A Brain Segmentation Algorithm Based on Markov Model Fused with Fuzzy Similarity Dynamic Weights
According to the fuzziness of medical image itself, this paper fused the dynamic connectedness in the Markov models. The method used the dynamic connectedness method to estimate fuzzy similarity between the pixels, and used this information to control the potential energy parameter in Markov model. The spatial correlation parameters can be changed with the image intensity and shape information. At last, we analyzed the result of experiments using the simulated images and actual clinical images of human brain MR images. The experiment result indicated that the method we proposed was better than the traditional Markov image segmentation method. It had some improvement of having higher segmentation accuracy and achieved a relatively satisfactory result.
Brain Segmentation Markov Model Fuzzy Similarity
Shi Wei-di Wei Ying
College of Information Science and Engineering, Northeastern University, Shenyang 110004 College of Information Science and Engineering, Northeastern University, Shenyang 110004 Key Laborat
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
1473-1476
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）