南方医科大学学报 ›› 2006, Vol. 26 ›› Issue (07): 959-962.

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图像分类数的自适应估计准则与最优分割算法

颜刚; 陈武凡;   

  1. 南方医科大学医学图像处理重点实验室; 南方医科大学医学图像处理重点实验室 广东广州510515; 广东广州510515;
  • 出版日期:2006-07-20 发布日期:2006-07-20
  • 基金资助:
    国家973重点基础研究发展规划项目(2003CB716101)~~

An adaptive criterion for cluster number estimation and the optimal algorithm for image segmentation

YAN Gang, CHEN Wu-fan Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China   

  1. 南方医科大学医学图像处理重点实验室; 南方医科大学医学图像处理重点实验室 广东广州510515; 广东广州510515;
  • Online:2006-07-20 Published:2006-07-20

摘要: 在图像分割算法中,将图像灰度分为多少类是首先需要决定的问题,它将直接影响分割的最终结果。因此,必须合理地估计图像分类数,它在理论分析和应用上都是很重要的一步。本文提出了一种基于马尔可夫场的图像分类数的自适应估计准则。当该准则达到最小时对应的类数就是图像正确分割所要求的分类数。准则中各参数由期望最大法和最大伪似然法来估计。试验表明,本文的算法能通过自适应的调整参数而正确地找到图像的分类数,并且在此分类数下能自动得到图像的最大后验分割。 

Abstract: In the algorithms for image segmentation, the number of clusters (NOC), which impacts on the segmentation results, should be first solved, and its correct estimation both theoretically and in application is of much importance. The authors propose an adaptive total energy criterion (ATEC) based on Markov random fields (MRF). The correct NOC of different images can be obtained by minimizing the ATEC and the parameters in the criterion are estimated by expectation maximization algorithm and maximum pseudo-likelihood method. The experiments show that the NOC can be automatically detected by adjusting the parameters, and the segmentation with the estimated NOC can be obtained by the maximum a posteriori at the same time. 

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