Paper Info
Title
A Robust Solution to Multi-modal Image Registration by Combining Mutual Information with Multi-scale Derivatives
Abstract
In this paper we present a novel method for performing image registration of different modalities. Mutual Information (MI) is an established method for performing such registration. However, it is recognised that standard MI is not without some problems, in particular it does not utilise spatial information within the images. Various modifications have been proposed to resolve this, however these only offer slight improvement to the accuracy of registration. We present Feature Neighbourhood Mutual Information (FNMI) that combines both image structure and spatial neighbourhood information which is efficiently incorporated into Mutual Information by approximating the joint distribution with a covariance matrix (c.f. Russakoff's Regional Mutual Information). Results show that our approach offers a very high level of accuracy that improves greatly on previous methods. In comparison to Regional MI, our method also improves runtime for more demanding registration problems where a higher neighbourhood radius is required. We demonstrate our method using retinal fundus photographs and scanning laser ophthalmoscopy images, two modalities that have received little attention in registration literature. Registration of these images would improve accuracy when performing demarcation of the optic nerve head for detecting such diseases as glaucoma.
Year
DOI
Venue
2009
10.1007/978-3-642-04268-3_76
MICCAI
Field
DocType
Volume
Modalities,Spatial analysis,Computer vision,Joint probability distribution,Pattern recognition,Computer science,Neighbourhood (mathematics),Mutual information,Artificial intelligence,Covariance matrix,Image registration,Modal
Conference
12
Issue
ISSN
Citations 
Pt 1
0302-9743
8
PageRank 
References 
Authors
0.56
7
4
Name
Order
Citations
PageRank
Philip A. Legg115611.55
Paul L. Rosin22559254.25
David Marshall31133106.45
James E. Morgan4393.67