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Proceedings Article

COLLINARUS: collection of image-derived non-linear attributes for registration using splines

[+] Author Affiliations
Jonathan Chappelow, Satish Viswanath, Anant Madabhushi

Rutgers Univ. (USA)

B. Nicolas Bloch, Neil Rofsky, Elizabeth Genega, Robert Lenkinski, William DeWolf

Harvard Univ. (USA)

Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72592N (March 27, 2009); doi:10.1117/12.812352
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From Conference Volume 7259

  • Medical Imaging 2009: Image Processing
  • Josien P. W. Pluim; Benoit M. Dawant
  • Lake Buena Vista, FL | February 07, 2009

abstract

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.

© (2009) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Jonathan Chappelow ; B. Nicolas Bloch ; Neil Rofsky ; Elizabeth Genega ; Robert Lenkinski, et al.
"COLLINARUS: collection of image-derived non-linear attributes for registration using splines", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72592N (March 27, 2009); doi:10.1117/12.812352; http://dx.doi.org/10.1117/12.812352


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