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Unifying maximum likelihood approaches in medical image registration
Author(s) -
Roche Alexis,
Malandain Grégoire,
Ayache Nicholas
Publication year - 2000
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/(sici)1098-1098(2000)11:1<71::aid-ima8>3.0.co;2-5
Subject(s) - similarity (geometry) , image registration , mutual information , computer science , measure (data warehouse) , similarity measure , correlation ratio , image (mathematics) , artificial intelligence , maximum likelihood , modalities , pattern recognition (psychology) , correlation coefficient , data mining , mathematics , machine learning , statistics , social science , sociology
Although intensity‐based similarity measures are increasingly used for medical image registration, they often rely on implicit assumptions regarding the imaging physics. This paper clarifies the assumptions on which a number of popular similarity measures rely. After formalizing registration based on general image acquisition models, we show that the search for an optimal measure can be cast into a maximum likelihood estimation problem. We then derive similarity measures corresponding to different modeling assumptions and retrieve some well‐known measures (correlation coefficient, correlation ratio, mutual information). Finally, we present results of rigid registration between several image modalities to illustrate the importance of choosing an appropriate similarity measure.© 2000 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 11, 71–80, 2000