Integrated Intensity and Point-Feature Nonrigid Registration - PubMed (original) (raw)

Integrated Intensity and Point-Feature Nonrigid Registration

Xenophon Papademetris et al. Med Image Comput Comput Assist Interv. 2001.

Abstract

In this work, we present a method for the integration of feature and intensity information for non rigid registration. Our method is based on a free-form deformation model, and uses a normalized mutual information intensity similarity metric to match intensities and the robust point matching framework to estimate feature (point) correspondences. The intensity and feature components of the registration are posed in a single energy functional with associated weights. We compare our method to both point-based and intensity-based registrations. In particular, we evaluate registration accuracy as measured by point landmark distances and image intensity similarity on a set of seventeen normal subjects. These results suggest that the integration of intensity and point-based registration is highly effective in yielding more accurate registrations.

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Figures

Fig. 1

Fig. 1

Sulci used in the evaluation of the registration methods. Note that these are major sulci, hence the matching error of the intensity based method is smaller that what is reported by Hellier et al. [10]

Fig. 2

Fig. 2

Example registration result. In this close-up of the central sulcus overlaid on a volume rendered stripped brain, the target surface is shown in white. The warped template is shown for three different registrations RPM − red, Integrated λ = 0.1) − blue and NMI − green.

Fig. 3

Fig. 3

Average intensity similarity for the point-based registration method (RPM), the new integrated algorithm (INT-λ) with seven different values of the weight of adherence to the point correspondences λ and the intensity only similarity algorithm (NMI) as computed from N = 17 normal controls.

Fig. 4

Fig. 4

Average sulcal matching error for the point-based registration method (RPM), the new integrated algorithm (INT-λ) as above and the intensity only similarity algorithm (NMI) as computed from N = 17 normal controls.

Fig. 5

Fig. 5

Average bending energy for the registrations computed by the point-based registration method (RPM), the new integrated algorithm (INT-λ) and the intensity only similarity algorithm (NMI) as computed from N = 17 normal controls.

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