算机科学系,福建 厦门361005
  • 收稿日期:2010-11-01 修回日期:2010-12-30 发布日期:2011-05-01 出版日期:2011-05-01
  • 通讯作者: 林亚忠
  • 作者简介:林亚忠(1973-),男,福建漳州人,高级工程师,博士,主要研究方向:计算机图像处理、模式识别、数据挖掘,;顾金库(1985-),男,山东德州人,硕士研究生,主要研究方向:计算机图像处理;郝刚(1986-),男,内蒙古巴彦淖尔人,硕士研究生,主要研究方向:计算机图像处理;蔡茜(1986-),女,福建三明人,硕士研究生,主要研究方向:计算机图像处理。
  • 基金资助:

    福建省自然科学基金项目资助项目(2008J0312);南京军区“十一五”计划项目(06MA99);南京军区重点项目(08Z021)。

Fast and stable local bianry fitting approach for image segmentation

LIN Ya-zhong1, GU Jin-ku2, HAO Gang2, CAI Qian2   

  1. 1. The 175 Hospital of PLA (Southeast Hospital of Xiamen University), Zhangzhou Fujian 363000, China
    2. Department of Computer Science, Xiamen University, Xiamen Fujian 361005, China
  • Received:2010-11-01 Revised:2010-12-30 Online:2011-05-01 Published:2011-05-01
  • Contact: Yazhong Lin

摘要: 基于局部区域信息的局部二元拟合(LBF)模型在处理弱边界或灰度不均匀的图像分割方面有一定优势,但该方法非常依赖于初始轮廓,不当的初始轮廓不仅会导致分割时间较长,甚至分割失败。针对这一不足,提出一种快速稳定的LBF模型。首先通过添加带有变权系数面积项的LBF模型进行初始分类以获取较好的初始轮廓,然后采用传统的LBF模型对图像进行进一步的分割。实验证明,在保证良好分割效果的前提下,该方法对初始轮廓的选择更加灵活,分割速度明显快于传统的LBF模型。

关键词: 图像分割, 活动轮廓模型, 水平集算法, 局部二元拟合模型, 偏微分方程

Abstract: The Local Binary Fitting (LBF) model based on local region information has its certain advantages in image segmentation of weak boundary or uneven greay. But, the segmentation results are very sensitive to the initial contours, and improper initial contour can directly lead to segmentation failure. Thus, a fast and stable LBF approach was proposed. First, after adding the area item with variable weights to the traditional LBF model, better initial contour could be obtained than manual one. Second, the traditional LBF model would be used for further segmentation. The experimental results show that, under the precondition of preferable results, this method can not only get promising segmentation results with flexible initial contour selection, but also faster than the traditional LBF model.

Key words: image segmentation, active contour model, level set method, Local Binary Fitting (LBF) model, partial differential equation