19 November 2013 Non-rigid registration based on local uncertainty quantification and fluid models for multiparametric MR images
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Proceedings Volume 8922, IX International Seminar on Medical Information Processing and Analysis; 89220G (2013) https://doi.org/10.1117/12.2035492
Event: IX International Seminar on Medical Information Processing and Analysis, 2013, Mexico City, Mexico
Abstract
In this work, we present a novel fully automated multimodal elastic registration method for medical images. The new methodology combines a novel mapping based on the quantification of the intensity uncertainty of the neighborhood pixels, with a monomodal fluid like registration technique; thus the methodology can be summarized as a two-step technique. First, a mapping over both multimodal images is applied. This mapping provides information about the intensity uncertainty of the neighborhood pixels in both images, and it is based on the entropy computed over a local region. Second, a monomodal non-rigid registration is achieved between the transformed images. For this step, it is proposed to use a registration based on fluid-models: demons, diffeomorphic-demons, and a variation of the classical optical-flow. To evaluate the algorithm, a set composed by 12 magnetic resonance images of different modalities (T1, T2 and proton density) were taken from a brain model, and these images were modified by a set of controlled elastic deformations (using splines), in order to generate ground-truths to be registered with the proposed technique. The obtained results in this work showed an average error of less than 1.3 mm by combining the local uncertainty mapping with the diffeomorphic-demons technique, suggesting that the proposed methodology could be considered as a new alternative for fully automated multimodal non-rigid registrations on medical applications, which also ensures to obtain only possible physically deformations.
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I. Reducindo, A. R. Mejia-Rodriguez, E. R. Arce-Santana, D. U. Campos-Delgado, G. Rizzo, "Non-rigid registration based on local uncertainty quantification and fluid models for multiparametric MR images", Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 89220G (19 November 2013); doi: 10.1117/12.2035492; https://doi.org/10.1117/12.2035492
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