Reliability-Driven, Spatially-Adaptive Regularization for Deformable Registration

被引:0
作者
Tang, Lisa [1 ]
Hamarneh, Ghassan [1 ]
Abugharbieh, Rafeef [2 ]
机构
[1] Simon Fraser Univ, Sch Comp Sci, Med Image Anal Lab, Burnaby, BC V5A 1S6, Canada
[2] Univ British Columbia, Dept Elect & Comp Engn, Biomed Signal & Image Comp Lab, Vancouver, BC V5Z 1M9, Canada
来源
BIOMEDICAL IMAGE REGISTRATION | 2010年 / 6204卷
关键词
IMAGE REGISTRATION; GRAPH-CUTS; SEGMENTATION; FIELDS; MODEL;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We propose a reliability measure that identifies informative image cues useful for registration, and present a novel, data-driven approach to spatially adapt regularization to the local image content via use of the proposed measure. We illustrate the generality of this adaptive regularization approach within a powerful discrete optimization framework and present various ways to construct a spatially varying regularization weight based on the proposed measure. We evaluate our approach within the registration process using synthetic experiments and demonstrate its utility in real applications. As our results demonstrate, our approach yielded higher registration accuracy than non-adaptive approaches and the proposed reliability measure performed robustly even in the presences of noise and intensity inhomogenity.
引用
收藏
页码:173 / +
页数:3
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