Adaptive Regularization Level Set Evolution for Medical Image Segmentation and Bias Field Correction
Xin, Xiaomeng1,2; Wang, Lingfeng1; Pan, Chunhong1; Liu, Shigang2
2015
会议名称ICIP 2015
会议录名称ICIP 2015
会议日期2015
会议地点Quebec, Canada
摘要

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.

关键词Level Set Adaptive Regularization Image Segmentation Bias Field
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11026
专题模式识别国家重点实验室_先进数据分析与学习
作者单位1.中国科学院自动化研究所
2.陕西师范大学
推荐引用方式
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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