一种基于时空MRF的工业CT图像序列分割方法

基金项目:

中国博士后基金资助项目(20080441190)

详细信息

ICT Image Sequence Segmentation Based on the Spatial Temporal Markov Random Field Models

摘要: 提出了一种基于时空马尔科夫随机场的工业CT图像序列分割算法。此算法根据工业CT图像序列信息连续性的特点, 建立时空Markov随机场, 并且构造相应的混合高斯统计模型能量函数, 利用条件迭代算法(ICM)实现最大后验概率(MAP)估计。仿真试验表明, 该方法能够较好地实现工业CT图像序列的分割。
- 工业CT图像序列 /
- 马尔科夫模型 /
- 图像分割
Abstract: A method of ICT image sequence segmentation based on Spatial Temporal Markov field model was presented. According to the spatial correlation of the ICT image sequence, the Markov random field(MRF) model based on spatial-temporal neighborhood system was proposed and the cost function of corresponding Gaussian mixture model was constructed. Then the maximum a posteriori(MAP) estimation was fulfilled by using the iterated conditional model(ICM) algorithms. The experimental results show that the proposed method is suitable, accurate and effective for ICT image sequence segmentation.
- ICT image sequence /
- Markov random field /
- Image sequence segmentation

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