Unifying maximum likelihood approaches in medical image registration

✍ Scribed by Alexis Roche; Grégoire Malandain; Nicholas Ayache

• Publisher: John Wiley and Sons
• Year: 2000
• Tongue: English
• Weight: 199 KB
• Volume: 11
• Category: Article
• ISSN: 0899-9457
• DOI: 10.1002/(sici)1098-1098(2000)11:1<71::aid-ima8>3.0.co;2-5

✦ Synopsis

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.