Cover Image

Medical image registration by maximizing mutual information based on combination of intensity and gradient information

Link to publisher's homepage at http://ieeexplore.ieee.org/

Saved in:
Bibliographic Details
Main Authors: Tan, Chye Cheah, S., Anandan Shanmugam, Ang, Kenneth Li Mann
Other Authors: Tan.ChyeCheah@nottingham.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2012
Subjects:
Image registration;
Image-guided neurosurgery
Mutual information
Gradient informatiom
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/21429
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimap-21429
record_format dspace
spelling my.unimap-214292012-10-18T08:44:23Z Medical image registration by maximizing mutual information based on combination of intensity and gradient information Tan, Chye Cheah S., Anandan Shanmugam Ang, Kenneth Li Mann Tan.ChyeCheah@nottingham.edu.my Sanandan.Shanmugam@nottingham.edu.my Kenneth.Ang@nottingham.edu.my Image registration; Image-guided neurosurgery Mutual information Gradient informatiom Link to publisher's homepage at http://ieeexplore.ieee.org/ In recent years, mutual information has developed as a popular image registration measure especially in multimodality image registration. However, based on Shannon entropy, it focuses on the relationships between corresponding individual pixels and not those neighboring pixels. It ignores the spatial information contained in the images such as edges and corners that might be useful in the image registration. Thus we propose the adaptation of mutual information measure which incorporates the spatial information by combining intensity and gradient information. Mutual information value now is calculated from the gradient value and intensity value of the images. Salient pixels in the regions with high gradient value contribute more in the estimation of mutual information of image pairs being registered. Results of normalized mutual information, gradient-based mutual information and new proposed method are presented for rigid registration of medical images. We show that the new method yield better registration accuracy and it is more robust to noise than normalized mutual information. 2012-10-18T08:44:23Z 2012-10-18T08:44:23Z 2012-02-27 Working Paper p. 368-372 978-145771989-9 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6179040 http://hdl.handle.net/123456789/21429 en Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012) Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Image registration;
Image-guided neurosurgery
Mutual information
Gradient informatiom
spellingShingle Image registration;
Image-guided neurosurgery
Mutual information
Gradient informatiom
Tan, Chye Cheah
S., Anandan Shanmugam
Ang, Kenneth Li Mann
Medical image registration by maximizing mutual information based on combination of intensity and gradient information
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 Tan.ChyeCheah@nottingham.edu.my
author_facet Tan.ChyeCheah@nottingham.edu.my
Tan, Chye Cheah
S., Anandan Shanmugam
Ang, Kenneth Li Mann
format Working Paper
author Tan, Chye Cheah
S., Anandan Shanmugam
Ang, Kenneth Li Mann
author_sort Tan, Chye Cheah
title Medical image registration by maximizing mutual information based on combination of intensity and gradient information
title_short Medical image registration by maximizing mutual information based on combination of intensity and gradient information
title_full Medical image registration by maximizing mutual information based on combination of intensity and gradient information
title_fullStr Medical image registration by maximizing mutual information based on combination of intensity and gradient information
title_full_unstemmed Medical image registration by maximizing mutual information based on combination of intensity and gradient information
title_sort medical image registration by maximizing mutual information based on combination of intensity and gradient information
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/21429
_version_ 1643793373465673728
score 13.222552

Similar Items