Robust Global Optimized Affine Registration Method for Microscopic Images of Biological Tissue
Lv YN(吕亚楠)1,3; Chen X(陈曦)3; Shu C(舒畅)1,3; Han H(韩华)2,3,4,5
2020-05
会议名称IEEE International Conference on Acoustics, Speech and Signal Processing
会议日期4-8 May 2020
会议地点Barcelona, Spain
摘要

Affine registration can fit the non-rigid deformation of slices effectively, and it is widely used in volume reconstruction of biological tissue. But most of the existing affine registration methods are registered in a given sequence, which results in the accumulation of errors. In this paper, a global optimized affine registration method is proposed, which can be used in volume reconstruction. To eliminate the cumulative error, the affine transformation of all images is estimated simultaneously based on an energy function. A constraint on affine transformation is added to restrict the shearing of images. Experiments show that our method provides a more reliable registration result compared with sequential affine registration. It can solve the problems caused by the accumulation of errors. The registration result fits the deformation of slices well and preserves the rigidity of images.

关键词Volume reconstruction registration affine transformation
学科门类工学
DOI10.1109/ICASSP40776.2020.9054568
收录类别EI
资助项目Strategic Priority Research Program of Chinese Academy of Science[XDB32030200] ; Special Program of Beijing Municipal Science & Technology Commission[Z181100000118002] ; National Science Foundation of China[61701497] ; National Science Foundation of China[61673381] ; National Science Foundation of China[61673381] ; National Science Foundation of China[61701497] ; Special Program of Beijing Municipal Science & Technology Commission[Z181100000118002] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32030200]
语种英语
七大方向——子方向分类图像视频处理与分析
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/44304
专题脑图谱与类脑智能实验室_微观重建与智能分析
通讯作者Han H(韩华)
作者单位1.University of Chinese Academy of Sciences, Beijing
2.Institute of Automation, Chinese Academy of Sciences
3.The Center for Excellence in Brain Science and Intelligence Technology
4.National Laboratory of Pattern Recognition
5.School of Future Technology, University of Chinese Academy of Sciences
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Lv YN,Chen X,Shu C,et al. Robust Global Optimized Affine Registration Method for Microscopic Images of Biological Tissue[C],2020.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
final_lyn.pdf(579KB)会议论文 开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Lv YN(吕亚楠)]的文章
[Chen X(陈曦)]的文章
[Shu C(舒畅)]的文章
百度学术
百度学术中相似的文章
[Lv YN(吕亚楠)]的文章
[Chen X(陈曦)]的文章
[Shu C(舒畅)]的文章
必应学术
必应学术中相似的文章
[Lv YN(吕亚楠)]的文章
[Chen X(陈曦)]的文章
[Shu C(舒畅)]的文章
相关权益政策
暂无数据
收藏/分享
文件名: final_lyn.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。