“…There are many references that deal with the role of registration and its applications in medical image processing, for an overview, see [29,27,23,12]. Moreover, specific applications to medical image registration involving Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) are considered in [23,12,31,30]. Registration is also one of the basic stages of image fusion, which is the process of combining multiple information from a set of images.…”
“…There are many references that deal with the role of registration and its applications in medical image processing, for an overview, see [29,27,23,12]. Moreover, specific applications to medical image registration involving Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) are considered in [23,12,31,30]. Registration is also one of the basic stages of image fusion, which is the process of combining multiple information from a set of images.…”
“…A number of authors have investigated the most popular radial basis function transformations in the image registration context: thin plate spline [7,31], multiquadric [30,41], inverse multiquadric [41], and Gaussian transformations [7]. A more specific application which involves registration and includes imaging techniques, such as computer tomography and magnetic resonance imaging, can be found in [37,38]. Since using globally supported RBFs, as for example the Gaussians, a single landmark pair change may influence the whole registration result, in the last two decades several methods have been presented to circumvent this disadvantage, such as weighted least squares and weighted mean methods (WLSM and WMM, respectively) [24], compactly supported radial basis functions (CSRBFs), especially Wendland's and Gneiting's functions [14,15,23], and elastic body splines (EBSs) [29].…”
In this paper we focus, from a mathematical point of view, on properties and performances of some local interpolation schemes for landmark-based image registration. Precisely, we consider modified Shepard's interpolants, Wendland's functions, and Lobachevsky splines. They are quite unlike each other, but all of them are compactly supported and enjoy interesting theoretical and computational properties. In particular, we point out some unusual forms of the considered functions. Finally, detailed numerical comparisons are given, considering also Gaussians and thin plate splines, which are really globally supported but widely used in applications.
“…The landmark-based registration problem can be formulated in the context of multivariate scattered data interpolation, and solved by different techniques, among which radial basis functions (RBFs) play a preminent role (see, e.g., [7,24]). The use of RBF transformations, in particular of the thin plate splines, for point-based image registration was first proposed by Bookstein [3], and it is still common (see [20,21] and the software package MIPAV [16]). …”
Abstract:In this paper we consider landmark-based image registration using radial basis function interpolation schemes. More precisely, we analyze some landmark-based image transformations using compactly supported radial basis functions such as Wendland's, Wu's, and Gneiting's functions. Comparisons of interpolation techniques are performed and numerical experiments show differences in accuracy and smoothness of them in some test cases. Finally, a real-life case with medical images is considered.
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