Multimodal non-rigid registration methods based on local variability measures in computed tomography and magnetic resonance brain images

Isnardo Reducindo, Aldo R. Mejia-Rodriguez, Edgar R. Arce-Santana, Daniel U. Campos-Delgado, Flavio Vigueras-Gomez, Elisa Scalco, Anna M. Bianchi, Giovanni M. Cattaneo, Giovanna Rizzo

Research output: Contribution to journalArticle

Abstract

This paper presents a novel non-rigid multimodal registration method that relies on three basic steps: first, an initial approximation of the deformation field is obtained by a parametric registration technique based on particle filtering; second, an intensity mapping based on local variability measures (LVM) is applied over the two images in order to overcome the multimodal restriction between them; and third, an optical flow method is used in an iterative way to find the remaining displacements of the deformation field. Hence the new methodology offers a solution for multimodal NRR by a quadratic optimisation over a convex surface, which allows independent motion of each pixel, in contrast to methods that parameterise the deformation space. To evaluate the proposed method, a set of magnetic resonance/computed tomography clinical studies (pre- and post-radiotherapy treatment) of three patients with cerebral tumour deformations of the brain structures was employed. The resulting registration was evaluated both qualitatively and quantitatively by standard indices of correspondence over anatomical structures of interest in radiotherapy (brain, tumour and cerebral ventricles). These results showed that one of the proposed LVM (entropy) offers a superior performance in estimating the non-rigid deformation field.

Original languageEnglish
Pages (from-to)699-707
Number of pages9
JournalIET Image Processing
Volume8
Issue number12
DOIs
Publication statusPublished - Dec 1 2014

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Software
  • Computer Vision and Pattern Recognition

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    Reducindo, I., Mejia-Rodriguez, A. R., Arce-Santana, E. R., Campos-Delgado, D. U., Vigueras-Gomez, F., Scalco, E., Bianchi, A. M., Cattaneo, G. M., & Rizzo, G. (2014). Multimodal non-rigid registration methods based on local variability measures in computed tomography and magnetic resonance brain images. IET Image Processing, 8(12), 699-707. https://doi.org/10.1049/iet-ipr.2013.0705