[1]李碧草,张俊峰,杨冠羽,等.基于结构图像表示和微分同胚Demons算法的多模态医学图像配准[J].东南大学学报(自然科学版),2015,45(5):851-855.[doi:10.3969/j.issn.1001-0505.2015.05.007]
 Li Bicao,Zhang Junfeng,Yang Guanyu,et al.Multi-modal medical image registration based on structural image representation and diffeomorphic Demons algorithm[J].Journal of Southeast University (Natural Science Edition),2015,45(5):851-855.[doi:10.3969/j.issn.1001-0505.2015.05.007]
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基于结构图像表示和微分同胚Demons算法的多模态医学图像配准()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
45
期数:
2015年第5期
页码:
851-855
栏目:
计算机科学与工程
出版日期:
2015-09-20

文章信息/Info

Title:
Multi-modal medical image registration based on structural image representation and diffeomorphic Demons algorithm
作者:
李碧草张俊峰杨冠羽舒华忠
东南大学影像科学与技术实验室, 南京210096; 东南大学计算机网络和信息集成教育部重点实验室, 南京211189; 东南大学中法生物医学信息研究中心, 南京210096
Author(s):
Li Bicao Zhang Junfeng Yang Guanyu Shu Huazhong
Laboratory of Image Sciences and Technology, Southeast University, Nanjing 210096, China
Key Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing 211189, China
Centre de Recherche en Information Médicale Sino-français(CRIBs), Southeast University, Nanjing 210096, China
关键词:
Arimoto熵 Demons算法 结构图像表示 图像配准
Keywords:
Arimoto entropy Demons algorithm structural image representation image registration
分类号:
TP391
DOI:
10.3969/j.issn.1001-0505.2015.05.007
摘要:
为了提高多模态医学图像的配准精度,利用参考图像和浮动图像的结构信息,提出了一种基于结构图像表示和微分同胚Demons算法的多模态医学图像配准算法.该算法由结构图像表示和图像配准组成.在结构图像表示阶段,采用Arimoto熵图像来描述参考图像和浮动图像的结构信息,首先计算2幅图像中所有像素点周围指定大小邻域的熵值,然后把计算的熵值作为熵图像中对应点的灰度值以生成2幅熵图像.在图像配准阶段,使用微分同胚Demons配准算法对2幅熵图像进行配准.基于Brainweb数据库中磁共振数据的测试结果表明:与微分同胚Demons算法和基于香农熵的Demons算法相比,利用Arimoto熵表示图像的结构信息可以进一步提高配准精度.
Abstract:
In order to improve the registration accuracy of multi-modal medical images, a multi-modal medical image registration algorithm based on structural image representation and diffeomorphic Demons algorithm is proposed by using the structural information of the reference image and the float image. This algorithm contains structural image representation and image registration. In the process of structural image representation, the Arimoto entropy is used to describe the structural information of the reference image and the float image. First, the entropic values of the neighbourhoods with the designed size of all pixel points in the two images are calculated. Then, these entropic values are regarded as the intensity values of the corresponding points in the entropy image and two entropy images can be obtained. In the process of image registration, the diffeomorphic Demons registration approach is used to register these two entropy images. The experimental results of the MRI(magnetic resonance imaging)data in Brainweb database show that applying Arimoto entropy to represent the structural information can further improve the registration accuracy compared with the diffeomorphic Demons algorithm and the Demons method based on Shannon entropy.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2015-04-12.
作者简介: 李碧草(1985—),男,博士生;舒华忠(联系人),男,博士,教授,博士生导师,shu.list@seu.edu.cn.
基金项目: 国家重点基础研究发展计划(973计划)资助项目(2011CB707904)、国家自然科学基金资助项目(61073138,61201344,61271312,81101104).
引用本文: 李碧草,张俊峰,杨冠羽,等.基于结构图像表示和微分同胚Demons算法的多模态医学图像配准[J].东南大学学报:自然科学版,2015,45(5):851-855. [doi:10.3969/j.issn.1001-0505.2015.05.007]
更新日期/Last Update: 2015-09-20