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We present a new method for fully automatic non-rigid registration of multimodal imagery, including structural
and functional data, that utilizes multiple texutral feature images to drive an automated spline based non-linear
image registration procedure. Multimodal image registration is significantly more complicated than registration
of images from the same modality or protocol on account of difficulty in quantifying similarity between different
structural and functional information, and also due to possible physical deformations resulting from the data
acquisition process. The COFEMI technique for feature ensemble selection and combination has been previously
demonstrated to improve rigid registration performance over intensity-based MI for images of dissimilar modalities
with visible intensity artifacts. Hence, we present here the natural extension of feature ensembles for driving
automated non-rigid image registration in our new technique termed Collection of Image-derived Non-linear
Attributes for Registration Using Splines (COLLINARUS). Qualitative and quantitative evaluation of the COLLINARUS
scheme is performed on several sets of real multimodal prostate images and synthetic multiprotocol
brain images. Multimodal (histology and MRI) prostate image registration is performed for 6 clinical data sets
comprising a total of 21 groups of in vivo structural (T2-w) MRI, functional dynamic contrast enhanced (DCE)
MRI, and ex vivo WMH images with cancer present. Our method determines a non-linear transformation to
align WMH with the high resolution in vivo T2-w MRI, followed by mapping of the histopathologic cancer extent
onto the T2-w MRI. The cancer extent is then mapped from T2-w MRI onto DCE-MRI using the combined
non-rigid and affine transformations determined by the registration. Evaluation of prostate registration is performed
by comparison with the 3 time point (3TP) representation of functional DCE data, which provides an
independent estimate of cancer extent. The set of synthetic multiprotocol images, acquired from the BrainWeb
Simulated Brain Database, comprises 11 pairs of T1-w and proton density (PD) MRI of the brain. Following the
application of a known warping to misalign the images, non-rigid registration was then performed to recover the
original, correct alignment of each image pair. Quantitative evaluation of brain registration was performed by
direct comparison of (1) the recovered deformation field to the applied field and (2) the original undeformed and
recovered PD MRI. For each of the data sets, COLLINARUS is compared with the MI-driven counterpart of the
B-spline technique. In each of the quantitative experiments, registration accuracy was found to be significantly
(p < 0.05) for COLLINARUS compared with MI-driven B-spline registration. Over 11 slices, the mean absolute
error in the deformation field recovered by COLLINARUS was found to be 0.8830 mm.
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