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Cahill, N.D.* ; Schnabel, J.A.* ; Noble, J.A.* ; Hawkes, D.J.*

Overlap invariance of cumulative residual entropy measures for multimodal image alignment.

Proc. SPIE 7259, DOI: 10.1117/12.811585 (2009)
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Cumulative residual entropy (CRE)1,2 has recently been advocated as an alternative to differential entropy for describing the complexity of an image. CRE has been used to construct an alternate form of mutual information (MI),3,4 called symmetric cumulative mutual information (SCMI) 5 or cross-CRE (CCRE).6 This alternate form of MI has exhibited superior performance to traditional MI in a variety of ways.6 However, like traditional MI, SCMI suffers from sensitivity to the changing size of the overlap between images over the course of registration. Alternative similarity measures based on differential entropy, such as normalized mutual information (NMI),7 entropy correlation coefficient (ECC)8,9 and modified mutual information (M-MI),10 have been shown to exhibit superior performance to MI with respect to the overlap sensitivity problem. In this paper, we show how CRE can be used to compute versions of NMI, ECC, and M-MI that we call the normalized cumulative mutual information (NCMI), cumulative residual entropy correlation coefficient (CRECC), and modified symmetric cumulative mutual information (M-SCMI). We use publicly available CT, PET, and MR brain images* with known ground truth transformations to evaluate the performance of these CRE-based similarity measures for rigid multimodal registration. Results show that the proposed similarity measures provide a statistically significant improvement in target registration error (TRE) over SCMI. © 2009 Copyright SPIE - The International Society for Optical Engineering.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Registration
Language english
Publication Year 2009
HGF-reported in Year 2009
ISSN (print) / ISBN 0277-786X
e-ISSN 1996-756X
Journal Proceedings of SPIE
Quellenangaben Volume: 7259 Issue: , Pages: , Article Number: , Supplement: ,
Publisher SPIE
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
DOI 10.1117/12.811585
Scopus ID 71749106973
Erfassungsdatum 2022-09-05
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