@string{icpr="Proceedings of International Conference on Pattern Recognition"}
@INPROCEEDINGS{Mitra-etal2012a,
  author = {Jhimli Mitra and Zoltan Kato and Soumya Ghose and Desire Sidibe and
	Robert Martí and Xavier Llado and Arnau Oliver and Joan C. Vilanova
	and Fabrice Meriaudeau},
  title = {Spectral clustering to model deformations for fast multimodal prostate
	registration},
  booktitle = icpr,
  year = {2012},
  pages = {2622--2625},
  address = {Tsukuba Science City, Japan},
  month = nov,
  organization = {IAPR},
  publisher = {IEEE},
  abstract = {This paper proposes a method to learn deformation parameters off-line
	for fast multimodal registration of ultrasound and magnetic resonance
	prostate images during ultrasound guided needle biopsy. The registration
	method involves spectral clustering of the deformation parameters
	obtained from a spline-based nonlinear diffeomorphism between training
	magnetic resonance and ultrasound prostate images. The deformation
	models built from the principal eigen-modes of the clusters are then
	applied on a test magnetic resonance image to register with the test
	ultrasound prostate image. The deformation model with the least registration
	error is finally chosen as the optimal model for deformable registration.
	The rationale behind modeling deformations is to achieve fast multimodal
	registration of prostate images while maintaining registration accuracies
	which is otherwise computationally expensive. The method is validated
	for 25 patients each with a pair of corresponding magnetic resonance
	and ultrasound images in a leave-one-out validation framework. The
	average registration accuracies i.e. Dice similarity coefficient
	of 0.927 ± 0.025, 95\% Hausdorff distance of 5.14 ± 3.67 mm and
	target registration error of 2.44 ± 1.17 mm are obtained by our
	method with a speed-up in computation time by 98\% when compared
	to Mitra et al. [7].}
}