Abstract
In this paper, we first propose a new subdivision of the image information axis used for the classification of nonrigid registration algorithms. Namely, we introduce the notion of iconic feature based (IFB) algorithms, which lie between geometrical and standard intensity based algorithms for they use both an intensity similarity measure and a geometrical distance. Then we present a new registration energy for IFB registration that generalizes some of the existing techniques. We compare our algorithm with other registration approaches, and show the advantages of this energy. Besides, we also present a fast technique for the computation of local statistics between images, which turns out to be useful on pairs of images having a complex, nonstationary relationship between their intensities, as well as an hybrid regularization scheme mixing elastic and fluid components. The potential of the algorithm is finally demonstrated on a clinical application, namely deep brain stimulation of a Parkinsonian patient. Registration of pre- and immediate postoperative MR images allow to quantify the range of the deformation due to pneumocephalus over the entire brain, thus yielding to measurement of the deformation around the preoperatively computed stereotactic targets.
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