Brain image segmentation method with hybrid model based on FESS
文献类型：期刊论文
| 作者 | *Lian, Yuanfeng; Zhao, Yan; He, Huiguang ; Chen, Xuejiao
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| 刊名 | Chinese Journal of Scientific Instrument
 |
| 出版日期 | 2013 |
| 卷号 | 34期号:6页码:1226 |
| 关键词 | Conditional Random Field Data Fields Discriminative Models Generalization Capability Generative Model Least Squares Support Vector Machines Spatial Neighborhoods Subcortical Structures |
| 英文摘要 | This paper presents a new brain image segmentation method based on free energy score space (FESS), which combines the advantages of both generative model and discriminative model. Through conditional random field, the generative model fuses the grey scale information, shape information and spatial neighborhood relation of the volume pixels; and the appearance feature description of subcortical structure is realized. Based on the above, the generative model is used to map the training samples to FESS feature space. Furthermore; in the discriminative model, the LS-SVM classifier is used and the data field is applied in the training process of the mixed segmentation model, which reduces the performance fluctuation of the discriminative model induced by the imbalanced training data sets and effectively improves the generalization capability. Experimental results show that the proposed model possesses better segmentation quality and performance compared with several other state-of-the-art brain image segmentation approaches. |
| URL标识 | 查看原文 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20582]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_影像分析与机器视觉团队 |
| 推荐引用方式 GB/T 7714 | *Lian, Yuanfeng,Zhao, Yan,He, Huiguang,et al. Brain image segmentation method with hybrid model based on FESS[J]. Chinese Journal of Scientific Instrument,2013,34(6):1226. |
| APA | *Lian, Yuanfeng,Zhao, Yan,He, Huiguang,&Chen, Xuejiao.(2013).Brain image segmentation method with hybrid model based on FESS.Chinese Journal of Scientific Instrument,34(6),1226. |
| MLA | *Lian, Yuanfeng,et al."Brain image segmentation method with hybrid model based on FESS".Chinese Journal of Scientific Instrument 34.6(2013):1226. |
入库方式： OAI收割
来源：自动化研究所
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