CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Adaptive Regularization Level Set Evolution for Medical Image Segmentation and Bias Field Correction
Xin, Xiaomeng1,2; Wang, Lingfeng1; Pan, Chunhong1; Liu, Shigang2
2015
Conference NameICIP 2015
Source PublicationICIP 2015
Conference Date2015
Conference PlaceQuebec, Canada
Abstract

In this paper, we propose a level-set based segmentation method for medical images with intensity inhomogeneity. Maximum a Posteriori estimation is adopted to combine image segmentation and bias field correction into a unified framework. Within this framework, both contour prior and bias field prior can be fully used. In order to restrict bias field, we introduce an adaptive regularization. Based on this new adaptive regularization, the bias field is estimated more smooth and the input medical image with intensity inhomogeneity is recovered more clearly. Especially, the estimated bias field of our method introduces less structure information obtained from input image. Experimental results on both synthetic and real images show the advantages of our method in both segmentation and bias field correction accuracies as compared with the state-of-the-art approaches.

KeywordLevel Set Adaptive Regularization Image Segmentation Bias Field
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11026
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation1.中国科学院自动化研究所
2.陕西师范大学
Recommended Citation
GB/T 7714
Xin, Xiaomeng,Wang, Lingfeng,Pan, Chunhong,et al. Adaptive Regularization Level Set Evolution for Medical Image Segmentation and Bias Field Correction[C],2015.
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