Knowledge Commons of Institute of Automation,CAS
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 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
作者单位 | 1.中国科学院自动化研究所 2.陕西师范大学 |
第一作者单位 | 中国科学院自动化研究所 |
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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13_ICIP.pdf(1301KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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