A protocol for evaluation of similarity measures for non-rigid registration.
In this paper, we present a protocol for the evaluation of similarity measures for non-rigid registration. The evaluation is based on five intuitive properties that characterize the behavior of a similarity measure, i.e. the accuracy, capture range, distinctiveness of the optimum, number of local minima, and risk of non-convergence. These five properties are estimated locally from similarity measure values that correspond to a range of systematic local free-form deformations, obtained by displacing control points in random directions from the gold standard position. Global similarity measure properties are obtained by combining the local properties over image regions or over the entire image. The feasibility of the proposed evaluation protocol is demonstrated for three similarity measures: mutual information, normalized mutual information and correlation ratio. The evaluation is carried out on a number of MR and CT images: a pair of simulated MR T1 and MR T2 images of the head, three pairs of real MR T1 and T2 images of the head, six pairs of real MR T1 and CT images of the head, and pairs of MR and CT images of three vertebrae. The protocol may help researchers to select the most appropriate similarity measure for a non-rigid registration task.
Skerl
Darko
D
University of Ljubljana, Faculty of Electrical Engineering, Trzaska 25, 1000 Ljubljana, Slovenia.
Likar
Bostjan
B
Pernus
Franjo
F
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D012986:Software Validation; D013382:Subtraction Technique