03162nas a2200265 4500008004100000020002300041245008800064210006900152260003500221300001600256520231800272100001802590700001702608700001802625700001902643700001902662700001902681700001802700700002102718700002402739700002202763700001702785700002102802856007302823 2012 eng d a978-1-4673-2216-4 00aSpectral clustering to model deformations for fast multimodal prostate registration0 aSpectral clustering to model deformations for fast multimodal pr aTsukuba, JapanbIEEEcNov 2012 a2622 - 26253 a

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].

 

1 aMitra, Jhimli1 aKato, Zoltan1 aGhose, Soumya1 aSidibe, Desire1 aMartí, Robert1 aLladó, Xavier1 aArnau, Oliver1 aVilanova, Joan C1 aMeriaudeau, Fabrice1 aEklundh, Jan-Olof1 aOhta, Yuichi1 aTanimoto, Steven uhttp://hal.archives-ouvertes.fr/docs/00/71/09/43/PDF/ICPR_Jhimli.pdf02350nas a2200253 4500008004100000020001400041245008300055210006900138260001300207300001600220490000700236520154200243100001801785700001701803700001901820700001801839700001901857700001901876700001801895700002101913700001701934700002401951856012101975 2012 eng d a1361-841500aA spline-based non-linear diffeomorphism for multimodal prostate registration.0 asplinebased nonlinear diffeomorphism for multimodal prostate reg cAug 2012 a1259 - 12790 v163 a

This paper presents a novel method for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the shape representations is used to find the point correspondences between the 2D fixed and moving images. The registration method involves parametric estimation of the non-linear diffeomorphism between the multimodal images and has its basis in solving a set of non-linear equations of thin-plate splines. The solution is obtained as the least-squares solution of an over-determined system of non-linear equations constructed by integrating a set of non-linear functions over the fixed and moving images. However, this may not result in clinically acceptable transformations of the anatomical targets. Therefore, the regularized bending energy of the thin-plate splines along with the localization error of established correspondences should be included in the system of equations. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate mid-gland ultrasound and magnetic resonance images. The results obtained in terms of Dice similarity coefficient show an average of 0.980+/-0.004, average 95% Hausdorff distance of 1.63+/-0.48mm and mean target registration and target localization errors of 1.60+/-1.17mm and 0.15+/-0.12mm respectively.

1 aMitra, Jhimli1 aKato, Zoltan1 aMartí, Robert1 aArnau, Oliver1 aLladó, Xavier1 aSidibe, Desire1 aGhose, Soumya1 aVilanova, Joan C1 aComet, Josep1 aMeriaudeau, Fabrice uhttps://www.inf.szte.hu/en/publication/a-spline-based-non-linear-diffeomorphism-for-multimodal-prostate-registration02226nas a2200217 4500008004100000020002300041245007800064210006900142260003200211300001200243520148200255100001801737700001701755700001901772700001801791700001901809700001801828700002101846700002401867856011701891 2011 eng d a978-1-4577-2006-2 00aA non-linear diffeomorphic framework for prostate multimodal registration0 anonlinear diffeomorphic framework for prostate multimodal regist aNoosa, QLD bIEEEcDec 2011 a31 - 363 a

This paper presents a novel method for non-rigid registration of prostate multimodal images based on a nonlinear framework. The parametric estimation of the non-linear diffeomorphism between the 2D fixed and moving images has its basis in solving a set of non-linear equations of thin-plate splines. The regularized bending energy of the thin-plate splines along with the localization error of established correspondences is jointly minimized with the fixed and transformed image difference, where, the transformed image is represented by the set of non-linear equations defined over the moving image. The traditional thin-plate splines with established correspondences may provide good registration of the anatomical targets inside the prostate but may fail to provide improved contour registration. On the contrary, the proposed framework maintains the accuracy of registration in terms of overlap due to the non-linear thinplate spline functions while also producing smooth deformations of the anatomical structures inside the prostate as a result of established corrspondences. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate midgland ultrasound and magnetic resonance images in terms of Dice similarity coefficient with an average of 0.982 ± 0.004, average 95% Hausdorff distance of 1.54 ± 0.46 mm and mean target registration and target localization errors of 1.90±1.27 mm and 0.15 ± 0.12 mm respectively. © 2011 IEEE.

1 aMitra, Jhimli1 aKato, Zoltan1 aMartí, Robert1 aArnau, Oliver1 aLladó, Xavier1 aGhose, Soumya1 aVilanova, Joan C1 aMeriaudeau, Fabrice uhttps://www.inf.szte.hu/en/publication/a-non-linear-diffeomorphic-framework-for-prostate-multimodal-registration