CPOL: complex phase order likelihood as a similarity measure for MR-CT registration.
A novel similarity measure for registering magnetic resonance (MR) and computed tomography (CT) images has been designed and built. MR-CT registration methods often rely on the statistical intensity relationship between the images. The proposed similarity measure instead depends on the statistical relationship between the complex phase order between the images. By utilizing the complex phase order likelihood (CPOL) as a similarity measure, structural relationships instead of intensity relationships are explicitly used. This approach can be advantageous for MR-CT registration, where the intensities of the CT imagery have highly complex and nonlinear relationships with the intensities of corresponding MR imagery but simpler linear structural relationships. This new similarity measure has been tested on real MR-CT 3D volumes and has been evaluated based on fiducial registration error to determine alignment accuracy. Quantitative results show that CPOL is capable of achieving comparable alignment accuracy when compared to normalized mutual information, while being more robust to imaging artifacts such as noise.
Wong
Alexander
A
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada N2L 3G1. a28wong@engmail.uwaterloo.ca
Clausi
David A
DA
Fieguth
Paul
P
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*