A protocol for evaluation of similarity measures for rigid registration

Abstract

The accuracy and robustness of a registration method depend on a number of factors, such as imaging modality, image content and image degrading effects, the class of spatial transformation used for registration, similarity measure, optimization, and numerous implementation details. The complex interdependence of these factors makes the assessment of the influence of a particular factor on registration difficult, although it is often desirable to have some estimate of such influences prior to registration. The similarity measure used to create the cost function is one of the factors that most influences the quality of registration. Traditionally, limited information on the behavior of a similarity measure is obtained either by studying the quality of the final registration or by drawing plots of similarity measure values obtained by translating or rotating one image relative to the "gold standard." In this paper, we present a protocol for a more thorough, optimization-independent, and systematic statistical evaluation of similarity measures. This protocol estimates a similarity measure's capture range, the number, location and extent of local optima, and the accuracy and distinctiveness of the global optimum. To show that the proposed evaluation protocol is viable, we have conducted several experiments with nine similarity measures and real computed tomography and magnetic resonance (MR) images of a spine phantom, MR brain images, and MR and positron emission tomography brain images, for which "gold standard" registrations were available. We have also studied the impact of histogram bin size on the behavior of nine similarity measures. The proposed evaluation protocol is useful for selecting the best similarity measure and corresponding optimization method for a particular application, as well as for studying the influence of sampling, interpolation, histogram bin size, partial image overlap, and image degradation, such as noise, intensity inhomogeneity, and geometrical distortions on the behavior of a similarity measure.

A Multistage Registration Method Using Texture Features

“…For this task, we extended the protocol for the evaluation of the similarity measures for rigid registration. 22 The evaluation protocol is applied to a dataset of image pairs for which we have a gold standard. It has been tested for various multimodal rigid-registration tasks.…”

“…It has been tested for various multimodal rigid-registration tasks. 22,24,25 The output of the evaluation protocol is the parameters that measure the appropriateness of the criterion functions for a given registration task. In the case of texture-based registration, the texture feature itself forms a part of the criterion function.…”

“…1 The figure is reproduced from. 22 The normalization parameters used to generate the sampling lines for the image pairs used in our experiments are listed in Table 1. For a detailed explanation of the parameters, see.…”

“…For a detailed explanation of the parameters, see. 22 The original evaluation protocol was modified for a more efficient evaluation of a large number of criterion functions, which is the case when texture features are evaluated. The modifications are detailed in; 26 here, we provide just a brief summary.…”

“…To select the appropriate texture features for registration we propose an extended protocol for the evaluation of the similarity measures for rigid registration, originally devised by Škerl et al 22 The evaluation protocol assesses the quality of the similarity measure used in a specific registration problem prior to the registration. This is done by evaluating the behavior of the similarity measure for a set of simulated transformations.…”