Cerebral Vessel Segmentation Based on Adaptive Clustering Centers
[1]Wang Zhe,Zhao Shifeng,Tian Yun,et al.Cerebral Vessel Segmentation Based on Adaptive Clustering Centers[J].Journal of Zhengzhou University (Engineering Science),2019,40(01):18-23.[doi:10.13705/j.issn.1671-6833.2019.01.004]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
40
Number of periods:
2019 01
Page number:
18-23
Column:
Public date:
2019-01-10
- Title:
- Cerebral Vessel Segmentation Based on Adaptive Clustering Centers
- Keywords:
- Cerebrovascular segmentation; finite mixture models; K-means
- CLC:
- -
- Abstract:
- Cerebral blood vessel segmentation was a key step in three-dimensional (3D) reconstruction, computer aided diagnosis and quantitative analysis. Due to complex geometric structure, small area percentage, low contrast vessel segmentation was still a challenging problem. Based on traditional statistical method with intensity, an improved K-means algorithm based on self-adapting clustering centers with gradient of remaining voxels preserved from previous step was used for further extraction of thin vessels. Firstly, one Rayleigh distribution and two Gaussian distributions were adapted to model background, tissues and vessel areas, respectively. And EM algorithm was used to estimate parameters for Gaussian distributions. Then posterior probability is used to extract the main body of blood vessels. Secondly, the remaining part containing the low contrast vessel areas and vessel edges was computed for gradient. An improved K-means method with self-adapting clustering centers was proposed to detect those areas. Experiment result demonstrated that our method was better than traditional statistical methods, especially at low contrast branches and vessel edges.
- References:
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Last Update:
2019-02-28